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AMYOTROPHIC
LATERAL SCLEROSIS
\
GENETIC SUSCEPTIBILITY
FACTORS AND PLEIOTROPY
Frank P Diekstra
English title Amyotrophic lateral sclerosis: genetic susceptibility factors and pleiotropy
Nederlandse titel Amyotrofische laterale sclerose: genetische risicofactoren en pleiotropie
Cover design & layout Nadine Reef / www.nadinereef.nl
Printing Ridderprint BV
ISBN 978-94-6299-180-4
© 2015 F.P. Diekstra
All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means,
electronic or mechanical, including photocopy, recording, or any information storage or retrieval system,
without permission in writing from the author (where appropriate).
AMYOTROPHIC LATERAL SCLEROSIS:
GENETIC SUSCEPTIBILITY FACTORS AND PLEIOTROPY
AMYOTROFISCHE LATERALE SCLEROSE:
GENETISCHE RISICOFACTOREN EN PLEIOTROPIE
(met een samenvatting in het Nederlands)
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector
magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties
in het openbaar te verdedigen op dinsdag 27 oktober 2015 des ochtends te 10.30 uur
door
Frank Paul Diekstra
geboren op 4 augustus 1983
te Nijmegen
Promotoren: Prof. dr. L.H. van den Berg
Prof. dr. J.H. Veldink
Maintenant, que le malade n'est plus là, nous pouvons et nous devons nous
parler en toute franchise. Les remêdes les plus divers et dont l'emploi paraît
le plus rationnel, seront impuissants à retarder la marche progressive du mal.
C'est triste à dire, mais c'est comme cela: Pour le médecin, il ne s'agit pas
de savoir si cela est triste, il s'agit de savoir si cela est vrai. On a l'air de nous
reprocher quelquefois nos persévérantes études sur les grandes maladies
nerveuses jusqu'à présent le plus souvent incurables; à quoi cela sert-il?
Allons, notre devoir est autre: cherchons, malgré tout, cherchons toujours;
c'est encore le meilleur moyen de trouver et peut-être, grâce à nos efforts, le
verdict de demain ne sera-t-il pas le verdict d'aujourd'hui?
— Jean-Martin Charcot, Policlinique du Mardi 28 Février 1888
TABLE OF CONTENT
Introduction 9
..... Chapter 1
Interaction between PON1 and population density in 21
amyotrophic lateral sclerosis ..... Chapter 2
A case of ALS-FTD in a large FALS pedigree with a 31
K17I ANG mutation ..... Chapter 3
Mapping of gene expression reveals CYP27A1 as a 39
susceptibility gene for sporadic ALS ..... Chapter 4
No evidence for shared genetic basis of common 65
variants in multiple sclerosis and amyotrophic lateral sclerosis ..... Chapter 5
6
PART I: GENETIC SUSCEPTIBILITY FACTORS FOR ALS
PART II: GENETIC PLEIOTROPY
7
C9orf72 and UNC13A are shared risk loci for ALS 79
and FTD: A genome-wide meta-analysis
..... Chapter 6
UNC13A is a modifier of survival in amyotrophic 107
lateral sclerosis
..... Chapter 7
Genetic modifiers in C9orf72 repeat expansion 119
carriers: a genome-wide analysis
..... Chapter 8
Summary and general discussion 135
..... Chapter 9
Nederlandse samenvatting (summary in Dutch) 151
..... Chapter 10
Dankwoord 157
List of publications 163
Curriculum vitae 169
PART III: GENETIC DISEASE MODIFIERS
1
INTRODUCTION
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive mus-
cle weakness, spasticity, dysarthria and, ultimately, respiratory muscle insufficiency. These symptoms are
caused by the loss of motor neurons in both the brain and in the anterior horn of the spinal cord. The French
neurologist Jean-Martin Charcot first described amyotrophic lateral sclerosis in 1869.1 The disorder is also
referred to as motor neuron disease or Lou Gehrig’s disease (named after the famous American baseball
player who died of ALS). Symptoms most often start in one body region, for example with hand muscle
atrophy or slurry speech, and subsequently progress to other parts of the body.
Population-based studies have estimated an incidence rate of 2.16-2.8 per 100,000 person-years
in the European population and the prevalence is about 4-10 per 100,000.2-4 The lifetime risk of ALS is
about 1:350-400. There is a male preponderance in a male-to-female ratio of about 1.25-1.4.2, 3 The peak
incidence for ALS lies between 70-75 years in males, and somewhat lower in females: between 65-70
years of age.3, 5, 6 After the age of 80 years, incidence rates drop rapidly.
ALS is a rapidly progressive and fatal disease. The median survival time from onset is approximate-
ly three years.7, 8 To date, no cure is available, and denervation of the respiratory muscles or dysphagia lead-
ing to respiratory complications are the most common fatal events.9 Unfavorable prognostic factors include
older age at onset, bulbar onset, a low body mass index (BMI) and poor nutritional status, concomitant
cognitive decline, and respiratory function.10 Riluzole, a glutamate inhibitor, is the only drug with a proven
effect, prolonging survival with on average 2-3 months.11
Classically, ALS is divided in two forms: sporadic ALS or sALS (in which there is no apparent family
history of ALS) and familial ALS (fALS), defined as the presence of at least one affected first or second de-
gree relative. Approximately 5-10% of ALS cases are designated as familial ALS.12 The distinction between
sporadic and familial ALS has mainly been useful for genetic linkage studies, as familial ALS patients ap-
pear to have a higher frequency of monogenetic or oligogenetic causes, showing a Mendelian inheritance
pattern. Clinically, however, sporadic ALS and familial ALS are usually indistinguishable, although there are
some forms with a younger age at onset or with additional neurological symptoms such as parkinsonism
or cognitive symptoms.13 Recent advances in the study of genetic causes of ALS have identified the same
familial ALS mutations in a proportion of sporadic ALS patients. Furthermore, apparently sporadic cases
might actually carry mutations in ‘familial’ ALS genes, because of an unreliable family history, small pedi-
gree size, or incomplete gene penetrance.14 Therefore, the question rises whether this distinction between
sporadic and familial ALS still holds.
The etiology of sporadic ALS remains largely unknown. The general view is that sporadic ALS is a
disorder of complex etiology, in which both environmental factors and genetic factors play a role. Multiple
environmental factors have been studied, of which many remain controversial. Smoking appears to be the
most established risk factor for ALS.15, 16 Alcohol consumption may have no or even a protective effect.17
Furthermore, there is evidence for an increased risk of ALS in persons with intensive physical exercise,
10
1
among Gulf War veterans, persons exposed to pesticides, persons exposed to heave metals like lead or
certain occupational hazards like welding or electricity.6, 18-23 The latter risk factors, however, remain con-
troversial.23-25
In the past years interest for the genetic causes of ALS has grown tremendously. Studies of
twin pairs discordant for ALS have estimated a considerable heritability of around 60%.26, 27 Heritabili-
ty estimates using genome-wide data appear to be lower (11-21%), possibly due to statistical aspects
or because these calculations are based on common variants and do not account for the proportion of
disease-causing rare variants.28-30 The first causative gene for ALS, copper/zinc superoxide dismutase 1
(SOD1), was discovered in 1993 in several large pedigrees with familial ALS.31 First through linkage studies,
using genetic markers across the genome in affected and unaffected family members, and more recently
by using next-generation sequencing techniques, additional causative genes have been discovered in fa-
milial ALS. These genes include ALS2, SETX, SPG11, FUS, VAPB, ANG, TARDBP, FIG4, OPTN, VCP, UBQLN2, SIG-
MAR1, PFN1, C9orf72, MATR3, CHCHD10, and TBK1 (Figure 1). The familial ALS genes are involved in different
physiological processes, including endosome trafficking, RNA transcription or processing, neurofilament
formation, and axonal transport. Most of the mutations in aforementioned genes have a dominant mode
of inheritance, although mutations in ALS2 and SPG11 usually are recessive, and mutations in UBQLN2 are
X-linked.13 As noted previously, the clinical presentation of patients carrying mutations in these familial ALS
genes is mostly indistinguishable from other (for example sporadic) ALS patients. Exceptions are ALS2 and
SPG11, which are associated with juvenile ALS; FUS and ANG, which are associated with ALS and parkin-
sonism; and C9orf72, which also causes frontotemporal dementia (FTD).13 The frequency and distribution
of familial ALS gene mutations can vary strongly across different populations. For example, SOD1 mutations
are frequent in Scandinavian, Belgian and US ALS pedigrees, but are a rare cause of ALS in The Nether-
lands.32 Also, in Belgium about all familial ALS cases have been explained by known ALS genes, while in The
Netherlands we know the causative gene defect in approximately 65% of familial cases (Figure 2).
By contrast, the genetic causes of sporadic ALS remain far more elusive. Mutations in some of the
familial ALS genes have also been found in a small proportion of the sporadic ALS patients. However, these
can explain only 6-15% of the sporadic ALS cases (Figure 2). Earlier genetic studies in ALS have mainly
used a candidate gene approach, based on proposed pathogenic pathways or known interactions between
a gene and environmental factors.33-36 Examples of genes identified by such candidate gene approaches
are SMN, HFE, PON1, VCP, and VEGF. However, not all of these associations have been successfully replicat-
ed.13
With the development of new genotyping techniques for faster and cheaper assessing genetic
variation across the genome, the first genome-wide association studies in sporadic ALS emerged. With
chip-based genotyping arrays hundreds of thousands of single nucleotide polymorphisms (SNPs) could
be genotyped in a single experiment. This allowed for the testing of associations between a large number
of common variants (with a minor allele frequency (MAF) in the general population of 1-5% and higher)
INTRODUCTION
11
in so-called genome-wide association studies (GWAS). Following the common disease — common variant
hypothesis, these GWASs test for associations with genetic variants that have low penetrance, but are
relatively frequent in the population.37 These common variants, on the other hand, might tag haplotypes
Figure 1
Timeline of gene discoveries in familial and sporadic ALS
fALS = familial ALS; sALS = sporadic ALS
Figure 2
Proportions of mutations identified in familial and sporadic ALS
Proportions are based Caucasian study populations. fALS = familial ALS;
sALS = sporadic ALS
12
containing much rarer variants with a large effect on disease susceptibility. One great advantage of a GWAS
is the hypothesis-free assessment of the genome, thus greatly expanding the scope of possible pathogenic
candidates. Such a great number of association tests, however, is penalized by the need for multiple-tests
correction. Therefore, in order to identify associations with small effect sizes, and typically with odds ratios
of less than 2.0, GWASs require large sample sizes in order to achieve sufficient statistical power.
In 2007, the first genome-wide association studies in ALS were published, one of which identi-
fied ITPR2 as a susceptibility gene, although this association has not been replicated in later studies.38-40
Subsequent GWASs in sporadic ALS have implicated several other susceptibility loci, including DPP6, FGGY,
UNC13A, chromosome 9p21.2.41-43 Other GWASs have implicated disease-modifying loci in ALS, such as
KIFAP3 (associated with survival) and chromosome 1p34.1 (associated with age at onset).44, 45 However,
replication has proven difficult and only the association with the chromosome 9p21.2 locus has been
consistently replicated in independent cohorts.46-51 Later, in 2011, C9orf72 was discovered to be the caus-
ative gene within the chromosome 9p21.2 locus, bridging quite conclusively the gap between familial and
sporadic ALS.52, 53
The identification of a hexanucleotide repeat expansion in C9orf72 as the cause for chromosome
9p-linked ALS and FTD has been a major breakthrough. The chromosome 9p21.2 locus was first identified
in linkage studies in families with both ALS and FTD.54-56 Subsequently, GWASs have been able to fine-map
the locus to three genes, of which C9orf72 was ultimately discovered to harbor the causal variant. This
discovery forms an important genetic link between ALS with pure motor neuron symptoms and cognitive
symptoms in FTD. The function of C9orf72, to date, is unclear. Recent reports have suggested that the func-
tion of the protein may be related to DENN-like proteins, which are involved in vesicular trafficking.57, 58 The
repeat expansion in C9orf72 may either lead to a detrimental loss-of-function or to a toxic gain-of-function.
Also, through a mechanism called repeat-associated non-ATG (RAN) translation, different polypeptides are
produced forming neuronal inclusions that have been identified throughout the central nervous system in
C9orf72-related ALS and FTD patients.59
Frontotemporal dementia is a cortical-type dementia characterized by changes in cognition, be-
havior and language, in contrast to Alzheimer’s disease in which loss of memory function forms the hall-
mark. Brain imaging studies typically show frontal and temporal lobe atrophy. Population incidence rates
are comparable to those in ALS (approximately 3 in 100,000 person-years). Approximately 6% of sporadic
ALS and FTD patients carry the expanded C9orf72 repeat, while in familial ALS and FTD 37% and 25% of
cases have the repeat expansion, respectively.
In summary, although genetic factors appear to play a considerable role in the etiology of ALS, a
large part of the estimated heritability has not yet been accounted for. Sporadic ALS is considered a trait
of complex etiology, in which environmental factors may interplay with genetic risk factors and, together,
reach a ‘liability threshold’ that triggers motor neuron degeneration. Genetic studies in ALS have evolved
from candidate gene approaches to hypothesis-free genome-wide association studies. Ultimately, one of
INTRODUCTION
13
1
the most important genetic causes of ALS, repeat expansions in C9orf72, has demonstrated that genetic
links exist to other neurological disorders, for example FTD.
AIMS OF THIS THESIS
THIS THESIS AIMS:
- to identify genetic susceptibility factors for ALS in candidate genes or by using a genome-wide
mapping of gene expression;
- to identify genetic susceptibility factors for sporadic ALS that are shared with other neurological
disorders in order to elucidate pathogenic pathways underlying neurodegeneration;
- to identify genetic disease modifiers for ALS that may provide insight into pathogenic mechanisms or
provide possible therapeutic targets to change the onset or course of ALS.
In Part I, genetic susceptibility factors are investigated in a candidate gene approach by exploring a gene-
environment interaction for the paraoxonase 1 gene (PON1) in Chapter 2 and by investigating ANG muta-
tions in familial ALS cases (Chapter 3). Chapter 4 describes the integration of genome-wide expression
profiles and genome-wide SNP genotypes in order to identify additional susceptibility loci not identified in
previous GWASs.
Part II focuses on a possible overlap in genetic risk factors between ALS and other neurological disorders
(genetic pleiotropy). In Chapter 5, shared risk factors for ALS and multiple sclerosis (MS) are explored,
while we performed a genome-wide meta-analysis in Chapter 6 to identify shared risk loci for ALS and FTD.
Ultimately, Part III aims at the identification of genetic disease modifiers. As a follow-up on the results of
a previous GWAS, we investigated whether UNC13A might modify survival in ALS patients (Chapter 7). In
Chapter 8, we collected genome-wide data from ALS and FTD patients with C9orf72 repeat expansions and
investigated possible ‘genetic switches’ that may determine the disease phenotype.
14
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1
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and the risk of amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatr 2013;84:976-981.
20. Weisskopf MG, O’Reilly EJ, McCullough ML, Calle EE, Thun MJ, et al. Prospective study of military
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21. Haley RW. Excess incidence of ALS in young Gulf War veterans. Neurology 2003;61:750-756.
22. Park RM, Schulte PA, Bowman JD, Walker JT, Bondy SC, et al. Potential occupational risks for neurodege-
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of amyotrophic lateral sclerosis. JAMA Neurol 2014;71:1123-1134.
30. McLaughlin RL, Vajda A, Hardiman O. Heritability of Amyotrophic Lateral Sclerosis: Insights From Disparate
Numbers. JAMA Neurol 2015;72:857-858.
31. Rosen DR, Siddique T, Patterson D, Figlewicz DA, Sapp P, et al. Mutations in Cu/Zn superoxide dismutase
gene are associated with familial amyotrophic lateral sclerosis. Nature 1993;362:59-62.
32. van Es MA, Dahlberg C, Birve A, Veldink JH, van den Berg LH, et al. Large-scale SOD1 mutation
screening provides evidence for genetic heterogeneity in amyotrophic lateral sclerosis. J Neurol Neurosurg
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33. Kasperaviciute D, Weale ME, Shianna KV, Banks GT, Simpson CL, et al. Large-scale pathways-based
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34. Slowik A, Tomik B, Wolkow PP, Partyka D, Turaj W, et al. Paraoxonase gene polymorphisms and sporadic
ALS. Neurology 2006;67:766-770.
35. Cronin S, Greenway MJ, Prehn JHM, Hardiman O. Paraoxonase promoter and intronic variants modify risk
of sporadic amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatr 2007;78:984-986.
36. Saeed M, Siddique N, Hung WY, Usacheva E, Liu E, et al. Paraoxonase cluster polymorphisms are associated
with sporadic ALS. Neurology 2006;67:771-776.
37. Gibson G. Rare and common variants: twenty arguments. Nat Rev Genet 2011;13:135-145.
38. van Es MA, Van Vught PW, Blauw HM, Franke L, Saris CG, et al. ITPR2 as a susceptibility gene in sporadic
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39. Fernández-Santiago R, Sharma M, Berg D, Illig T, Anneser J, et al. No evidence of association of FLJ10986
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and ITPR2 with ALS in a large German cohort. Neurobiol Aging 2011;32:551.e551-554.
40. Schymick JC, Scholz SW, Fung H-C, Britton A, Arepalli S, et al. Genome-wide genotyping in amyotrophic
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41. van Es MA, van Vught PWJ, Blauw HM, Franke L, Saris CGJ, et al. Genetic variation in DPP6 is associated
with susceptibility to amyotrophic lateral sclerosis. Nat Genet 2008;40:29-31.
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INTRODUCTION
17
1
PART I
Genetic susceptibility
factors for ALS
2
INTERACTION BETWEEN PON1 AND
POPULATION DENSITY IN AMYOTROPHIC
LATERAL SCLEROSIS
NEUROREPORT. 2009;20(2):186-90
Frank P Diekstra, Ana Beleza-Meireles,
Christopher E Shaw, P Nigel Leigh, Ammar Al-Chalabi
ABSTRACT
Paraoxonase polymorphisms have been associated with amyotrophic lateral sclerosis (ALS).
Paraoxonases are detoxifying enzymes involved in the metabolism of organophosphates. We tested
the hypothesis that genetic variation within paraoxonase genes would interact with the
environmental exposure to paraoxonase substrates. We used population density in the location of
residence of ALS patients as a surrogate marker for environmental exposure. Paraoxonase
genotypes at previously associated single nucleotide polymorphisms rs662, rs854560, rs6954345
and rs11981433 were studied in 98 patients from the South East England ALS population-based
register. A case-only analysis was carried out and median population density was used to categorize
patients into rural or urban environments. We found a significant interaction with population density
for marker rs854560 (L55M) in ALS.
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is thought to be a disease of complex etiology in which both genetic and
environmental factors contribute to pathogenesis. Numerous epidemiological studies have investigated en-
vironmental risk factors for ALS. A greater incidence of ALS has been reported in individuals with a history
of intensive physical activity1, professional football2, cigarette smoking3, and exposure to heavy metals such
as lead4, although these remain controversial. In addition, pesticide and herbicide exposure has been impli-
cated in ALS, and for example an increased risk has been reported in rural residents or agricultural workers
who have been exposed to these substances.5,6 An excess incidence of ALS (or an ALS-like syndrome) has
been found in Gulf War veterans, and it has been hypothesized that exposure to pesticides used in their
tents or nerve gases may have contributed.7
Considering the oxidative-stress hypothesis in sporadic ALS and the previously described environ-
mental factors, the paraoxonases are interesting gene candidates.
The paraoxonase family consists of three adjacently located genes named PON1, PON2, and PON3 on
chromosome 7q21.3, coding for esterase enzyme.8 All three enzymes possess anti-oxidative properties.9
Four previous studies have investigated polymorphisms within the paraoxonase gene family in ALS.8,10-12
Each study found a different paraoxonase variant to be associated. So far, the PON1 promoter single nucle-
otide polymorphism (SNP) rs705379 (−108c > t)11, the PON1 coding SNPs rs662 (576a > g, Q192R)8
and rs854560 (163t > a, L55M)10, the PON2 coding SNP rs6954345 (926c > g, C311S)8 and a hap-
lotype covering PON2 and PON312 have been associated with ALS. Although ALS susceptibility because
of paraoxonase variants is very likely to be linked to environmental factors, only one study investigated a
gene-environment interaction.11 The study found a significant interaction between self-reported pesticide
exposure and paraoxonase polymorphisms for three PON1 promoter SNPs, including −108c>t in an Aus-
tralian population. However, only allelic tests reached significance and the findings did not extend to the
22
2
genotype or haplotype level. No gene-environment interaction was found for the L55M polymorphism.11
Variations in paraoxonase 1 can modify the rate of detoxification of pesticides.13,14 We hypothesized that
ALS patients living near agricultural fields would be more exposed to pesticides than patients in an urban
area and that paraoxonase polymorphisms might modify their susceptibility to pesticide toxicity. We there-
fore investigated a gene-environment interaction between paraoxonase gene polymorphisms and popu-
lation density in ALS patients for four previously associated SNPs in a population-based case-only study
design.
METHODS
PATIENTS
Patients for this study were selected from the South East England ALS registry.15 The catchment area for
this population-based study consists of 26 postcode regions including seven South East London boroughs
and nineteen local authorities in the counties of Brighton and Hove, East Sussex and Kent. The registry
identified 471 patients with ALS in the period from 1 January 2002 to 30 June 2006.15 This study was
approved by the Institutional Research Ethics Committee (222/02). Data on smoking and exercise were
not available for covariate analysis.
GENOTYPES
Genotypes for SNPs rs662, rs854560, rs6954345 and rs11981433 were determined using a 1536-
plex GoldenGate assay on an Illumina BeadArray station as described earlier.16
POPULATION DENSITY
Population density data for each of the 26 postcode regions in 2002 were obtained from the UK Office
for National Statistics.17
STATISTICAL ANALYSIS
As genotype data were available for a selected number of cases within the South East England ALS reg-
istry, patient characteristics of the current study population were compared to the full South East England
ALS registry population. Dichotomous variables were tested by using Pearson’s χ2 test; normally distributed
variables were tested with the independent samples t-test; and the Mann-Whitney test was used for contin-
uous variables with a non-normal distribution.
SNPs were tested for deviation from Hardy-Weinberg equilibrium by using an exact test18 in the
program PLINK v1.01 (Shaun Purcell, http://pngu.mgh.harvard.edu/purcell/plink).
To test for gene-environment interaction, population density data were dichotomized. The median
(1272 people/km2) was used as the cut-off point, as this value had the highest discriminating value when
tested in a receiver-operator curve for each SNP. Areas with high population densities were referred to
INTERACTION BETWEEN PON1 AND POPULATION DENSITY IN ALS
23
as urban; low population density areas were designated as rural. Interaction with SNPs was tested with
Fisher’s exact test at both the genotypic and allelic levels as well as with the Cochran-Armitage trend test.
Association analyses were carried out by using the software packages SPSS v15.0 (SPSS Inc) and PLINK.
For haplotypic association tests, pairwise linkage disequilibrium values for the selected SNPs were
determined and haplotypes estimated with the program Haploview v4.0
Table 1
Patient characteristics
South East England ALS registry n = 471
Study population n = 98 p
Age of onset, mean (y) 59.3 61.3 0.160 *
Gender, female 190 (40.3 %) 46 (46.9 %) 0.228 †Site of onset, bulbar 143 (30.4 %) 36 (36.7 %) 0.216 †Survival, median (y) 2.8 2.7 0.655 ‡Family history, none 397 (84.3 %) 88 (89.8 %) 0.250 †Population density, median (people/km
2)
1977 1272 0.900 ‡
*, independent samples t-test; †, 1df Pearson χ2 test; ‡, Mann-Whitney test
Table 2
Gene-environment interaction statistics of the genotyped polymorphisms
model rural (n=49) urban (n=49) p p corrected†PON1 Q192R (rs662)
Genotypic * QQ vs QR vs RR 30/17/2 19/22/8 0.043 0.17
Cochran-Armitage Q vs R (trend) 77/21 60/38 0.0099 0.04
Allelic Q vs R 77/21 60/38 0.012
Dominant QQ vs QR+RR 30/19 19/30 0.043
Recessive QQ+QR vs RR 47/2 41/8 0.091
PON1 L55M (rs854560)
Genotypic * LL vs LM vs MM 18/19/12 25/24/0 0.00048 0.0019
Cochran-Armitage L vs M (trend) 55/43 74/24 0.0047 0.019
Allelic L vs M 55/43 74/24 0.0065
Dominant LL vs LM+MM 18/31 25/24 0.22
Recessive LL+LM vs MM 37/12 49/0 0.00023
PON2 C311S (rs6954345)
Genotypic * CC vs CS vs SS 28/17/4 26/21/2 0.55 1
Cochran-Armitage C vs S (trend) 73/25 73/25 1 1
PON2 g10045a>g (rs11981433)
Genotypic * aa vs ag vs gg 16/23/10 17/20/12 0.87 1
Cochran-Armitage a vs g (trend) 55/43 54/44 0.89 1
*, genotypic Fisher's exact test; †, Bonferroni correction for four markers.
24
(Broad Institute, http://www.broad.mit.edu/mpg/haploview) according to the default confidence interval
method.19 Kaplan-Meier survival curves were estimated for the SNP with the strongest gene-environment
association. A log-rank test was used to compare the survival curves.
RESULTS
PATIENTS
Ninety-eight patients were studied. Patient characteristics of the current study population were not signifi-
cantly different from the non-genotyped South East England ALS registry population (Table 1).
GENOTYPES
The genotyping call rate was 100% for each of the four selected SNPs. All SNPs were in Hardy-Weinberg
equilibrium.
GENE-ENVIRONMENT INTERACTION
There was a significant association with population density for L55M (rs854560) at the genotypic level
(genotypic p=0.00048, Cochran-Armitage p=0.0047). This association withstood Bonferroni correction
for four markers (Table 2). We tested other models of association for this SNP and found the most signifi-
cant association was for the recessive model (p=0.00023, table 2). None of those homozygous for the T
allele were resident in an urban environment and the odds ratio cannot therefore be calculated.
There was a weaker signal from Q192R (rs662) (genotypic p=0.043, Cochran-Armitage p=0.0099,
table 2). Also, there was strong linkage disequilibrium between these two SNPs (D’=0.93). The D’ value for
the PON2 SNPs rs6954345 and rs11981433 was 0.87.
Two haploblocks were formed; one for the PON1 SNPs and another for the PON2 SNPs. The strong-
est association was for the Q192R L55M RL haplotype (p=0.0049), which was underrepresented in rural
residents (Table 3). Overall, haplotype analysis did not improve the single-marker association.
SURVIVAL
In the rural environment, when comparing the three L55M genotypes (LL vs LM vs MM), survival signifi-
cantly decreased with an increasing number of M alleles (log rank p=0.025, figure 1a).
INTERACTION BETWEEN PON1 AND POPULATION DENSITY IN ALS
Table 3
Estimated haplotype frequencies and association results
Frequency, � Overall frequency, %
p Haplotype rural urban
Q192R – L55M
Q – L 35.9 37.0 36.5 0.87
Q – M 42.7 24.2 33.4 0.0062
R – L 20.2 38.5 29.4 0.0049
The R-M haplotype did not occur in our study population.
25
2
By contrast, in the urban environment, there was no significant difference in survival between the L and M
alleles (log rank p=0.56, figure 1b).
DISCUSSION
This study investigated gene-environment interactions between paraoxonase gene polymorphisms and
population density in ALS patients, using population density as a proxy for rural versus urban regions and
therefore presumed differential exposures. We found a significant association for SNP L55M (rs854560).
There was a higher M allele frequency in ALS patients with rural residence, and strikingly, no individuals with
MM in the urban group. Also, in rural areas, the M allele was associated with shorter survival.
The L55M polymorphism is one of the two common coding variants in PON1; the other being
Q192R. Paraoxonase 1 hydrolase activity is modified by the Q192R polymorphism, with different activity
levels for different substrates. The Q variant favors hydrolysis of diazoxon, soman and sarin, while the R
isoform is more effective at hydrolyzing paraoxon.14 The L55M polymorphism also appears to be substrate
specific, for example, the 55LM and 55MM genotypes exert a higher protection from lipid peroxidation in
low and high density lipoproteins than the 55LL genotype.20 L55M has been reported to affect paraox-
onase 1 activity by modifying plasma expression levels. Lower plasma mRNA, paraoxonase 1 concentra-
tions and diazoxonase activities were found in individuals carrying the M variant compared to those with
the L variant.21,22 It is possible that the decreased paraoxonase 1 expression levels are not due to the L55M
amino-acid substitution, but rather due to linkage disequilibrium with the PON1 promoter polymorphism
−108c>t (rs705379).23 In the present study we were not able to test for linkage disequilibrium between
the -108c>t promoter SNP and L55M because -108c>t had not been typed.
In those with rural residence, LL genotype was associated with longer survival, whereas in those with
urban residence there was no difference in survival. The urban group contained no one with MM genotype.
Figure 1
Kaplan-Meier survival curves for L55M (rs854560).
26
Overall, the L55M genotype frequencies in our sample were in Hardy-Weinberg equilibrium. However, when
stratified by rural or urban residence, a relative increase of MM homozygosity was found in the rural group,
while none of the ALS patients in an urban environment carried the MM genotype. This finding suggests
that in the rural environment the MM genotype predisposes to the development of ALS.
A weakness of this study is the use of population density data as a proxy for increased pesticide exposure
within the rural environment. There are many substances that may differ between low and high population
density environments and it is therefore difficult to point out a single factor to explain our findings. However,
this study indicates a functional variant mapped by the M allele of rs854560 may affect the risk of ALS in
the context of an environmental exposure.
A further weakness of this study is that a value for population density was assigned to each patient
based on postcode data collected at time of diagnosis. This might not account for the total duration a pa-
tient lived in a certain area. It would, however, be reasonable to expect that this is a valid approximation for
the environmental exposure in the immediate years around the onset of symptoms that might account for
disease triggering.
As noted above, the current study population was derived from a population-based study of ALS
patients in the South East of England. Population-based studies can minimize selection bias and maximize
generalizability of findings.24 We realize that the individuals included in this study formed a selection from
the full South East England ALS registry. However, since major patient characteristics did not differ signif-
icantly from the original population, we consider the current selection a representative part of the whole
South East England ALS registry.
A case-only design was used to assess gene-environment interaction. This approach can achieve
greater precision in estimating interactions than the more conventional case-control method.24,25 It is there-
fore the most suitable approach when dealing with small study populations. However, case-only studies rely
on two assumptions. The genotype must be independent from the environmental exposure and the disease
should be rare so that the probability of undetected disease amongst “controls” is low.24 There is, of course,
no reason to believe that variations in paraoxonase genes (responsible for pesticide and low density lipo-
protein metabolism) would determine someone’s area of residence, but this possibility cannot be excluded.
Furthermore, the crude incidence of ALS within the South East England ALS registry was estimated to be
1.06 per 100,000 person years,15 therefore, the probability of hidden cases amongst the control popula-
tion is low. A drawback of the case-only design is that although the interaction of gene and environment can
be measured, the effects of gene or environment separately cannot be determined.
CONCLUSION
In this study we have found evidence for a gene-environment interaction of the L55M paraoxonase variant
in ALS with population density. This is consistent with our prior hypothesis of an interaction with different
pollutants in urban and rural environments but requires replication in studies using a larger population
and formal measures of pesticide exposure. The measurement of additional parameters such as serum
paraoxonase 1 activity would provide useful information as to the possible causes of the reported gene-
environment interaction.
INTERACTION BETWEEN PON1 AND POPULATION DENSITY IN ALS
27
2
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1. Scarmeas N, Shih T, Stern Y, Ottman R, Rowland LP. Premorbid weight, body mass, and varsity athletics in
ALS. Neurology 2002;59:773-775.
2. Chiò A, Benzi G, Dossena M, Mutani R, Mora G. Severely increased risk of amyotrophic lateral sclerosis
among Italian professional football players. Brain 2005;128:472-476.
3. Nelson LM, McGuire V, Longstreth WT, Matkin C. Population-based case-control study of amyotrophic
lateral sclerosis in western Washington State. Cigarette smoking and alcohol consumption. Am J Epidemiol
2000;151:156-163.
4. Kamel F, Umbach DM, Munsat TL, Shefner JM, Hu H, Sandler DP. Lead exposure and amyotrophic lateral
sclerosis. Epidemiology 2002;13:311-319.
5. Holloway SM, Emery AE. The epidemiology of motor neuron disease in Scotland. Muscle Nerve 1982;5:131-
133.
6. McGuire V, Longstreth WT, Nelson LM, Koepsell TD, Checkoway H, Morgan MS et al. Occupational
exposures and amyotrophic lateral sclerosis. A population-based case-control study. Am J Epidemiol
1997;145:1076-1088.
7. Haley RW. Excess incidence of ALS in young Gulf War veterans. Neurology 2003;61:750-756.
8. Slowik A, Tomik B, Wolkow PP, Partyka D, Turaj W, Malecki MT et al. Paraoxonase gene polymorphisms and
sporadic ALS. Neurology 2006;67:766-770.
9. Mackness B, Durrington P, Mackness M. The paraoxonase gene family and coronary heart disease. Curr
Opin Lipidol 2002;13:357-362.
10. Cronin S, Greenway MJ, Prehn JH, Hardiman O. Paraoxonase promoter and intronic variants modify risk of
sporadic amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatr 2007;78:984-986.
11. Morahan JM, Yu B, Trent RJ, Pamphlett R. A gene-environment study of the paraoxonase 1 gene and
pesticides in amyotrophic lateral sclerosis. Neurotoxicology 2007;28:532-540.
12. Saeed M, Siddique N, Hung WY, Usacheva E, Liu E, Sufit RL et al. Paraoxonase cluster polymorphisms are a
ssociated with sporadic ALS. Neurology 2006;67:771-776.
13. Adkins S, Gan KN, Mody M, La Du BN. Molecular basis for the polymorphic forms of human serum paraox-
onase/arylesterase: glutamine or arginine at position 191, for the respective A or B allozymes. Am J Hum
Genet 1993;52:598-608.
14. Davies HG, Richter RJ, Keifer M, Broomfield CA, Sowalla J, Furlong CE. The effect of the human serum
paraoxonase polymorphism is reversed with diazoxon, soman and sarin. Nat Genet 1996;14:334-336.
15. Abhinav K, Stanton B, Johnston C, Hardstaff J, Orrell RW, Howard R et al. Amyotrophic lateral sclerosis in
South-East England: a population-based study. The South-East England register for amyotrophic lateral
sclerosis (SEALS Registry). Neuroepidemiology 2007;29:44-48.
16. Kasperaviciute D, Weale ME, Shianna KV, Banks GT, Simpson CL, Hansen VK et al. Large-scale
pathways-based association study in amyotrophic lateral sclerosis. Brain 2007;130:2292-2301.
17. Office of National Statistics. Population density, 2002 Regional trends [Internet]. Islington (United Kingdom):
ONS. [cited 2008 February 19] Available from: http://www.statistics.gov.uk/StatBase/ssdataset.as
p?vlnk=7662&More=Y
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18. Wigginton JE, Cutler DJ, Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum
Genet 2005;76:887-893.
19. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B et al. The structure of haplotype blocks
in the human genome. Science 2002;296:2225-2229.
20. Cherki M, Berrougui H, Isabelle M, Cloutier M, Koumbadinga GA, Khalil A. Effect of PON1 polymorphism on
HDL antioxidant potential is blunted with aging. Experiment Gerontol 2007;42:815-824.
21. Brophy VH, Jarvik GP, Richter RJ, Rozek LS, Schellenberg GD, Furlong CE. Analysis of paraoxonase (PON1)
L55M status requires both genotype and phenotype. Pharmacogenetics 2000;10:453-460.
22. O’Leary KA, Edwards RJ, Town MM, Boobis AR. Genetic and other sources of variation in the activity of
serum paraoxonase/diazoxonase in humans: consequences for risk from exposure to diazinon. Pharmaco-
genet Genomics 2005;15:51-60.
23. Brophy VH, Jampsa RL, Clendenning JB, McKinstry LA, Jarvik GP, Furlong CE. Effects of 5’ regulatory-
region polymorphisms on paraoxonase-gene (PON1) expression. Am J Hum Genet 2001;68:1428-
1436.
24. Khoury MJ, Flanders WD. Nontraditional epidemiologic approaches in the analysis of gene-environment
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25. Piegorsch WW, Weinberg CR, Taylor JA. Non-hierarchical logistic models and case-only designs for assessing
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INTERACTION BETWEEN PON1 AND POPULATION DENSITY IN ALS
29
2
3
A CASE OF ALS-FTD IN A LARGE
FALS PEDIGREE WITH A K17I
ANG MUTATION
NEUROLOGY. 2009;72(3):287-8
Michael A van Es, Frank P Diekstra, Jan H Veldink,
Frank Baas, Pierre R Bourque, Helenius J Schelhaas,
Eric Strengman, Eric AM Hennekam, Dick Lindhout,
Roel A Ophoff, Leonard H van den Berg
INTRODUCTION
Approximately 90% of amyotrophic lateral sclerosis (ALS) cases are sporadic (SALS), but 10% are familial
(FALS). Mutations in SOD1, Alsin, Dynactin, SETX, DJ-1, VAPB, and TDP-431 have been reported (table e-1). After
the identification of sequence variation VEGF in patients with ALS, mutations in another angiogenic gene
(ANG) were identified in SALS and FALS.2,3 Studies in other populations have identified ANG mutations in
patients with ALS, but also in healthy controls. This suggests that not all mutations are pathogenic.3,4
METHODS
A total of 39 unrelated FALS patients, negative for SOD1 mutations, were screened for ANG mutations. This
study was approved by the local ethics committee and participants provided informed consent. DNA was
isolated from venous blood and ANG mutation analysis was performed as described in appendix e-1. A total
of 275 unrelated, healthy controls were taken from a prospective population-based study on ALS in The
Netherlands and were also screened.5 PMut (http://mmb2.pcb.ub.es:8080/PMut/) was used to predict
the impact of an amino acid substitution on the structure and function of the protein.
RESULTS
We identified one mutation in one patient (122 A>T) (figure, A), leading to an amino acid substitution of
lysine to isoleucine (K17I) (figure, B). PMut analysis predicted this mutation to be pathogenic. Sequence
alignments of ANG in different species demonstrated high conservation (figure, C).
Analysis of this pedigree revealed an autosomal dominant inheritance of the mutation (male to male
transmission) (figure, D). DNA was available from 44 out of 62 family members (five affected individuals).
All affected family members carried the K17I mutation.
Ten carriers were identified, but all were under 50 years of age (except one who was 75 years old
without symptoms or signs of ALS). The K17I mutation was not found in 275 control samples.
Cases III-3, III-4, and IV-1 all presented with progressive upper and lower motor neuron loss of limbs.
Case III-1 rapidly developed weakness in both arms with atrophy, fasciculations, and dyspnea, but no upper
motor neuron signs. The patient died after 6 months from onset. Case III-2 initially presented with parkin-
sonism (bradykinesia, diminished postural reflexes, cogwheel rigidity [right arm], shuffling, short-stepped
gait, and decreased spontaneous eye blink rate). There was no autonomic dysfunction and eye movements
were intact. Dopaminergic treatment had little effect. After 5 years, the patient developed progressive
weakness of the arms and legs with atrophy, fasciculations, and hyperreflexia. Interestingly, the patient also
demonstrated symptoms characteristic of frontotemporal dementia (FTD), such as loss of interest in social
contacts and family, short attention span, logopenia, verbal apraxia, perseveration, decreased personal hy-
giene, hyperorality, reckless behavior in traffic, sexual disinhibition, and apathy. Case I-2 and II-4 also appear
to have been affected. However, no medical records were available. Patient I-2 developed limb weakness
at age 70, leading to paralysis and death within 3 years. Patient II-4 developed speech impairment at age
32
3
60 and also died within 3 years. Patient II-2 (obligate carrier) died at age 50 from cardiovascular disease.
Detailed clinical characteristics are provided in table e-2.
DISCUSSION
Several ANG mutations in FALS have been reported, but clear segregation of mutations with the disease
has not been shown. Here, we report the K17I mutation segregating with disease in a large pedigree. The
fact that II-2 and a carrier (75 years of age) were without symptoms of ALS suggests incomplete pene-
trance of the mutation. This might explain why mutations in this codon have only been found in SALS. The
K17I mutation was previously reported in three cases and K17E in two cases.3,6
A CASE OF ALS-FTD IN A LARGE FALS PEDIGREE WITH A K17I ANG MUTATION
33
Figure
Mutational analysis and partial pedigree
This study provides a report of a patient with an ANG mutation and ALS, FTD, and parkinsonism.
Five percent of patients with ALS also have FTD and up to 50% demonstrated mild cognitive impairment.
Similarly, relatives of patients with ALS have an increased risk for developing PD. Therefore, genes involved
in ALS are also considered candidate genes for other neurodegenerative disorders. Indeed, an Italian study
reported a SALS patient with a 132C>T mutation and frontal lobe dysfunction.4
ANG is highly conserved between species, suggesting it has an important biologic function. Modeling
of the K17I mutation using PMut predicted this to be pathogenic. Two functional studies demonstrated that
the K17I mutation results in loss of function, possibly leading to insufficient ribosomes synthesis, decreased
protein translation, and ultimately decreased motor neuron viability.6,7
We report segregation of the K17I mutation with FALS and a patient with FALS, FTD, and parkinson-
ism, which possibly implicates ANG in these diseases.
34
REFERENCES
1. Valdmanis PN, Rouleau GA. Genetics of familial amyotrophic lateral sclerosis. Neurology
2008;70:144-152.
2. Greenway MJ, Alexander MD, Ennis S, et al. A novel candidate region for ALS on chromosome
14q11.2. Neurology 2004;63:1936-1938.
3. Greenway MJ, Andersen PM, Russ C, et al. ANG mutations segregate with familial and ‘sporadic’
amyotrophic lateral sclerosis. Nat Genet 2006;38:411-413.
4. Gellera C, Colombrita C, Ticozzi N, et al. Identification of new ANG gene mutations in a large cohort
of Italian patients with amyotrophic lateral sclerosis. Neurogenetics 2008;9:33-40.
5. van Es MA, Van Vught PW, Blauw HM, et al. ITPR2 as a susceptibility gene in sporadic amyotrophic
lateral sclerosis: a genome-wide association study. Lancet Neurol 2007; 6:869-877.
6. Wu D, Yu W, Kishikawa H, et al. Angiogenin loss-of-function mutations in amyotrophic lateral sclerosis.
Ann Neurol 2007;62:609-617.
7. Crabtree B, Thiyagarajan N, Prior SH, et al. Characterization of human angiogenin variants implicated
in amyotrophic lateral sclerosis. Biochemistry 2007; 46:11810-11818.
SUPPLEMENTARY INFORMATION
APPENDIX E-1
Mutations Analysis:
DNA was amplified with PCR using primers: ANG_xn2_For, 5’ TGTTCTTGGGTCTACCACAC; ANG_xn2_Rev,
5’ AATGGAAGGCAAGGACAGC. Forward and reverse strands were sequenced with the same primers. Se-
quence reaction products were purified using Sephadex (GE Healthcare) columns and run on an ABI 3730
automated sequencer. Traces were analyzed using ContigExpress from the Vector NTI Suite 10 (Invitro-
gen). All mutations were confirmed using stock DNA samples.
A CASE OF ALS-FTD IN A LARGE FALS PEDIGREE WITH A K17I ANG MUTATION
35
3
36
Table e-2
Clinical findings of the family with ALS and ANG K17I mutation
Gene Chromosome Inheritance Clinical Features
SOD11 21q22 AD Typical ALS
ALSin2 2q33 AR Juvenile onset, slowly progressive, predominantly corticospinal
VAPB3 20q13 AD Typical ALS
Dynactin4 2p13 AD Adult onset, slowly progressive, early vocal cord paralysis
DJ-15 22q13.2 AR Parkinson's disesase, ALS-FTDP
SETX6 9q34 AD Adult onset, slowly progressive
ANG7 14q11 AD Typical ALS
TDP-438 1p26 AD Typical ALS
Subject no.:
Age at Onset (yrs)
Site of Onset
Respiratory Involvement
Upper motor neuron signs
Lower motor neuron signs
Disease duration (mo)
ALS Plus
I:2* ± 70 Limb Yes na na ± 36 † -
II:4* ± 60 Bulbar Yes na na ± 36 † -
III:1 61 Limb Yes No Yes 6 † -
III:2 70 Limb Yes Yes Yes 42 † -
III:3 72 Limb Yes Yes Yes 24 FTD & PD
III:4 68 Limb Yes Yes Yes 34 † -
IV:1 55 Limb Yes Yes Yes 26 - Subject numbers correspond to the numbers in the pedigree in Fig 1c. Age of onset is calculated from initial onset of weakness.
Disease duration is calculated from initial manifestation of weakness until death or last date of contact. †: Individual is deceased.
*No medical records were available for I:2 and II:4. Information was collected from family members
Table e-1
Mutations in Familial ALS
SUPPLEMENTARY REFERENCES
1. Rosen DR, Siddique T, Patterson D, et al. Mutations in Cu/Zn superoxide dismutase gene are associated
with familial amyotrophic lateral sclerosis. Nature 1993;362(6415):59-62.
2. Hadano S, Hand CK, Osuga H, et al. A gene encoding a putative GTPase regulator is mutated in
familial amyotrophic lateral sclerosis 2. Nat Genet 2001;29(2):166-173.
3. Nishimura AL, Mitne-Neto M, Silva HC, et al. A mutation in the vesicle-trafficking protein VAPB causes
late-onset spinal muscular atrophy and amyotrophic lateral sclerosis. Am J Hum Genet
2004;75(5):822-831.
4. Munch C, Sedlmeier R, Meyer T, et al. Point mutations of the p150 subunit of dynactin (DCTN1) gene
in ALS. Neurology 2004;63(4):724-726.
5. Annesi G, Savettieri G, Pugliese P, et al. DJ-1 mutations and parkinsonism-dementia-amyotrophic
lateral sclerosis complex. Ann Neurol 2005;58(5):803-807.
6. Chen YZ, Bennett CL, Huynh HM, et al. DNA/RNA helicase gene mutations in a form of juvenile
amyotrophic lateral sclerosis (ALS4). Am J Hum Genet 2004;74(6):1128-1135.
7. Greenway MJ, Alexander MD, Ennis S, et al. A novel candidate region for ALS on chromosome
14q11.2. Neurology 2004;63(10):1936-1938.
8. Sreedharan J, Blair IP, Tripathi VB, et al. TDP-43 mutations in familial and sporadic amyotrophic
lateral sclerosis. Science 2008;319(5870):1668-1672
A CASE OF ALS-FTD IN A LARGE FALS PEDIGREE WITH A K17I ANG MUTATION
37
3
4
MAPPING OF GENE EXPRESSION
REVEALS CYP27A1 AS A SUSCEPTIBILITY
GENE FOR SPORADIC ALS
PLOS ONE. 2012;7(4):E35333
Frank P Diekstra,* Christiaan GJ Saris,* Wouter van Rheenen,
Lude Franke, Ritsert C Jansen, Michael A van Es, Paul WJ van Vught,
Hylke M Blauw, Ewout JN Groen, Steve Horvath, Karol Estrada,
Fernando Rivadeneira, Albert Hofman, Andre G Uitterlinden, Wim
Robberecht, Peter M Andersen, Judith Melki, Vincent Meininger,
Orla Hardiman, John E Landers, Robert H Brown Jr, Aleksey Shatunov,
Christopher E Shaw, P Nigel Leigh, Ammar Al-Chalabi, Roel A Ophoff,
Leonard H van den Berg*, Jan H Veldink*
* These authors contributed equally to this work
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle
weakness caused by loss of central and peripheral motor neurons. Symptoms typically have a localized limb
or bulbar onset and progress to other muscle groups of the body. Denervation of respiratory muscles and
dysphagia leading to respiratory complications are the most common causes of death. There is no cure for
this rapidly progressive disease.
Approximately 5% of patients have a family history of ALS.1 All other cases are considered to have
a sporadic form of the disease. ALS is considered to be a disease of complex etiology with both genetic
and environmental factors contributing to disease susceptibility.2 These genetic factors are the subject of
extensive research.3 Multiple genome-wide association studies (GWAS) and candidate gene studies have
been carried out, implicating several genes in the susceptibility to ALS,4-8 but attempts to replicate most of
these genes have proven difficult.9-13 Recently, our group has published a GWAS comprising over 4,800
40
ABSTRACT
Amyotrophic lateral sclerosis (ALS) is a progressive, neurodegenerative disease characterized by
loss of upper and lower motor neurons. ALS is considered to be a complex trait and genome-wide
association studies (GWAS) have implicated a few susceptibility loci. However, many more causal
loci remain to be discovered. Since it has been shown that genetic variants associated with complex
traits are more likely to be eQTLs than frequency-matched variants from GWAS platforms, we con-
ducted a two-stage genome-wide screening for eQTLs associated with ALS. In addition, we applied
an eQTL analysis to finemap association loci.
Expression profiles using peripheral blood of 323 sporadic ALS patients and 413 controls were
mapped to genome-wide genotyping data. Subsequently, data from a two-stage GWAS (3,568 pa-
tients and 10,163 controls) were used to prioritize eQTLs identified in the first stage (162 ALS,
207 controls). These prioritized eQTLs were carried forward to the second sample with both gene-
expression and genotyping data (161 ALS, 206 controls). Replicated eQTL SNPs were then tested
for association in the second-stage GWAS data to find SNPs associated with disease, that survived
correction for multiple testing.
We thus identified twelve cis eQTLs with nominally significant associations in the second-stage
GWAS data. Eight SNP-transcript pairs of highest significance (lowest p=1.27×10−51) withstood
multiple-testing correction in the second stage and modulated CYP27A1 gene expression. Addition-
ally, we show that C9orf72 appears to be the only gene in the 9p21.2 locus that is regulated in cis,
showing the potential of this approach in identifying causative genes in association loci in ALS.
This study has identified candidate genes for sporadic ALS, most notably CYP27A1. Mutations in
CYP27A1 are causal to cerebrotendinous xanthomatosis, which can present as a clinical mimic of ALS
with progressive upper motor neuron loss, making it a plausible susceptibility gene for ALS.
4
patients and nearly 15,000 controls and identifying UNC13A and 9p21.2 as susceptibility loci for sporadic
ALS.7 The 9p21.2 locus was recently replicated in an independent set of British patients and controls12 and
also shown to be strongly associated with ALS in Finland.14 This locus was previously found to be one of
the linked loci in families with ALS and frontotemporal dementia (FTD), and it was recently shown that a
hexanucleotide repeat expansion in C9orf72 was the basis of this linkage signal.15,16
Despite these large study samples, GWAS have been able to explain only little of the genetic variation
in ALS.4-7 An important drawback of GWAS is the burden of multiple-testing correction, requiring even larger
sample sizes in order to be able to detect small effects. It is common practice to apply a strict Bonferroni
correction to GWAS data. With so many tests, there is a high false-negative rate, as true associations are
hidden in the fog of random associations.
It has been established that gene expression levels can be mapped to genomic variation as a quan-
titative trait in order to detect so-called expression quantitative trait loci (eQTLs).17-19 Recently, it has been
shown that trait-associated SNPs are more likely to be eQTLs20, making the systematic analysis of eQTLs in
the context of a GWAS a promising tool for the discovery of novel disease-causing genes. In addition, eQTLs
can have local and distant effects, allowing for the identification of parts of biological networks related to
disease. These networks might be the link between several different genetic variants that appear to be as-
sociated with a disease in a GWAS.19 In practical terms, in order to identify eQTLs associated with disease,
both genome-wide genotype data as well as genome-wide gene expression levels have to be collected.
The focused genetic mapping of gene expression levels has frequently been applied to the fine-mapping
of risk loci resulting from GWAS, for example in the study of asthma21 and Crohn’s disease.22 Furthermore,
genome-wide eQTL analysis has proven fruitful in the study of diseases including obesity23, hypercholes-
terolemia24, celiac disease25, and late-onset Alzheimer disease.26 In the present study, we have performed a
genome-wide screen for eQTLs associated with susceptibility to ALS.
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
41
Figure 1
Study design
A schematic overview of our study design is shown in Figure 1. We performed an initial screen for eQTLs
in an eQTL discovery set. The eQTL SNPs resulting from this screen that had a nominally significant effect
in a discovery set from our previously published GWAS7 were selected for follow-up in the eQTL replication
set. Ultimately, replicated eQTLs were tested for significant effects in the GWAS replication data, correcting
for multiple testing.
METHODS
ETHICS STATEMENT
All participants gave written informed consent and approval was obtained from the Institutional Review
Board of the University Medical Center Utrecht. The present study was conducted according to the princi-
ples expressed in the Declaration of Helsinki.
GWAS DATA
Genome-wide genotype data were derived from a previously published GWAS of sporadic ALS in seven
countries (The Netherlands, Belgium, France, Ireland, United Kingdom, Sweden, United States).7 All patients
fulfilled the 1994 El Escorial criteria for probable or definite ALS.27 Cohorts for which genome-wide SNP
data were available were included. For both the discovery and replication set, genotype files with Illumina
Beadchip data (HumanHap 300K, HumanCNV 370K, HumanHap 550K or HumanHap 610K platforms)
were merged and the following quality control measures were taken. Only SNPs common to all cohorts
were used. Triallelic and C/G or A/T SNPs were excluded. Genotype files were merged, and after each
merge, a flipscan (scan for possible allele swaps) was performed in PLINK v1.07.28 SNPs with call rate
<95%, minor allele frequency <5%, deviation from Hardy-Weinberg equilibrium in controls (p<1×10−4),
or with differing heterozygosity or missing rates between cases and controls were excluded. Duplicate
samples, samples with a genotyping rate <95%, samples without gender information, or samples where
the genotypic gender did not match the phenotype file gender were excluded. LD-based SNP pruning was
used to determine a subset of SNPs in approximate linkage equilibrium. This subset of SNPs was used to
identify related samples, which were subsequently removed (pi-hat >0.2). The software package EIGEN-
STRAT was used to detect population substructure by principal components analysis.29 HapMap phase III
release 2 genotypes were added into this analysis in order to determine population outliers. After removal
of population outliers, new principal components were calculated. More detailed data on included subjects,
genotyping methods, and quality control are available in Text S1 and Table S5.
EXPRESSION DATA
Genome-wide gene expression data were obtained from 805 Dutch individuals (357 patients and 448
controls), who were also genotyped on either the HumanHap 300K, HumanCNV 370K or HumanHap
550K platforms in the previously described GWAS.7 Patients were recruited at our referral clinic for mo-
42
tor neuron disease at the University Medical Center Utrecht, The Netherlands. Included patients were
diagnosed with probable or definite sporadic ALS according to the 1994 El Escorial criteria.27 Messenger
RNA was collected and extracted from peripheral whole blood using PAXgene tubes and PAXgene extrac-
tion kit (Qiagen). Samples were hybridized to Illumina HumanHT-12v3 Expression BeadChips. Case and
control samples were randomly assigned to the chips and all chips were run in one batch. Before quality
control, expression levels were available for 48,803 probes. Raw expression data were quantile normalized
and log2 transformed30 in R (2009, The R Foundation for Statistical Computing). Using principal compo-
nents analysis of expression data, outlier arrays were detected. Non-pseudoautosomal Y chromosome
transcript expression levels were used for a gender check. Outlier arrays, samples with inconsistent gender
information, and samples designated as duplicates in our GWAS data, were removed from the raw data
(n=67). Also, non-autosomal probes were excluded (n=2,002). The thus obtained trimmed raw dataset
was again quantile normalized and log2 transformed. All probe sequences were aligned to the NCBI build
36 reference genome using UCSC’s Genome Browser function BLAT.31 Non-specific probes, defined as
no or multiple hits with a sequence homology >95%, were removed (n=7,234). RefSeq (updated on 27
September 2010) and UniGene (build #228, release date 29 October 2010) databases were used to
determine probes mapping to transcripts designated as retired and these probes were excluded as well
(n=2,449), leaving 37,118 gene-expression probes.
EQTL DATASETS
For the genetic mapping of gene expression, the subset of Dutch individuals with both genome-wide gen-
otype and expression data was tested for population substructure by principal components analysis of
genomic data using EIGENSTRAT.29 By inspecting the first two principal components, two outlier samples
(one case, one control) were identified and excluded. Subsequently, new principal components were cal-
culated. Non-autosomal SNPs were removed from the eQTL analysis. We randomly split our expression
dataset to form equally sized discovery and replication sets (Table S1).
STATISTICAL ANALYSIS
For the GWAS data, association with disease was tested in a logistic model using gender, dummy-coded na-
tionality and the first eight principal components in order to correct for ancestry as covariates. To determine
the number of principal components to be included in the logistic regression model, the first ten principal
components from the EIGENSTRAT29 analysis were tested for association with case/control status (thresh-
old p<0.05). For the GWAS discovery set, eight principal components were included in the logistic model,
while for the GWAS replication set two principal components were included. Analyses were performed in
PLINK v1.0728 and R (2009, The R Foundation for Statistical Computing).
For all analyses involving expression data, Surrogate Variable Analysis (SVA) was used to account
for heterogeneity in gene expression due to known and unknown environmental, technical or demographic
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
43
4
factors.32 SVA captures these factors into covariates for use in statistical models. Additionally, ‘riluzole use’
status was obtained, the only drug available to ALS patients with proven effect on survival.
For the eQTL analyses, SNP genotypes coded as an additive genetic model were tested for associa-
tion with gene expression by linear regression using disease status, age, gender, surrogate variables (18 in
the discovery set and 19 in the replication) and riluzole use as covariates. Cis eQTLs were defined as SNPs
modulating transcript expression levels within a region of 1Mb surrounding a probe’s genomic midpoint.26
False-positive cis effects may, however, occur due to SNPs that are located within a transcript probe or
that are in linkage disequilibrium (LD) with SNPs mapping within a transcript probe.33 We used the Broad
Institute SNAP tool v2.234 to determine pairwise LD between cis effect SNPs and SNPs mapping to a
transcript probe in either of the HapMap phase III release 2 or 1000 Genomes Pilot 1 CEU panels. 21,863
SNP-transcript combinations (pairwise LD threshold r2 >0.2) were excluded from analysis. Similarly, we
removed 24,170 SNP-transcript combinations with an InDel overlapping with a transcript probe, according
to the Database of Genomic Variants (version 10, November 2010).35 There were 3,541,781 possible
SNP-transcript combinations in cis left for analysis. The number of possible combinations in cis was used for
Benjamini-Hochberg false discovery rate (FDR) calculations. Significant cis effects were those SNP-tran-
script pairs that had significant p values at an FDR of 5% after 10,000 permutations. Permutations were
performed swapping case/controls labels so that each subject is assigned the genotype vector of another
random subject, while the expression matrix is unchanged. This prevents the underestimation of the null
distribution, thereby preventing the detection of false-positive eQTLs, as described previously.36 Analyses
were performed in PLINK28 and R (2009, The R Foundation for Statistical Computing).
EQTL SELECTION
In order to link the identified eQTLs to disease, we made a selection of significant cis effects in the eQTL
discovery set. Recent studies on the genetics of gene expression have shown that disease-associated loci
from GWAS are greatly enriched for eQTLs.20,25 Thus, we selected SNP-transcript pairs that had a nominal
SNP p value <0.05 in our GWAS discovery data (Figure 1).
Only these SNP-transcript pairs were used for follow-up in the replication data. Patient character-
istics for the expression replication dataset are presented in Table S1. SNP genotypes were correlated to
gene expression levels following a similar statistical analysis as used for our discovery set. Again, a 5%
FDR significance threshold was applied. Subsequently, association with ALS for SNPs from the replicat-
ed cis SNP-transcript pairs was tested in the GWAS replication data by logistic regression using gender,
dummy-coded nationality and the first two EIGENSTRAT principal components (these were significantly
correlated to case/control status) as covariates. Association test results were clumped based on LD (r2
>0.5) using PLINK, so that SNP p values could be obtained for independent eQTLs. eQTLs with a replica-
tion pGWAS <0.05 after Bonferroni correction for the number of independent (LD-based clumped) loci were
considered to be significant (Figure 1).
44
RESULTS
EQTL DISCOVERY
After quality control, eQTL analyses were performed on 162 ALS cases and 207 controls in the eQTL dis-
covery set with data on 261,682 autosomal SNPs and 37,118 expression probes. Patient characteristics
are summarized in Table S1. At a Benjamini and Hochberg false discovery rate (FDR) of 5%, we detected
16,901 significant SNP-transcript pairs in cis (Figure 1).
GWAS DISCOVERY
In the GWAS discovery set, 2,261 ALS cases and 8,328 patients remained after quality control measures
with genotypes for 268,952 SNPs. Details of included study populations are shown in Table S2. Association
analysis resulted in one SNP (rs12608932 in gene UNC13A) with genome-wide significance (p=1.7×10−8)
after Bonferroni correction for 268,952 SNPs. A Manhattan plot of genome-wide results is shown in Fig-
ure S1. A quantile-quantile plot of disease association p values is provided in Figure S2 (genomic control
λ=1.03). There were 14,167 autosomal SNPs with a nominal p value <0.05. These SNPs were used to
prioritize eQTLs found in the eQTL discovery set (Figure 1).
From the eQTL discovery results, we selected the 1,108 SNP-transcript pairs (755 eQTL SNPs)
in cis with discovery pGWAS <0.05 (Figure 1). To confirm the hypothesis that disease-associated SNPs are
more likely to be cis eQTLs20, we searched for enrichment for eQTLs in our list of SNPs with pGWAS <0.05. We
first determined the number of cis eQTLs in the set of SNPs with pGWAS <0.05 (n=755). Then, we randomly
selected a subset of 14,167 SNPs with pGWAS >0.05, matched for minor allele frequency to the set of SNPs
with pGWAS <0.05 (in 5% frequency bins). Subsequently, we determined the number of eQTLs present in
each of these sets of SNPs, using 100,000 permutations. By determining how often more than the initial
number of eQTLs were observed, we showed that there was evidence for enrichment for eQTLs in the set
of disease-associated SNPs (empirical p=0.003).
EQTL REPLICATION
The eQTL replication set comprised 161 ALS patients and 206 control samples (Table S1). 951 out of
1,108 selected SNP-transcript pairs in cis were significantly replicated (Figure 1). The eQTL SNPs of these
SNP-transcript pairs were selected for replication in the GWAS replication data.
GWAS REPLICATION
After quality control, there were 1,307 ALS cases and 1,835 controls in the GWAS replication set with
genotypes for 266,492 SNPs (Table S2). 577 cis eQTL SNPs were tested for association in the GWAS
replication data. Using linkage disequilibrium-based clumping of association results28, 322 independent
clumps could be formed. This number of clumps was used for Bonferroni correction, as these clumps des-
ignate independent loci. Table 1 shows clumps with a nominal pGWAS <0.05 in the replication set. Ultimately,
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
45
4
46
Table 1
eQTLs w
ith a nominally significant G
WA
S p value in the replication data
Locus Chr
Illumina H
T-12v3 prob
e identifier
Clum
p ind
ex
SN
P M
inor allele
GW
AS d
iscovery SN
P association
GW
AS rep
lication SN
P association
Joint GW
AS S
NP
association eQ
TL p value after
perm
utations eQ
TL direction
of effect
OR
p
OR
p
p b
onf. O
R
p
Discovery
Rep
lication
CYP
27
A1
2 ILM
N_
1704985
rs4674345 G
1.0
8 0.0
49 1.23
1.3210
4 0.0
42 1.12
1.8410
4 1.65
1046
1.1910
47 +
CE
NP
V
17 ILM
N_
1729142 rs10
491104
G
1.11 3.79
103
1.17 3.64
103
n.s. 1.14
2.3510
5 1.15
105
9.5010
4 +
SLC
11A1
2 ILM
N_
1741165, ILM
N_
1735737 rs22790
14 A
1.12 2.26
103
1.15 0.0
11 n.s.
1.13 4.98
105
5.4810
27 7.49
1040
+
TTC39C
18
ILMN
_1746720
rs1154227
G
1.08
0.0
37 1.15
0.0
11 n.s.
1.12 3.0
010
4 6.23
106
5.2510
4 +
SP
I1, M
YBP
C3
11 ILM
N_
1696463, ILM
N_
1781184 rs7126210
A
1.08
0.0
44 1.15
0.0
21 n.s.
1.11 2.28
103
5.4410
9 1.57
105
+
RA
BE
P1
17 ILM
N_
1719622 rs3865351
A
0.91
0.0
24 0.88
0.0
21 n.s.
0.90
2.0
610
3 2.70
107
1.1910
6 +
ZN
F586 19
ILMN
_237220
0
rs4801516
A
0.92
0.0
20
0.89
0.0
27 n.s.
0.92
8.1510
3 6.73
105
3.6010
3 +
KIA
A0
513 16
ILMN
_1693233
rs8056742
G
1.17 7.51
103
1.19 0.0
29 n.s.
1.19 1.42
104
4.2910
8 6.44
1015
+
C17
orf75,
CD
K5R
1 17
ILMN
_1797155,
ILMN
_1730
928 rs479570
0
A
1.12 2.15
103
1.12 0.0
34 n.s.
1.11 4.14
104
3.3310
26 9.85
1040
+
SLC
39A1
1 ILM
N_
2116714 rs11264743
A
0.92
0.0
32 0.88
0.0
35 n.s.
0.91
2.7210
3 9.47
107
2.09
104
+
Hs.447
737
5
ILMN
_1896967
rs13354021
G
0.92
0.0
40
0.89
0.0
40
n.s. 0.91
3.4610
3 3.0
510
7 1.51
104
+
CLE
C12
A
12 ILM
N_
1663142, ILM
N_
2292178 rs10
505745
A
1.16 1.91
103
1.14 0.0
49 n.s.
1.14 5.75
104
5.09
105
6.8110
5
Independent eQTLs are based on LD
-based SN
P clumping. For each locus, the clum
p index SN
P (with the low
est p value) is shown. For the G
WAS
replication results, Bonferroni corrected p
values are
given for the testing of 322 clumps. S
NP association results in the joint G
WAS
data were based on a total of 3,568 ALS
patients and 10,163 controls. For the eQ
TL direction of effect, '+' means the S
NP
minor allele w
as associated with increased expression levels, '-' m
eans decreased gene expression. Chr, chrom
osome; LD
, linkage disequilibrium; G
WAS
, genome-w
ide association study; OR, odds ratio;
p bonf., Bonferroni corrected p
value; n.s., not significant; eQTL, expression quantitative trait locus.
-
Table 2
Results for fine-m
apping of loci previously associated with A
LS.
Locus
Illumina H
T-12v3
prob
e identifier
SN
P M
inor
allele
LD w
ith
rs3849942
GW
AS d
iscovery SN
P
association
GW
AS rep
lication SN
P
association
Joint GW
AS S
NP
association
eQTL p
value after
perm
utations
Expression
variance explained
(R
2)
r
2, D'
OR
p
OR
p
OR
p
Discovery
Rep
lication Com
bined
data
C9orf7
2
isoform a,
Chr. 9
ILMN
_1741881
rs1012290
2 A
0.0
8, 1.00
0.97
0.49
0.98
0.81
0.97
0.42
1.39×10−
7 2.0
8×10−
4 0.80
rs1565948 G
0.32, 0
.99 1.14
3.17×10−
4 1.0
1 0.93
1.11 6.0
0×10−
4 5.0
0×10−
5 3.0
0×10−
4 0.80
The minor allele of rs10
122902 w
as associated with increased C
9orf72 expression levels, w
hile the minor allele of rs1565948 w
as associated with decreased expression. LD
estimates w
ith SN
P rs3849942 and S
NP association results in the joint G
WAS data w
ere based on a total of 3,568 ALS
patients and 10,163 controls. The expression explained variance (R
2) was estim
ated from expression
data from both discovery and replication eQ
TL datasets combined. C
9orf72, chrom
osome 9 open reading fram
e 72; Chr., chrom
osome; LD
, linkage disequilibrium; G
WAS, genom
e-wide association study;
OR, odds ratio; eQ
TL, expression quantitative trait locus.
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
47
Figure 2
Regional linkage disequilibrium (LD) near the CYP27A1 locus on chromosome 2
we identified 1 cis eQTL, comprising 8 SNP-transcript pairs, which was significantly replicated, and the
transcript of which mapped to gene CYP27A1 (Figure 2). The results for this locus are listed in Table S3, also
indicating that the explained variance of gene expression that is achieved by the linear models ranged from
48-65%. The relationships between the SNPs and gene-expression levels are shown in Figure S3.
FINE-MAPPING OF LOCI UNC13A AND CHROMOSOME 9p21.2
In addition to our genome-wide screen for eQTLs associated with sporadic ALS, we specifically examined
possible relevant cis effects in two previously associated loci (gene UNC13A and chromosome 9p21.2).7,12
The detection of cis effects might fine-map these loci. For the UNC13A locus (SNP rs12608932), multi-
ple-testing correction was applied for 41 possible SNP-transcript pairs in cis (as determined by a genomic
Top: the position of GWAS SNPs and RefSeq genes located within the regional LD block are drawn. On the X-axis, ge-
nomic position in kb, aligned to NCBI genome build 36 coordinates. On the left Y-axis, -log10(p values) for the strongest
cis eQTL association for a gene in the replication data, the vertical position of genes (drawn as arrows) are aligned
to this axis and thus represent statistical significance. For one gene (RQCD1), no SNP-transcript pair and, therefore,
no eQTL p value was available in our data. This gene is shown as a dashed arrow. On the right Y-axis, -log10(p values)
from the replication GWAS analysis for SNPs within the region (black line), SNPs modulating CYP27A1 expression are
shown as black dots, other SNPs are grey. Bottom: pairwise linkage disequilibrium for HapMap phase III release 2 SNPs
(CEU+TSI populations). The LD plot was created in Haploview v4.250, using the standard D’/LOD color scheme.
4
distance of <500kb between the SNP and a probe’s midpoint). One SNP-transcript pair had a nominal p
value <0.05, the transcript of which mapped to gene PGLS (peQTL=0.01). However, when using a 5% Benja-
mini-Hochberg FDR for the locus as multiple-testing correction, no SNP-transcript pairs reached statistical
significance. For the chromosome 9p21.2 locus, we looked for cis eQTLs within a 130kb LD block compris-
ing previously associated SNPs (rs2814707 and rs3849942). Multiple-testing correction for the testing
of 328 SNP-transcript pairs was applied using a 5% FDR. Two SNP-transcript pairs reached the threshold
for statistical significance and were associated with C9orf72 isoform a expression levels (Table 2 and Figure
S4). SNP rs1565948 modulated C9orf72 gene expression in both eQTL discovery and replication sets and
was associated with susceptibility to ALS in the joint GWAS data; however, no association with ALS was
found in the GWAS replication set alone (Table 2).
DISCUSSION
The present study reports the results of a large and comprehensive genome-wide screening of the genetics
of gene expression in an attempt to find novel genetic variants that associate with sporadic ALS. We used
a two-stage approach to minimize the chance of false-positive findings, both for eQTL discovery purposes
and for the detection of novel SNP-ALS associations. eQTLs were used for prioritizing GWAS results, as it
has been established that SNPs that are truly associated with disease are more likely to be eQTLs.20,25,37
In the present study, we show that the number of eQTLs is greater than expected by chance (p=0.003)
among the SNPs with a nominal association with ALS, compared to frequency-matched SNPs, also indi-
cating that eQTLs may be useful in the prioritization of GWAS results in ALS. We identified eight SNPs in
one cis eQTL, modulating CYP27A1 gene expression levels, which replicated in the second eQTL dataset and
second GWAS set. The eQTL SNPs within this locus are part of a large linkage disequilibrium (LD) block
comprising a total of ten genes (Figure 2). The figure clearly shows that the strongest eQTL associations
exist for SNPs modulating CYP27A1 expression, explaining up to 65% of variation in gene expression of this
gene. Additionally, we show that C9orf72 appears to be the only gene in the 9p21.2 locus that is regulated
in cis, showing the potential of this approach in identifying causative genes in association loci in ALS.
As shown in Table S3, the SNPs modulating transcript levels had small effect sizes in our joint GWAS
association results, the highest odds ratio (OR) being 1.13. We used PS v3.038 for statistical power cal-
culations to determine the required sample size for a third genotypic replication of such SNPs. In order to
replicate an association for one SNP with minor allele frequency 0.35 at α=0.05, one would require a min-
imum of 2,250 cases and 2,250 controls to achieve 80% power for detecting an effect with OR 1.13. As
shown in Table 1, several eQTL SNPs did not reach Bonferroni corrected significance in the replication data
alone, but do show stronger effects in the joint GWAS data, indicating that statistical power of the GWAS
replication set might be a limiting factor. By testing these SNPs in a third independent replication cohort,
additional true associations may be detected. The required sample size for such an effort would, however,
increase dramatically when adding more tests. Further international collaboration, therefore, is needed in
48
order to achieve sufficient statistical power for the replication of SNPs with small effect sizes.
We searched MEDLINE, Gene Ontology and OMIM databases to identify links to known pathways
in ALS pathogenesis for CYP27A1. The CYP27A1 gene is involved in cholesterol metabolism and has been
associated with cerebrotendinous xanthomatosis (CTX), which can present with progressive upper motor
neuron signs and is a known clinical mimic for primary lateral sclerosis.39,40 Two heterozygous mutations in
CYP27A1 have been reported in a patient with atypical CTX and frontotemporal dementia characteristics.41
Furthermore, previously, serum cholesterol levels have been implicated in modifying survival and in the
onset of respiratory impairment in ALS patients.42-44 The combination of our results and these prior data
make CYP27A1 a plausible candidate gene for ALS.
The strengths of our study are the meticulous pruning of expression probes as present on the ex-
pression array, with regard to non-specific mapping in the human transcriptome, or harboring SNPs that
might interfere with hybridization of probes to the array, resulting in false-positive eQTLs.33 In addition,
permutation schemes were applied, preserving the LD structure within subjects, also minimizing the detec-
tion of false-positive eQTLs. Finally, a two-stage approach, both for eQTLs discovery purposes and for the
detection of novel SNP-ALS associations, ensures robustness of the results.
A drawback of the present study lies in the use of whole blood instead of neuronal tissue for the
measurement of mRNA expression levels. As neuronal tissue is inaccessible in living ALS patients, one could
consider the use of human neuronal tissue from autopsy. However, in post-mortem material of ALS patients,
most affected motor neurons will have degenerated and one would be investigating exclusively end-stage
disease expression profiles. We have investigated the proportion of overlapping eQTLs between our study
and other studies, including two studies on human brain tissue (Table S4).24,26,45,46 Studies of the genetics
of gene expression appear to have modest overlap in the eQTLs identified. For example, 36.1% of genes
mapped by a cis eQTL in lymphocytes were identified in a study using lymphoblastoid cell lines.24,45 A small-
er overlap (22%) was found between two studies on brain tissue, which may partly be due to low statistical
power.26,46 In the present study, 37 – 52% of the genes mapped by cis eQTLs in human brain tissue studies
appeared to be present in our data (Table S4). The proportion of overlap with studies on blood-derived
tissues was comparable (41 – 45%). Considering the relatively high concordance of genes mapped by cis
eQTLs in our screen with those found in human brain tissue, we consider blood to be a valid starting point
for genetic mapping of gene expression in ALS. A large collection of central nervous system tissue control
samples may, however, further boost the discovery of novel genetic variants that are associated with ALS.
The focused analysis of variants in the chromosome 9p21.2 locus, which was previously associated
with ALS7,12, did not identify rs2814707 or rs3849942 as eQTL SNPs. We did, however, find evidence of
two other SNPs (rs10122902 and rs1565948), located within a large LD block surrounding the previous-
ly associated markers, to be correlated with altered expression levels of C9orf72 isoform a. SNP rs1565948
was associated with ALS in our joint GWAS data. The rs10122902 variant was not associated with ALS in
our joint GWAS, but was previously shown to be part of a haplotype with rs3849942, in which the major
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
49
4
allele of rs10122902 was associated with increased risk of ALS.12 Genetic variation in the chromosome
9p21.2 locus, therefore, appears to be associated with altered gene expression of C9orf72. The recent
discovery of the intronic hexanucleotide repeat expansion in C9orf72 on a common haplotype in 9p21.2
linked families with ALS and FTD15,16,47 thus illustrates the potential of the combined use of gene expression
and genotyping in search for causative genes in human diseases. The mechanism though of the recently
discovered repeat expansion in C9orf72 remains to be established. There could be a direct effect of expres-
sion levels of isoforms of C9orf72, or a “trans”-like effect through RNA-toxicity, as shown in other repeat
expansions diseases including fragile X-associated tremor/ataxia syndrome (FXTAS).48 Other types of ex-
periments are needed to elucidate this mechanism.
In summary, our genome-wide study of the genetics of gene expression has identified one cis eQTL
for sporadic ALS, which modulates CYP27A1 expression and additionally points to C9orf72 in the chromo-
some 9p21.2 locus as the gene involved in ALS pathogenesis. To further identify eQTLs relevant to ALS, the
concomitant analysis of epigenetic and other level -omic data, e.g. proteomic or metabonomic can be used,
as recently shown in a model organism.49 These studies are preferably performed in ‘ALS target tissues’,
including post-mortem central nervous system tissues and induced pluripotent stem cells differentiated to
a neuronal or glial lineage. Such studies may provide us with more insight into novel pathogenic pathways
and networks causal to this devastating disease.
50
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MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
53
4
SUPPORTING INFORMATION
TEXT S1. GWAS QUALITY CONTROL.
The following quality control measures were applied to the genome-wide genotype data. Quality control
was performed for the discovery and replication data separately.
1. MERGING DATASETS
Only SNPs common to all datasets were extracted. Tri-allelic SNPs or SNPs with A/T or C/G alleles were
removed to prevent the occurrence of allele swaps. Subsequently, datasets were merged per country. After
each merge, a flipscan was performed using PLINK software to check for possible allele swaps.1
2. REMOVAL OF DUPLICATE SAMPLES
SNPs on chromosome 22 were used for an identity-by-descent (IBD) analysis in PLINK. Pairs of individuals
with a relatedness measure (pi-hat) value >0.9 were considered to be indicative of a duplicate sample.
From these pairs, one of the individuals was randomly removed from the data.
3. SNP MARKER QUALITY CONTROL
SNPs with a minor allele frequency (MAF) <5%, or with a genotyping call rate <95%, or not in Hardy-Wein-
berg equilibrium in controls (test p<1×10−4) were removed.
4. SAMPLE QUALITY CONTROL
Samples where gender was not defined in the phenotype file, or with a genotyping call rate <95% were re-
moved. Additionally, inbreeding coefficients (F) were calculated in PLINK, and samples with high (F>0.05)
or low (F<−0.025) heterozygosity rates were excluded.
5. DIFFERENTIAL MISSINGNESS
SNPs were tested for differing missing data rates between cases and controls, and SNPs with a test
p<1×10−3 were removed. Subsequently, a haplotype-based test for non-random missing genotype data
was performed in PLINK, and SNPs with estimated haplotype frequencies >2%, and a test p<1×10−10
were excluded.
6. GENDER CHECK
Genetic gender (based on heterozygosity rates of X chromosome SNPs) was compared to the gender
reported in the phenotype file, and samples with mismatches were removed.
7. CHECK FOR RELATEDNESS BETWEEN INDIVIDUALS
For this analysis, a subset of SNPs in approximate linkage equilibrium was selected by linkage disequilib-
54
rium (LD)-based SNP pruning in PLINK. Autosomal SNPs with a genotyping call rate >0.999, MAF >5%,
and a 100% call rate per sample were LD-based pruned using PLINK’s default settings. In the discovery
data, this resulted in a pruned set of 31,362 SNPs, and in the replication data, this subset consisted of
34,400 markers.
The pruned set of markers was used for an IBD analysis in PLINK. For pairs of individuals with a pi-hat value
>0.2 the individual with the lowest genotyping rate was removed.
8. INDENTIFY POPULATION SUBSTRUCTURE
Population substructure was assessed by principal components analysis using the EIGENSTRAT program
from the EIGENSOFT v3.0 software package.2 Genotypes for the previously generated subset of markers
were merged with genotypes for different populations included in HapMap phase III release 2 (1,184 indi-
viduals), which were used as a reference.
Plots were generated of the first two principal components, and exclusion thresholds for population outliers
were defined by visual inspection. Samples identified as population outliers were removed, and principal
components analysis was reapplied to the remaining individuals.
Details of quality control statistics for the GWAS cohorts are summarized in Table S5.
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
55
4
56
n ALS cases n Controls Platform
Discovery The Netherlands 1016 7069 Illumina 317K, 370K, 550K Belgium 300 328 Illumina 370K Sweden 458 455 Illumina 370K Ireland 220 209 Illumina 550K United States 267 267 Illumina 550K Total 2261 8328 Replication France 231 709 Illumina 317K United Kingdom 239 212 Illumina 317K United States 736 791 Illumina 317K Ireland 101 123 Illumina 610K Total 1307 1835 Joint GWAS 3568 10163
ALS, amyotrophic lateral sclerosis; GWAS, genome-wide association study.
Table S2
GWAS populations and genotyping platforms
Tabel S1
Expression study populations
Gender, female Age, mean n n % y
Discovery ALS cases 162 61 38 63.9 Controls 207 90 43 62.7 Replication ALS cases 161 67 42 63.6 Controls 206 97 47 62.0 Total 736 315 43 63.2
ALS, amyotrophic lateral sclerosis.
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
57
Locus Illum
ina prob
e id
entifier SN
P M
inor allele
LD w
ith index
SN
P (rs4674345)
GW
AS d
iscovery SN
P association G
WAS rep
lication SN
P association
Joint GW
AS S
NP
association eQ
TL p value after
perm
utations Ex
pression
variance ex
plained
(R2)
r2
OR
p
OR
p
OR
p
Discovery
Rep
lication Com
bined
data
CYP
27
A1,
Chr. 2
ILMN
_170
4985 rs4674345
G
1 1.0
8 0.0
49 1.23
1.32×10−
4 1.12
1.84×10−
4 1.65×
10−
46 1.19×
10−
47 0.62
rs230
3565 G
0.66
1.09
0.0
20
1.22 2.25 ×
10−
4 1.13
8.79×10−
5 2.35×
10−
42 2.34×
10−
35 0.57
rs1554622
C
0.79
1.09
0.0
27 1.19
1.00×
10−
3 1.11
3.98×10−
4 2.93×
10−
56 1.27×
10−
51 0.65
rs760
7369 A
0.58
1.09
0.0
17 1.19
1.06×
10−
3 1.12
2.61×10−
4 6.27×
10−
35 1.0
8×10−
32 0.55
rs186370
4 A
0.55
1.10
0.0
16 1.18
2.37 ×10−
3 1.12
2.26×10−
4 1.75×
10−
31 3.98×
10−
28 0.52
rs3770
214 A
0.54
1.10
0.0
16 1.17
3.95×10−
3 1.12
3.14×10−
4 1.0
5×10−
31 3.0
7×10−
27 0.52
rs4674338
A
0.66
1.09
0.0
20
1.16 6.46×
10−
3 1.11
7.51×10−
4 1.70×
10−
42 1.26×
10−
42 0.59
rs921968
C
0.39
1.09
0.0
20
1.15 0.0
12 1.11
7.88 ×10−
4 1.0
6×10−
26 1.71×
10−
22 0.48
The direction of effect for all SN
P-transcript pairs was the sam
e; for each SN
P, the minor allele w
as associated with increased C
YP2
7A
1 expression levels. LD estim
ates with S
NP rs4674345 and S
NP
association results in the joint GW
AS data w
ere based on a total of 3,568 ALS
patients and 10,163 controls. The gene expression explained variance (R
2) was estim
ated from expression data from
both discovery and replication eQ
TL datasets combined. C
YP2
7A
1, cytochrome P450
, family 27, subfam
ily A, polypeptide 1; C
hr., chromosom
e; LD, linkage disequilibrium
; GW
AS, genom
e-wide association study;
OR, odds ratio; eQ
TL, expression quantitative trait locus; ALS
, amyotrophic lateral sclerosis.
Stud
y Sam
ple size
Tissue n genes m
apped
by cis eQ
TLs O
verlapping
results
n sign. threshold
n
n
This study, two stages
369 + 367 W
hole blood perm
uted p FDR 0
.05
2,211 –
–G
öring et al. 3 1,240
Lym
phocytes LO
D score >3
737 332
45.0
Stranger et al. 4
270
Lymphoblastoid cell lines
permuted p<0
.001
299 122
40.8
Webster et al. 5
364 Brain cortex
permuted p<0
.05
280
103
36.8 G
ibbs et al. 6 60
0
Four regions in human brain
FDR 0
.05
281 146
52.0
Com
parison of the number of genes m
apped by cis eQTLs in the present study and four other studies that have looked into the genetics of gene
expression. The number of genes m
apped by cis eQTLs w
as based on the significance threshold that was used in each of the studies. eQ
TL, expression quantitative trait locus; FD
R, false discovery rate; LO
D, logarithm
of odds.
%
Table S3
Results for replicated eQ
TLs associated with C
YP2
7A1 ex
pression levels
Table S4
cis eQTL overlap w
ith previous studies
4
58
Dataset
Pre QC
QC S
NPs
QC sam
ples
After Q
C
Cohort, country S
ource ALS
CO
N
SN
Ps Com
mon
MAF
HW
E Call
rate M
issing ALS
/CO
N
Missing b
y hap
lotype
Dup
li-cates
Call
rate H
etero-zygosity
Sex
check
Related
Pop
ulation outliers
ALS
CO
N
SN
Ps
Discovery
N
etherlands U
MC U
trecht 461
450
317503
311395 8131
3440
4091
22040
5891
0
3 10
3
3 6
450
436 268952
Netherlands
UM
C U
trecht 582
629 370
404
311395 8131
3440
4091
22040
5891
8 8
2 5
8 17
566 597
268952 N
etherlands U
MC U
trecht 0
5974 561466
311395 8131
3440
4091
22040
5891
14 0
7 3
534 20
0
5396 268952
Netherlands
RS-I cohort, The
Rotterdam
Study
0
704
561466 311395
8131 3440
40
91 220
40
5891 1
2 11
32 10
8
0
640
268952
Belgium
U
niversity Hospital
Gasthuisberg
300
328 370
404
311395 8131
3440
4091
22040
5891
3 27
11 8
3 2
300
328 268952
Sw
eden U
mea U
niversity H
ospital
458 455
37040
4 311395
8131 3440
40
91 220
40
5891 8
0
23 15
12 22
458 455
268952
Ireland Beaum
ont Hospital,
Dublin
220
209
561466 311395
8131 3440
40
91 220
40
5891 0
0
2 0
0
1 220
20
9 268952
USA
NIH
267
267 555351
311395 8131
3440
4091
22040
5891
3 1
6 0
0
3 267
267 268952
Total
2261 8328
311395 8131
3440
4091
22040
58
91 37
41 72
66 570
79
2261 8328
268
952
Rep
lication
France Evry
251 724
307790
30
1686 7735
2060
12579
9839 4571
0
19 6
4 0
6 231
709
266492 U
K King's C
ollege London
245 221
307790
30
1686 7735
2060
12579
9839 4571
0
0
4 6
0
5 239
212 266492
USA
MG
H &
Atlanta 753
811 30
7790
301686
7735 20
60
12579 9839
4571 0
0
12 11
0
14 736
791 266492
Ireland Beaum
ont Hospital,
Dublin
103
127 620
901
301686
7735 20
60
12579 9839
4571 5
0
0
1 0
0
101
123 266492
Total
1352 18
83
30
1686
7735 20
60
12579 98
39 4571
5 19
22 22
0
25 130
7 18
35 266492
QC, quality control; ALS
, amyotrophic lateral sclerosis; C
ON
, control; MAF, m
inor allele frequency; HW
E, Hardy
-Weinberg Equilibrium
.
Table S5
Details of quality control of genom
e-wide genotype data
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
59
Figure S1
Manhattan plot of autosomal SNP association p values in the GWAS discovery set
On the X-axis genomic positions of SNPs aligned to NCBI genome build 36, chromosome borders are designated
by changing dot colors. On the Y-axis -log10 (p values) for association between SNP genotype and disease status as
obtained from logistic regression analyses in the GWAS discovery set. The dotted line indicates the threshold for
genome-wide significance (p=5×10-8). GWAS, genome-wide association study.
Figure S2
Quantile-quantile plot of observed -log10
(p values) versus the expectation under
the null for the genome-wide association results in the GWAS discovery set
The figure shows departure from the null distribution
with λGC
=1.032. GWAS, genome-wide association study.
4
60
Figure S3
Plots for SNP genotype vs. expression level correlations for eQTL SNPs modulating CYP27A1 expression levels
On the Y-axis, the residuals of log2 transformed expression levels for probe ILMN_1704985 mapping to
CYP27A1 after regression of covariates in the replication data. On the X-axis SNP genotype bins, according
to an additive model; on the left homozygotes for the major allele and homozygotes for the minor allele on the
right. A regression line is plotted for each linear model. P values and R2 (variance explained) for GWAS and
eQTL associations in both discovery and replication cohorts are shown below each plot. Disc, Discovery; Repl,
Replication; eQTL, expression quantitative trait locus; GWAS, genome-wide association study.
SUPPLEMENTARY REFERENCES
1. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al. PLINK: a tool set for
whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559-
575.
2. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, et al. Principal components
analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904-909
3. Göring HHH, Curran JE, Johnson MP, Dyer TD, Charlesworth J, et al. Discovery of expression QTLs
using large-scale transcriptional profiling in human lymphocytes. Nat Genet 2007;39:1208-
1216.
4. Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, et al. Genome-wide associations of
gene expression variation in humans. PLoS Genet 2005;1:e78.
5. Webster JA, Gibbs JR, Clarke J, Ray M, Zhang W, et al. Genetic control of human brain transcript
expression in Alzheimer disease. Am J Hum Genet 2009;84:445-458.
6. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, et al. Abundant quantitative trait
loci exist for DNA methylation and gene expression in human brain. PLoS Genet 2010;6:e1000952
MAPPING OF GENE EXPRESSION REVEALS CYP27A1 AS A SUSCEPTIBILITY GENE FOR SPORADIC ALS
61
Figure S4
Plots for SNP genotype vs. expression level correlations for eQTL SNPs modulating C9orf72 expression levels
On the Y-axis, the residuals of log2 transformed expression levels for probe ILMN_1741881 mapping to
C9orf72 after regression of covariates in the replication data. On the X-axis SNP genotype bins, accor-
ding to an additive model; on the left homozygotes for the major allele and homozygotes for the minor
allele on the right. A regression line is plotted for each linear model. P values and R2 (variance explained)
for GWAS and eQTL associations in both discovery and replication cohorts are shown below each plot.
Disc, Discovery; Repl, Replication; eQTL, expression quantitative trait locus; GWAS, genome-
wide association study
4
PART II
Genetic pleiotropy
5
NO EVIDENCE FOR SHARED GENETIC BASIS
OF COMMON VARIANTS IN MULTIPLE
SCLEROSIS AND AMYOTROPHIC LATERAL
SCLEROSIS
HUMAN MOLECULAR GENETICS. 2014;23(7):1916-22
Frank P Diekstra*, An Goris*, Jessica van Setten*, Stephan Ripke*, Nikolaos A
Patsopoulos, Stephen Sawcer, Michael van Es, The Australia and New Zealand
MS Genetics Consortium, Peter Andersen, Judith Melki, Vincent Meininger, Orla
Hardiman, John Landers, Robert Brown Jr, Ammar Al-Chalabi, Bryan Traynor,
Adriano Chio, Roel Ophoff, Jorge Oksenberg, Philip Van Damme, Alastair
Compston, Wim Robberecht, Benedicte Dubois, Leonard H van den Berg,
Philip L De Jager, Jan H Veldink, Paul IW de Bakker
* These authors contributed equally to this work
INTRODUCTION
Multiple sclerosis (MS, OMIM: 126200) is a common disease of the central nervous system characterized
by inflammation, demyelination and axonal loss.1 Large extended families with the disease are extremely
rare2, but a genetic component in susceptibility to MS has been clearly demonstrated.1 Currently known risk
variants include four classical human leukocyte antigen (HLA) alleles and >50 single-nucleotide polymor-
phisms (SNPs) outside the HLA region.3,4
Amyotrophic lateral sclerosis (ALS, OMIM: 105400) is a neurodegenerative condition with devas-
tating impact. Multiple cellular events contribute to the pathobiology, including mitochondrial dysfunction,
excitotoxicity, protein aggregation in the cytosol, impaired axonal transport, neuroinflammation and dysreg-
ulated RNA signaling.5 About 10 – 20% of cases are familial, and up to 50% of these can be explained by
known mutations in 18 genes including SOD1, FUS, TARDBP and C9orf72.6 The majority of patients are iso-
lated cases, however. Not all results from genome-wide association studies (GWAS) have been replicated,
but two regions of association have been confirmed in independent studies: a locus on chromosome 9 and
variation in the UNC13A region.7-11
One of the lessons learned in the GWAS era is the substantial overlap in susceptibility loci between
diseases. This has been demonstrated for immune-related12,13, metabolic14 and psychiatric15 disorders and
indicates, sometimes unexpectedly, commonalities and differences between diseases. MS indeed shares
several susceptibility loci with other immune-related disorders, including type 1 diabetes and Crohn’s dis-
66
ABSTRACT
Genome-wide association studies have been successful in identifying common variants that
influence the susceptibility to complex diseases. From these studies, it has emerged that there
is substantial overlap in susceptibility loci between diseases. In line with those findings, we
hypothesized that shared genetic pathways may exist between multiple sclerosis (MS) and
amyotrophic lateral sclerosis (ALS). While both diseases may have inflammatory and neurode-
generative features, epidemiological studies have indicated an increased co-occurrence within
individuals and families. To this purpose, we combined genome-wide data from 4088 MS pa-
tients, 3762 ALS patients and 12 030 healthy control individuals in whom 5 440 446 single-
nucleotide polymorphisms (SNPs) were successfully genotyped or imputed. We tested these
SNPs for the excess association shared between MS and ALS and also explored whether
polygenic models of SNPs below genome-wide significance could explain some of the observed
trait variance between diseases. Genome-wide association meta-analysis of SNPs as well
as polygenic analyses fails to provide evidence in favor of an overlap in genetic susceptibility
between MS and ALS. Hence, our findings do not support a shared genetic background of
common risk variants in MS and ALS.
5
ease.3 However, besides the immune component, key features of neurodegeneration, i.e. axonal transection,
neuronal cell atrophy and neuronal death, are early pathological events in MS.1 Moreover, the irreversible
disability seen in patients correlates stronger with neuronal damage than with inflammatory demyelina-
tion16, although the cause of the neuronal damage remains elusive. On the other hand, for diseases clas-
sified as neurodegenerative such as ALS, an inflammatory or immune component has been implicated
but is not yet conclusive.17,18 Case reports have described patients affected by both diseases19-24 and an
increased co-occurrence of MS and ALS compared with what is expected has been observed.25,26 Studies
also report an increased risk of MS among relatives of patients suffering from ALS and vice versa27-29, and
some but not all studies report geographical correlation in mortality rates of both diseases.30,31
In order to assess the shared genetic contribution between MS and ALS, possibly through common
pathways of neurodegeneration or inflammation, we investigated the overlap of common susceptibility
variants using available GWAS data.
RESULTS
We first investigated previously reported3,4,7-11 susceptibility loci in one disease for evidence of association
in the other. None of the reported ALS susceptibility loci show evidence for association with MS (Table
1). Out of 56 established, independent MS susceptibility loci3,4, 4 (7.1%) show nominal significance for
association with ALS, but none survived multiple testing for the number of SNPs investigated (Table 2). As
expected because of the overlap between the datasets used here and those used in the original studies of
each disease separately, all previously reported risk factors for either MS or ALS show the same direction of
effect for the respective disease in this dataset as in the original studies. Regarding the other disease, 4/5
reported ALS risk SNPs show the same direction of effect in MS as in ALS (sign test P = 0.38), and among
established MS-associated SNPs, 26/56 (46%) SNPs show the same direction of effect in ALS (sign test
P = 0.69). Four SNPs were previously highlighted for reaching suggestive P-values of <10-5 for association
with disease course (bout onset versus primary progressive MS).3 Only one of these shows evidence for
association with ALS but in the opposite direction (data not shown).
NO EVIDENCE FOR SHARED GENETIC BASIS OF COMMON VARIANTS IN MS AND ALS
67
Table 1
Evidence of association for reported ALS susceptibility loci with MS
chr rsid position (hg19)
Gene Risk Allele P ALS OR ALS P MS OR MS
1 rs6700125 59702797 FGGY9 T 0.087 1.06 0.085 1.06
7 rs10260404 154210798 DPP610 C 0.0049 1.10 0.55 1.02
9 rs3849942 27543281 C9orf727,8
T 5.8E-06 1.19 0.26 1.04
12 rs2306677 26636386 ITPR211 A 0.080 1.10 0.60 1.03
19 rs12608932 17752689 UNC13A7 C 8.3E-09 1.21 0.39 0.97
68
Table 2
Evidence of association for independent, established MS susceptibility loci with ALS
chr rsid position (hg19) Gene Risk Allele
P MS OR MS
P ALS OR ALS
1 rs4648356 2709164 MMEL1 (TNFRSF14) C 0.012 1.09 0.97 1.00
1 rs11810217 93148377 EVI5 T 0.00032 1.14 0.12 0.94
1 rs11581062 101407519 SLC30A7 G 0.032 1.08 0.025 1.08
1 rs1335532 117100957 CD58 A 1.2E-08 1.35 0.97 1.00
1 rs1323292 192541021 RGS1 A 0.0098 1.11 0.53 1.03
1 rs7522462 200881595 C1orf106 G 0.00083 1.13 0.023 0.92
2 rs6718520a,4
43325570 ZFP36L2 (THADA) A 1.2E-05 1.16 0.84 1.01
2 rs12466022 43359061 ZFP36L2 (THADA) C 4.2E-05 1.16 0.76 0.99
2 rs7595037 68647095 PLEK T 1.6E-05 1.15 0.32 0.97
2 rs17174870 112665201 MERTK C 0.00012 1.15 0.79 1.01
2 rs10201872 231106724 SP140 T 0.00056 1.15 0.13 1.07
3 rs669607 28071444 intergenic C 2.5E-05 1.15 0.57 0.98
3 rs2028597 105558837 CBLB G 0.56 1.03 0.52 1.04
3 rs2293370 119219934 C3orf1 G 0.056 1.08 0.29 0.96
3 rs9282641 121796768 CD86 G 0.0015 1.22 0.52 0.96
3 rs2243123 159709651 IL12A C 0.17 1.05 0.25 1.04
4 rs228614 103578637 MANBA G 0.0092 1.18 0.23 0.625
5 rs6897932 35874575 IL7R C 0.0014 1.12 0.20 0.96
5 rs4613763 40392728 PTGER4 C 0.00014 1.19 0.87 0.99
5 rs2546890 158759900 IL12B A 3.8E-06 1.16 0.78 1.01
6 rs12212193 90996769 BACH2 G 0.0055 1.09 0.14 1.05
6 rs802734 128278798 PTPRK A 0.0014 1.12 0.89 1.00
6 rs11154801 135739355 AHI1 A 0.014 1.08 0.49 0.98
6 rs17066096 137452908 IL22RA2 G 0.00096 1.13 0.29 0.96
6 rs1738074 159465977 TAGAP C 0.00075 1.12 0.45 0.98
7 rs354033 149289464 ZNF767 G 0.00079 1.13 0.26 1.04
8 rs1520333 79401038 PKIA G 0.11 1.06 0.41 1.03
8 rs4410871 128815029 MYC C 0.018 1.09 0.54 1.02
9 rs2150702 5893861 MLANA G 2.5E-05 1.14 0.015 1.08
10 rs3118470 6101713 IL2RA C 0.00078 1.12 0.76 1.01
10 rs1250550 81060317 ZMIZ1 A 0.0024 1.11 0.66 0.98
10 rs7923837 94481917 HHEX G 0.015 1.08 0.18 0.96
11 rs650258 60832282 CD5 C 0.00018 1.14 0.097 0.95
11 rs630923 118754353 CXCR5 C 0.033 1.11 0.066 1.08
12 rs1800693 6440009 TNFRSF1A G NAb NA 0.67 1.01
12 rs10466829 9876091 CLECL1 A 0.0009 1.11 0.49 0.98
12 rs12368653 58133256 AGAP2 A 0.0018 1.10 0.31 0.97
12 rs949143 123595163 ARL6IP4 G 0.015 1.08 0.57 0.98
14 rs4902647 69254191 ZFP36L1 C 0.00022 1.12 0.72 0.99
14 rs2300603 76005557 BATF T 0.014 1.10 0.10 0.94
14 rs2119704 88487689 GPR65 C 0.045 1.13 0.23 0.93
16 rs2744148 1073552 SOX8 G 0.023 1.10 0.30 0.95
16 rs7200786 11177801 CLEC16A A 8.8E-05 1.14 0.58 0.98
16 rs13333054 86011033 IRF8 T 0.063 1.09 0.98 1.00
17 rs9891119 40507980 STAT3 C 0.00016 1.13 0.86 0.99
17 rs180515 58024275 RPS6KB1 G 0.093 1.06 0.74 1.01
18 rs7238078 56384192 MALT1 T 0.00075 1.13 0.99 1.00
19 rs1077667 6668972 TNFSF14 C 0.033 1.10 0.10 0.94
19 rs8112449 10520064 CDC37 G 0.14 1.05 0.83 0.99
19 rs874628 18304700 MPV17L2 A 0.021 1.09 0.65 0.98
19 rs2303759 49869051 DKKL1 G 0.0075 1.11 0.034 1.08
20 rs2425752 44702120 NCOA5 T 0.0001 1.14 0.40 0.97
20 rs2248359 52791518 CYP24A1 C 0.00085 1.12 0.29 1.04
20 rs6062314 62409713 ZBTB46 T 0.047 1.14 0.52 1.04
22 rs2283792 22131125 MAPK1 G 0.00036 1.12 0.23 1.04
22 rs140522 50971266 ODF3B T 0.0022 1.12 0.72 0.99
Source of variants:
3, except where specified:
4
a r
2 = 0.15 with adjacent variant rs12466022,
b No SNP with r
2 > 0.6
We next combined summary results from both MS and ALS datasets in a meta-analysis, looking for modest
effects in each dataset that strengthen each other in the combined analysis. The combined analysis of both
diseases included a total of 5 440 446 SNPs (Fig. 1). The genomic inflation factor (λs) was 1.033 for MS,
0.997 for ALS and 1.005 for the combined MS-ALS meta-analysis. In the meta-analysis, the HLA region
reaches genome-wide significance, but this is driven by the MS component (P ALS with same direction of
effect ≥0.01). One region, near NPEPPS on chromosome 17 (rs2935183), reaches suggestive associa-
tion levels of P < 5 × 10-7 but is once again driven by MS [P (MS) = 6.5 × 10-7; P (ALS) = 0.41].
Lastly, we investigated the possibility of an overlap of small susceptibility effects (polygenic score
or ‘en masse’ effect). Therefore, we tested collectively SNPs that reached certain thresholds in the MS or
ALS GWASs for association with ALS and MS, respectively. After correction for multiple testing, none of the
models were significantly associated with disease (Tables 3 and 4), with the best model for each disease
explaining only 0.05% of the phenotypic variance. To test whether the lack of association may have been
affected by association results in the HLA region (which is known to be strongly associated with MS, but
not with ALS), we repeated the polygenic analysis excluding SNPs in the HLA region (removing all SNPs on
chromosome 6 between 29 and 33 Mb). This did not influence the results (Supplementary Material, Table
S1).
NO EVIDENCE FOR SHARED GENETIC BASIS OF COMMON VARIANTS IN MS AND ALS
69
Figure 1
Manhattan plots
Manhattan plots of (A) combined MS – ALS analysis and (B) an overlay of the
individual components consisting of both diseases (blue: MS, red: ALS). The y-axis
has been cut off at -logP = 10. Red and blue horizontal lines indicate genome-
wide (P < 5 × 10-8) and suggestive (P < 5 × 10-7) evidence.
5
DISCUSSION
In this study, we have applied several statistical approaches to the investigation of shared susceptibility
loci between the neurological diseases MS and ALS, which are both thought to involve inflammatory and
neurodegenerative components1,17,18 and for which case reports and epidemiological studies have reported
70
Table 3
Polygenic score based on MS data in ALS
Table 4
Polygenic score based on ALS in MS
Model P-value Number of SNPs
Nagelkerke r2 corrected for baseline
a
<5E-8 0.820 75 5.4E-06
<5E-7 0.963 90 2.0E-07
<5E-6 0.987 114 0.0E+00
<5E-5 0.827 184 5.0E-06
<5E-4 0.880 633 2.4E-06
<5E-3 0.414 3454 6.9E-05
<0.05 0.775 22284 8.5E-06
<0.1 0.848 38861 3.8E-06
<0.2 0.986 66276 1.0E-07
<0.3 0.743 89109 1.1E-05
<0.4 0.459 108626 5.7E-05
<0.5 0.412 125558 7.0E-05 a Baseline: PC1-3, dummy coded cohorts
Model P-value Number of SNPs
Nagelkerke r2 corrected for baseline
a
<5E-8 0.843 3 4.5E-06
<5E-7 0.785 4 8.4E-06
<5E-6 0.500 7 5.2E-05
<5E-5 0.452 49 6.4E-05
<5E-4 0.928 389 9.3E-07
<5E-3 0.306 3075 1.2E-04
<0.05 0.032 22315 5.2E-04
<0.1 0.050 38922 4.4E-04
<0.2 0.040 66738 4.8E-04
<0.3 0.057 89592 4.1E-04
<0.4 0.048 108839 4.4E-04
<0.5 0.074 125337 3.6E-04 a Baseline: PC1-5, dummy coded cohorts
co-occurrence within individuals or families.19-29 The strength of the study is that different statistical ap-
proaches are consistent in demonstrating that the number of regions in the genome with evidence for an
overlap in susceptibility between the two diseases is not more than expected by chance. Among 65 loci
having previously been implicated in one disease or disease subgroup, only 5 show nominally significant
association with the other disease and none survive correction for multiple testing. There was no significant
enrichment for the same direction of effect in both diseases. In a combined analysis of both diseases, no
region outside of the HLA reaches genome-wide significance and only one reaches suggestive association
levels of P < 5 × 10-7. Moreover, for both these regions with evidence for association in both diseases,
results appear driven by strong evidence in MS, despite sample sizes of similar magnitude for both diseases.
Furthermore, the polygenic analysis demonstrates that it is unlikely that many common variants with effect
sizes that are beyond the detection threshold for association are shared between the two diseases. This
contrasts with other diseases where a polygenic risk score calculated for one disease is associated with
related diseases, as in the example of schizophrenia and bipolar disorder.15
MS is a clinically heterogeneous disease, with the majority of patients (~80%) suffering from a
bout onset form of the disease with relapses and remissions, possibly followed by secondary progres-
sion, and the remaining 20% being characterized by progression from onset.1 It has been speculated that
both forms represent a continuous spectrum of disease phenotypes with risk factors driving the balance
between inflammation and neurodegeneration.32 Genetic association studies have so far not provided
evidence for a different pathogenesis of the two forms.3 On the contrary, HLA-DRB1*1501, the strongest risk
factor in MS and especially immunological in nature, is shared between both bout onset and primary pro-
gressive MS. In this study, there was no evidence for shared loci with the same direction of effect between
ALS and primary progressive MS.
A total of >50 common risk variants for MS have now been identified.3,4 There is a highly significant
enrichment for immune system genes in this list, with only few variants having a potential neurological
function.3 GWAS studies in ALS have seen limited success.8 This discrepancy in the number of common risk
variants identified between immunological and other diseases has been suggested to reflect a history of
selection and adaptation of variants influencing the immune system.33,34 Mutations in several genes cause
familial forms of ALS, and it has been thought that less common (1 – 5%) or rare (<1%) variants play a role
in sporadic forms of the disease as well.35 Similarly, first reports of less common and rare variants in MS
are emerging.36,37 This category of variants, which are not well captured by current genome-wide associa-
tion studies, may explain part of the heritability in MS and ALS that remains unaccounted for by common
variants (‘missing heritability’), and potentially the shared neurodegenerative component. Next-generation
sequencing offers a technology suited to address this hypothesis.
It has recently been demonstrated that a large proportion of ALS is related to a GGGGCC hexanu-
cleotide repeat expansion in intron 1 of C9orf7238,39, located in a region on chromosome 9p previously high-
lighted in GWAS studies of ALS.7,8 We did not observe any association of the C9orf72 region with MS. This
NO EVIDENCE FOR SHARED GENETIC BASIS OF COMMON VARIANTS IN MS AND ALS
71
5
is in line with the fact that no repeat expansions were observed in a cohort of 215 MS patients.25 Hence,
C9orf72 variation does not appear to be a risk factor for MS. It has been suggested that MS can act as a
modifier that increases the likelihood of C9orf72 expansions becoming penetrant and causing concurrent
ALS25, although further investigation is required.40
In summary, the overlap of common variants between MS and other autoimmune disorders is
not matched by a similar overlap between MS and other neurological disorders, such as ALS in this study.
Whether less common or rare variants explain some of the shared neurodegenerative or neuroinflamma-
tory aspects of both diseases cannot be addressed with the currently available datasets and remains to be
examined with emerging technologies.
MATERIALS AND METHODS
We used data from 6 datasets totaling 4088 MS patients and 7144 controls from a recent meta-analysis
of MS genome-wide association studies.4 Imputation was performed using Beagle v3.1 and the 1000
Genomes Project (1000G) Phase I (a) reference panel (2010/11 data freeze, 2011/6 haplotypes), and
analysis was performed as described previously using the postimputation probabilities and the first five
principal components (PC) as covariates4, leading to association results for a total of 6 948 682 SNPs with
INFO of >0.10 and a minor allele frequency of >0.01 in all 6 datasets.
The ALS study population consists of 3 762 patients and 4 886 controls over 11 cohorts, for
which details have been described previously.7,41 Imputation was performed using Beagle v.3.1.1. software
with the 1000G CEU Aug 2010 reference panel. Analysis on dosage data including 3 PC led to association
results for 12 249 385 SNPs.
A/T and C/G SNPs were removed, and results from both datasets on 5 440 446 overlapping
SNPs were combined using an inverse variance fixed-effects model as implemented in the PLINK software
package.42 Power was >99% for OR of ≥1.2 and >80% for OR of ≥1.15 at a typical risk allele frequency
of 30% and genome-wide significance (P < 5 × 10-8).
Polygenic risk scores were calculated per individual to test the collective impact of SNPs that are
associated with ALS on MS and vice versa. For each trait (MS and ALS), we first pruned the association re-
sults of the GWAS by linkage disequilibrium (r2 = 0.1), preferentially keeping SNPs with lower P-values. We
selected twelve sets of SNPs (models) based on their GWAS P-values (<5 × 10-8, <5 × 10-7, <5 × 10-6, <5
× 10-5, <5 × 10-4, <5 × 10-3, <0.05, <0.1, <0.2, <0.3, <0.4 and <0.5). The smallest model contains three
SNPs, whereas the models of P < 0.5 contain >125 000 SNPs (Table 3). Next, we calculated a polygenic
risk score in all individuals of the other GWAS by summing up the dosages of the risk alleles in each model,
multiplied by the log-odds. We then tested the association between the risk score and the phenotype using
logistic regression with the same number of PCs as used in the original analysis of each trait (ALS: PC1-3,
MS: PC1-5) and dummy-coded cohorts as covariates. Nagelkerke r2 was calculated to test the variance
explained by each model.43
72
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with multiple sclerosis: a clinical and pathological report. Amyotroph Lateral Scler Other Motor Neuron
Disord 2000;1:349-353.
24. Hader WJ, Rpzdilsky B, Nair CP. The concurrence of multiple sclerosis and amyotrophic lateral sclerosis.
Can J Neurol Sci 1986;13:66-69.
25. Ismail A, Cooper-Knock J, Highley JR, Milano A, Kirby J, Goodall E, Lowe J, Scott I, Constantinescu CS,
Walters SJ, et al. Concurrence of multiple sclerosis and amyotrophic lateral sclerosis in patients with
hexanucleotide repeat expansions of C9ORF72. J Neurol Neurosurg Psychiatry 2012;84:79-87.
26. Turner MR, Goldacre R, Ramagopalan S, Talbot K, Goldacre MJ. Autoimmune disease preceding
amyotrophic lateral sclerosis: an epidemiologic study. Neurology 2013;81:1222-1225.
27. Hemminki, K Li X, Sundquist J, Hillert J, Sundquist K. Risk for multiple sclerosis in relatives and spouses
of patients diagnosed with autoimmune and related conditions. Neurogenetics 2009;10:5-11.
28. Etemadifar M, Abtahi SH, Akbari M, Maghzi AH. Multiple sclerosis and amyotrophic lateral sclerosis: is
there a link? Mult Scler 2012;18:902-904.
29. Hemminki, K Li X, Sundquist J, Sundquist K. Familial risks for amyotrophic lateral sclerosis and
autoimmune diseases. Neurogenetics 2009;10:111-116.
30. Landtblom AM, Riise T, Boiko A, Söderfeldt B. Distribution of multiple sclerosis in Sweden based on
mortality and disability compensation statistics. Neuroepidemiology 2002;21:167-179.
31. Bostrom I, Riise T, Landtblom AM. Mortality statistics for multiple sclerosis and amyotrophic lateral
sclerosis in Sweden. Neuroepidemiology 2012;38:245-249.
32. Hensiek AE, Seaman SR, Barcellos LF, Oturai A, Eraksoi M, Cocco E, Vecsei L, Stewart G, Dubois B,
Bellman-Strobl J, et al. Familial effects on the clinical course of multiple sclerosis. Neurology 2007;68:376-
383.
33. Corona E, Dudley JT, Butte AJ. Extreme evolutionary disparities seen in positive selection across seven
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complex diseases. PLoS One 2010;5:e12236.
34. Casto AM, Feldman MW. Genome-wide association study SNPs in the human genome diversity project
populations: does selection affect unlinked SNPs with shared trait associations? PLoS Genet
2011;7:e1001266.
35. Dion PA, Daoud H, Rouleau GA. Genetics of motor neuron disorders: new insights into pathogenic
mechanisms. Nat Rev Genet 2009;10:769-782.
36. De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT, Piccio L, Raychaudhuri S, Tran D,
Aubin C, et al. Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new
multiple sclerosis susceptibility loci. Nat Genet 2009;41:776-782.
37. Goris A, Fockaert N, Cosemans L, Clysters K, Nagels G, Boonen S, Thijs V, Robberecht W, Dubois B. TN
FRSF1A coding variants in multiple sclerosis. J Neuroimmunol 2011;235:110-112.
38. DeJesus-Hernandez M, Mackenzie IR, Boeve BF, Boxer AL, Baker M, Rutherford NJ, Nicholson AM, Finch
NA, Flynn H, Adamson J, et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of
C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 2011;72:245-256.
39. Renton AE, Majounie E, Waite A, Simon-Sanchez J, Rollinson S, Gibbs JR, Schymick JC, Laaksovirta H, van
Swieten JC, Myllykangas L, et al. A hexanucleotide repeat expansion in C9ORF72 is the cause of
chromosome 9p21-linked ALS-FTD. Neuron 2011;72:257-268.
40. Van Doormaal PTC, Gallo A, van Rheenen W, Veldink JH, van Es MA, Van den Berg LH. Amyotrophic
lateral sclerosis is not linked to multiple sclerosis in a population based study. J Neurol Neurosurg
Psychiatry 2013;84:940-941.
41. Chio A, Schymick JC, Restagno G, Scholz SW, Lombardo F, Lai SL, Mora G, Fung HC, Britton A, Arepalli
S, et al. A two-stage genome-wide association study of sporadic amyotrophic lateral sclerosis. Hum Mol
Genet 2009;18:1524-1532.
42. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly
MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J
Hum Genet 2007;81:559-575.
43. Nagelkerke NJD. A note on a general definition of the coefficient of determination. Biometrika
1991;78:691-692.
NO EVIDENCE FOR SHARED GENETIC BASIS OF COMMON VARIANTS IN MS AND ALS
75
5
SUPPLEMENTARY INFORMATION
INTERNATIONAL MULTIPLE SCLEROSIS GENETIC CONSORTIUM (IMSGC) MEMBERSHIP
Lisa Barcellos, David Booth, Jacob L McCauley, Manuel Comabella, Alastair Compston, Sandra D Alfonso,
Philip De Jager, Bertrand Fontaine, An Goris, David Hafler, Jonathan Haines, Hanne F. Harbo, Stephen
L. Hauser, Clive Hawkins, Bernhard Hemmer, Jan Hillert, Adrian Ivinson, Ingrid Kockum, Roland Martin,
Filippo Martinelli Boneschi, Jorge Oksenberg, Tomas Olsson, Annette Oturai, Nikolaos Patsopoulos,
Margaret Pericak-Vance, Janna Saarela, Stephen Sawcer, Anne Spurkland, Graeme Stewart, Frauke Zipp
76
Supplementary Table 1
Polygenic risk model excluding the HLA region
A. Polygenic score based on MS data in ALS
Model P-value # SNPs Nagelkerke r2 corrected for baseline
a
<5E-8 0.341 4 9.4E-05
<5E-7 0.446 8 6.0E-05
<5E-6 0.481 23 5.2E-05
<5E-5 0.512 87 4.5E-05
<5E-4 0.727 518 1.3E-05
<5E-3 0.405 3314 7.2E-05
<0.05 0.623 22116 2.5E-05
<0.1 0.732 29682 1.2E-05
<0.2 0.928 66084 9.0E-07
<0.3 0.652 88914 2.1E-05
<0.4 0.380 108424 8.0E-05
<0.5 0.338 125353 9.5E-05 a Baseline: PC1-3, dummy coded cohorts.
B. Polygenic score based on ALS data in MS
Model P-value # SNPs Nagelkerke r2 corrected for baseline
b
<5E-8 0.843 3 4.5E-06
<5E-7 0.785 4 8.4E-06
<5E-6 0.500 7 5.2E-05
<5E-5 0.452 49 6.4E-05
<5E-4 0.882 388 2.5E-06
<5E-3 0.597 3066 3.2E-05
<0.05 0.029 22274 5.4E-04
<0.1 0.050 38850 4.4E-04
<0.2 0.038 66630 4.9E-04
<0.3 0.052 89464 4.3E-04
<0.4 0.041 108697 4.7E-04
<0.5 0.061 125179 4.0E-04 b Baseline: PC1-5, dummy coded cohorts.
THE AUSTRALIA AND NEW ZEALAND MS GENETICS CONSORTIUM (ANZGENE) MEMBERSHIP
Rodney J Scott, Jeannette Lechner-Scott, Pablo Moscato, David R Booth, Graeme J Stewart, Robert
N Heard, Deborah Mason, Lyn Griffiths, Simon Broadley, Matthew A Brown, Mark Slee, Simon J Foote,
Jim Stankovich, Bruce V Taylor, James Wiley, Melanie Bahlo, Victoria Perreau, Judith Field, Hel-
mut Butzkueven, Trevor J Kilpatrick, Justin Rubio, Mark Marriott, William M Carroll, Allan G Kermode
NO EVIDENCE FOR SHARED GENETIC BASIS OF COMMON VARIANTS IN MS AND ALS
77
5
6
C9ORF72 AND UNC13A ARE SHARED RISK
LOCI FOR ALS AND FTD: A GENOME-WIDE
META-ANALYSIS
ANNALS OF NEUROLOGY. 2014;76:120-33
Frank P Diekstra, Vivianna M Van Deerlin,† John C van Swieten,† Ammar
Al-Chalabi, Albert C Ludolph, Jochen H Weishaupt, Orla Hardiman, John E
Landers, Robert H Brown Jr, Michael A van Es, R Jeroen Pasterkamp, Max
Koppers, Peter M Andersen, Karol Estrada, Fernando Rivadeneira, Albert
Hofman, Andre G Uitterlinden, Philip van Damme, Judith Melki, Vincent
Meininger, Aleksey Shatunov, Christopher E Shaw, P Nigel Leigh, Pamela J
Shaw, Karen E Morrison, Isabella Fogh, Adriano Chio, Bryan J Traynor, David
Czell, Markus Weber, Peter Heutink,‡ Paul I W de Bakker, Vincenzo Silani,‡
Wim Robberecht, Leonard H van den Berg,* Jan H Veldink,*
These authors were joint senior authors on this work *
on behalf of the International Collaboration for Frontotemporal †
Lobar Degeneration; see Supplementary Table 5 for full list of
contributors
on behalf of the SLAGEN Consortium; see Supplementary Table 5 ‡
for full list of contributors
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive
muscle weakness due to the loss of motor neurons in both brain and spinal cord. No cure exists and
disease etiology has not yet been fully elucidated. Important overlap exists with frontotemporal dementia
(FTD), which is characterized by changes in cognition, behavior and language. Clinically, approximately
5-15% of patients with ALS have FTD, while about 3-14% of FTD patients also fulfill the criteria for ALS.1,2
Neuropathologically, the majority of FTD cases can be divided in two subtypes, characterized by cellular
inclusions of either tau (FTD-tau) or TDP-43 (FTD-TDP). TDP-43 inclusions have been found in neurons
of both ALS and FTD-TDP patients.3 Lastly, substantial genetic overlap between ALS and FTD has been
reported. Linkage studies identified a locus of several megabases on chromosome 9p21 in families of
patients with both ALS and FTD.4-6 Previous genome-wide association studies (GWAS) of non-familial ALS
80
ABSTRACT
OBJECTIVE: Substantial clinical, pathological and genetic overlap exists between amyotrophic lateral
sclerosis (ALS) and frontotemporal dementia (FTD). TDP-43 inclusions have been found in both ALS
and FTD cases (FTD-TDP). Recently, a repeat expansion in C9orf72 was identified as the causal var-
iant in a proportion of ALS and FTD cases. We sought to identify additional evidence for a common
genetic basis for the spectrum of ALS-FTD.
METHODS: We used published GWAS data of 4,377 ALS patients and 13,017 controls and 435
pathology-proven FTD-TDP cases and 1,414 controls for genotype imputation. Data were analyzed
in a joint meta-analysis, by replicating topmost associated hits of one disease in the other, and by us-
ing a conservative rank products analysis, allocating equal weight to ALS and FTD-TDP sample sizes.
RESULTS: Meta-analysis identified 19 genome-wide significant single nucleotide polymorphisms
(SNPs) at C9orf72 on chromosome 9p21.2 (lowest p=2.6×10-12) and one SNP in UNC13A on chro-
mosome 19p13.11 (p=1.0×10-11) as shared susceptibility loci for ALS and FTD-TDP. Conditioning
on the 9p21.2 genotype increased statistical significance at UNC13A. A third signal, on chromosome
8q24.13 at the SPG8 locus coding for strumpellin, (p=3.91×10-7) was replicated in an independent
cohort of 4,056 ALS patients and 3,958 controls (p=0.026; combined analysis p=1.01×10-7).
INTERPRETATION: We identified common genetic variants at C9orf72, but in addition in UNC13A that
are shared between ALS and FTD. UNC13A provides a novel link between ALS and FTD-TDP, and
identifies changes in neurotransmitter release and synaptic function as a converging mechanism in
the pathogenesis of ALS and FTD-TDP.
6
helped fine-map this region, and recently, a hexanucleotide repeat expansion in C9orf72 was discovered in
this region.7-11 The C9orf72 repeat expansion is present in approximately 6% of sporadic ALS and sporadic
FTD patients, and in up to 37% and 25% of familial ALS and FTD cases, respectively.12 Additionally, muta-
tions in VCP have been implicated in both ALS patients and in FTD.13 Furthermore, mutations in the gene
for TDP-43 (TARDBP) have been found in ALS and FTD with motor neuron degeneration (FTD-MND), but
are rarely present in FTD-TDP cases without motor neuron symptoms.14,15
The majority of gene mutations have been found in familial cases of ALS and FTD, but these
mutations are less frequent in cases without a positive family history.11,12,16 Meta-analysis of GWAS data is
a powerful tool to discover new susceptibility loci for non-familial disease.17 The association signals from
a GWAS may represent common variants acting as ‘genetic risk factors’, but may also form a proxy for
rare, moderately penetrant genetic variants, such as the repeat expansion in C9orf72.7,9 The discovery of
the C9orf72 repeat expansion has, additionally, shown that intronic, non-coding variation may be causal to
disease. Previously, the most recent and largest GWAS of sporadic ALS identified the locus on chromosome
9p21.2 (comprising C9orf72) and UNC13A as susceptibility loci.10,11 Recently, the first GWAS of FTD-TDP
patients has been published, identifying three common variants in TMEM106B associated with susceptibility
to sporadic FTD.16 The association with TMEM106B variants has now been replicated in independent co-
horts including FTD-TDP patients.18,19
Both ALS and FTD may form parts of a spectrum of neurodegenerative disease. This spectrum
ranges from pure motor ALS, to ALS with mild cognitive impairment, to FTD-MND, and ultimately, to pure
FTD without motor neuron symptoms.20 In the present study, we sought to identify a common genetic basis
for this spectrum of neurodegenerative disease. Therefore, we conducted a meta-analysis of all available
GWAS data in ALS and TDP-43 positive FTD aimed at the discovery of additional common variants that
would affect susceptibility to both neurodegenerative diseases.
METHODS
SUBJECTS
ALS cohorts were derived from all available previously published GWAS of ALS patients.10,11 We included
16 cohorts of Caucasian sporadic ALS patients (n = 4,638) and/or unaffected controls (n = 14,038)
from six European countries and the USA for whom genome-wide genotype data were available. Previous
replication cohorts with selected SNP sets (for example obtained by TaqMan genotyping) could not be
included. For all cohorts, the diagnosis of probable or definite ALS was made according to the revised El
Escorial criteria.21
We obtained raw genotype data for 658 individuals that were originally genotyped for the FTD-
TDP GWAS, and were recruited from 11 countries in Europe, USA, Canada and Australia.16 In the original
publication, 598 cases with FTD-TDP pathology matched the inclusion criteria, of which 515 were used
for analysis. For the present study, we only included cases with FTD-TDP confirmed by TDP-43 immunohis-
tochemistry, a single proband per pedigree, and only individuals of European descent. We excluded cases
81
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
with mutations in GRN or VCP, resulting in 453 FTD-TDP cases that remained for further quality control.
Clinical data on the presence of motor neuron symptoms were recorded. The Wellcome Trust Case Control
Consortium (WTCCC) 1958 Birth Cohort was used for population controls.16
For replication purposes, we collected genomic DNA from a total of 2,218 sporadic ALS patients
and 2,261 unaffected controls from The Netherlands, Germany, Sweden and Switzerland. In addition, in
silico genotypes were obtained from Illumina beadchip data for 1,838 sporadic ALS patients and 1,697
controls from Italy.
Dutch patients were recruited by neuromuscular centers at the University Medical Center Utrecht,
the Radboud University Nijmegen Medical Center, and the Academic Medical Center Amsterdam, as part of
an ongoing population-based study of ALS in The Netherlands. Unrelated control samples without any neu-
romuscular disease were matched for age and gender. Swedish samples were included from the Umeå Uni-
versity ALS Clinic that had not yet participated in previous GWAS included in the present meta-analysis. For
the Swiss stratum, patients were recruited at Kantonsspital St. Gallen. German ALS patients were recruited
through Ulm University Hospital and Charité University Hospital, Berlin. Control samples were unrelated
individuals, free of any neuromuscular disease. Italian ALS patients were included through different Italians
ALS centers as part of the Italian SLAGEN Consortium. Controls consisted of Italian healthy individuals who
did not have a personal or family history for neurodegenerative diseases.
For all strata, patients with ALS fulfilled the revised El Escorial Criteria for possible, probable, or
definite ALS. Cases with a family history of ALS or non-Caucasian descent were excluded. As the discovery
ALS and FTD GWAS samples include individuals with C9orf72 repeat expansions (no complete data availa-
ble), we did not exclude C9orf72 repeat expansion carriers from the replication sets.
All participants gave written informed consent and approval was obtained from the local institu-
tional review boards. More detailed information on ALS or FTD subject selection methods has been pub-
lished previously and can be found in Supplementary Table 1.10,11,16
GENOTYPES AND QUALITY CONTROL
For each cohort, raw Illumina beadchip genotype data were obtained. The following quality control meas-
ures were applied to each cohort separately. We removed A/T and C/G SNPs in order to prevent allele
swaps, SNPs with a minor allele frequency (MAF) < 5%, a genotyping call rate < 95%, or with deviation
from Hardy-Weinberg equilibrium (HWE) in controls (p < 0.001). Samples with missing phenotype data,
a genotyping call rate < 95%, high (inbreeding coefficient F > 0.05) or low (F < -0.025) heterozygosity
rates, or where the clinically reported gender did not match the genotypic gender (based on chromosome
X markers), were removed. SNP identifiers and positions were mapped to dbSNP 126 and NCBI genome
build 36. For the WTCCC 2 cohort, markers and samples listed for exclusion (as provided by the WTCCC),
and samples previously included in the WTCCC 1 cohort, were removed. Cohorts consisting of control
samples only were merged with cohorts that included affected patients from the same country, ultimately
82
forming twelve balanced strata for ALS and one for FTD. Per stratum, we excluded SNPs with differing
missing rates between cases and controls (p < 1×10-3), or SNPs with differing missing rates between
flanking haplotypes (p < 1×10-10). For population stratification analysis purposes, strata were merged into
a separate dataset containing only SNPs common to all strata. Population substructure was determined
by using principal components analysis in EIGENSTRAT, also incorporating HapMap 3 release 2 samples.22
After the removal of population outliers (based on deviation from the Utah residents with ancestry from
northern and western Europe (CEU) + Toscans in Italy (TSI) population cluster in a plot of the first two
principal components), duplicate (PI_HAT value > 0.9), and related samples (PI_HAT > 0.2), new princi-
pal components were calculated. For the ALS and FTD meta-analysis, we removed duplicate and related
individuals across disease datasets. See Supplementary Table 2 for Hardy-Weinberg equilibrium (HWE) p
values and call rates for significantly associated SNPs per stratum.
For the replication of association with the chromosome 8q24.13 locus, we used KASPar (KBiosci-
ence) and TaqMan (Applied Biosystems) assays to determine rs13268726 and rs12546767 genotypes
in the replication set, according to the manufacturer’s protocols. We used an ABI Prism 7900HT analyzer
(Applied Biosystems) and SDS v2.3 software (Applied Biosystems) to determine genotype clusters, and
outliers were excluded from further analyses. For the Italian SLAGEN cohort, in silico genotypes were ob-
tained from Illumina Human-660W Quad Beadchips for rs12546767, and rs13268726 genotypes were
imputed using IMPUTE v2 and HapMap3 release 2 and 1000 Genomes pilot reference panels.
STATISTICAL ANALYSIS
Because of the use of many different genotyping platforms, and a relatively small number of markers with
genotypes in all strata, we used genome-wide SNP imputation to extend genome-wide coverage and to
increase comparability.23 Imputation was carried out using IMPUTE2 v2.1.2 in genomic chunks of 5 Mb,
leaving all options at the program’s defaults. We preferred the HapMap3r2 CEU+TSI reference (~1.4M
markers) as a reference panel, because of the relatively large number of reference haplotypes (n = 410).
We, additionally, imputed using the HapMap2 reference (120 haplotypes, ~2.5M markers), to determine
if we would not miss important associations compared to the HapMap3r2 reference. Imputation using the
HapMap2 reference did not yield additional significant results (data not shown).
Imputed genotypes were stored as continuous allele dosage data, which are continuous numerical
values indicating the estimated number of minor alleles (ranging from 0 to 2), thus incorporating a measure
of imputation uncertainty.
Associations between genotypes and disease susceptibility, were tested in logistic regression mod-
els for each of the strata in PLINK. Gender and principal components (PCs) that were strongly (p < 1×10-5)
associated with disease status were included as covariates in the logistic regression analyses (seven PCs
for ALS, two PCs for FTD). Results from each of the strata were joined in a fixed effect inverse variance
meta-analysis in PLINK, both per disease (ALS or FTD) separately, and combined.24,25 See Supplementary
83
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
Table 2 for allele and genotype frequencies per stratum for the topmost associated SNPs. We calculated
genotypic odds ratios (ORgeno) for top-associated markers in heterozygotes (1 minor allele vs. 2 major allele
carriers) and homozygotes (2 minor allele vs. 2 major allele carriers) using logistic regression, and incorpo-
rating the same covariates as were used in the main disease susceptibility analyses.
We used the GCTA software package to calculate trait heritability estimates for ALS and FTD-TDP co-
horts, as well as to estimate the cross-traits heritability, using a bivariate restricted maximum likelihood
analysis, as has been described previously.26,27 Imputed dosage data were used as input, excluding SNPs
with a poor imputation quality score (R2 < 0.3) and minor allele frequency < 0.01. The first seven principal
components calculated from a combined dataset of ALS and FTD were included as covariates. Additionally,
we performed a conditional and joint multiple-SNP analysis of the ALS and FTD meta-analysis results in
order to determine possible independent association signals within a genomic locus reaching genome-wide
significance.28 The conditional and joint analyses were carried out after converting imputed dosage data to
hard-called genotypes (as required by the GCTA software).
As an additional approach, in order to determine SNPs with shared susceptibility to both ALS and
FTD, we selected SNPs with p < 1×10-4 from the above ALS meta-analysis and used the FTD-TDP data to
replicate the associations with these SNPs. We used linkage disequilibrium (LD)-based clumping of SNPs in
PLINK in order to cluster multiple genotyped and imputed SNPs within a region of strong LD, thus determin-
ing independent loci.25 Per clump, we looked up p values of SNPs from the above logistic regression results
in the FTD analysis. We applied a Bonferroni multiple-testing correction for the number of independent loci
(clumps) tested. Subsequently, SNPs with p values below the threshold of 1×10-3 in the FTD analysis were
selected for replication in the ALS data. For the selection of SNPs from the FTD analysis, we used a less
stringent p value threshold (p < 1×10-3) in order to avoid the omission of false-negative associations due
to limited statistical power of the FTD-TDP data.
Furthermore, as sample sizes differ substantially between the joined ALS strata and the FTD-TDP
stratum, we would be almost exclusively measuring the GWAS signal from the ALS patients. We, therefore,
used a conservative rank products (RP) analysis to compare results from both ALS and FTD.29 The rank
products method originates from the analysis of multiple expression micro-array experiments, and provides
a non-parametric test that is independent of sample size. For the RP analysis, we ranked SNPs by increas-
ing p value for each disease (ALS or FTD). Only SNPs with the same direction of effect were included. For
each SNP we calculated the RP. Ultimately, SNPs were sorted by increasing rank product, and a permuta-
tion test was used to determine statistical significance for each RP. In order to obtain empirical p values, we
permuted the ranks of each SNP 100,000,000 times and counted the number of times the permuted RP
was equal to or higher than the observed RP. SNPs with an empirical p value < 5×10-8 were considered to
be significantly top-ranked for both diseases.
84
85
Manhattan plots for association results obtained from (A) meta-analysis of amyotrophic lateral sclerosis (ALS) strata, (B)
analysis of the frontotemporal dementia (FTD) stratum, (C) joint meta-analysis of both ALS and FTD strata, and (D) joint
meta-analysis of both ALS and FTD strata after the removal of 99 FTD cases with motor neuron disease (MND) symptoms.
Each dot represents a single nucleotide polymorphism; -log10 p values are shown on the y-axis, and chromosomal positi-
ons on the x-axis. Chromosomes are numbered along the x-axis and are designated by changing colors. The threshold
for genome-wide significance (p < 5 × 10-8) is indicated by a dotted line. Genome-wide significant loci, the 8q24.13 locus,
and for the FTD analysis the previously associated TMEM106B locus are highlighted.
Figure 1
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
REPLICATION OF ASSOCIATION WITH LOCUS 8q24.13
In the replication set of sporadic ALS patients and unaffected controls, for each stratum, association be-
tween SNP and disease status was tested in a logistic regression model corrected for gender. Subsequent-
ly, results were analyzed in a weighted inverse variance meta-analysis in PLINK.
RESULTS
After quality control, there were twelve ALS strata (4,377 cases, 13,017 controls), and one FTD stratum
(435 cases, 1,414 controls). For some cohorts, the total number of cases and controls differed from the
original publications, due to different quality control methods. Genomic inflation factors (λGC) ranged from
1.01 – 1.07, indicating adequate quality control and correction for population stratification (Supplemen-
tary Table 1). Associations did not reach genome-wide significance (p < 5×10-8) in any of the 13 strata
separately.
86
Figure 2
Forest plots of top-associated single nucleotide polymorphisms (SNPs) in the amyotrophic lateral sclerosis (ALS) and fronto-
temporal dementia (FTD) meta-analysis. Forest plots for top SNPs near (A) C9orf72 (rs3849943), (B) UNC13A (rs12608932),
and (C) locus chromosome 8q24.13 (rs12546767) are shown. Strata are coded as NL1, NL2, et cetera (see Supplementary
Table 1 for details on strata naming). Association test results between genotype and disease are shown for each stratum, for
each disease separately, and for the combined analysis of ALS and FTD. In addition, for rs12546767, association test results
for ALS replication strata are shown. Meta-analysis results are shown for a fixed effect model. Box sizes are relative to
stratum sample sizes. Horizontal lines indicate 95% confidence intervals.
ANALYSIS OF ALS AND FTD SEPARATELY
First, we inspected a meta-analysis of the ALS strata. Consistent with previous findings, we found
genome-wide significant hits near C9orf72 on chromosome 9p21.2 (top SNP rs3849943) and in gene
UNC13A (top SNP rs12608932) on chromosome 19p13.11 (Fig 1 and 2; Table 1).10,11
Subsequently, we analyzed the separate FTD-TDP stratum. We found no genome-wide significant associa-
tions, which was consistent with the results for patients without progranulin (GRN) mutations in the original
publication (Fig 1).16 The exact association results differed minimally from the original publication, most
probably due to the use of a partly different control cohort, and different methods for quality control and
statistical analysis.
COMBINED ALS AND FTD ANALYSIS
To examine common genetic variants that are shared in ALS and FTD-TDP we applied three complementary
methods to avoid only picking up ALS effects, considering the imbalance in cohort size. First, all strata were
joined into a single meta-analysis. Not only was the signal at 9p21.2 (C9orf72) greatly enhanced, but also
at UNC13A (Fig 1). For rs3849943 at 9p21.2, the genotypic odds ratio (ORgeno) in heterozygotes is 1.25
(p = 3.19×10-7), in homozygotes the ORgeno is 1.19 (p = 6.76×10-5); while for rs12608932 in UNC13A,
the heterozygote ORgeno and homozygote ORgeno are 1.12 (p = 0.014) and 1.29 (p = 3.33×10-15),
respectively. Results for markers previously associated with sporadic ALS or FTD-TDP can be found in
Supplementary Table 3. We did not identify any new genome-wide significant associations in the joint meta-
analysis, although one new locus nearing the genome-wide significance threshold emerged at chromo-
some 8q24.13 (Fig 1).
87
Table 1
Top association results for independent loci containing SNPs, per
disease and in the joint meta-analysis
Chr Locus n SNPs Top SNP Minor
allele
MAF ALS analysis FTD analysis Meta-analysis
OR p OR p OR p
9 9p21.2 29 rs3849943 C 0.24 1.22 5.48×10-10
1.38 5.53×10-4 1.24 2.60×10
-12
19 UNC13A 1 rs12608932 C 0.35 1.18 1.70×10-8 1.46 6.57×10
-6 1.21 1.02×10
-11
8 8q24.13 11 rs13268726 G 0.10 0.80 4.69×10-6 0.70 0.020 0.79 3.91×10
-7
17 CENPV 1 rs7477 A 0.49 1.16 2.91×10-7 0.98 0.785 1.14 1.82×10
-6
3 TXNDC6 8 rs7638688 A 0.29 0.85 1.20×10-6 0.97 0.757 0.87 2.66×10
-6
16 KIAA0513 3 rs16975170 T 0.12 1.17 2.27×10-4 1.53 5.99×10
-4 1.20 4.20×10
-6
5 FAM13B 1 rs9327807 C 0.18 0.85 1.24×10-5 0.87 0.196 0.85 5.25×10
-6
7 7p21.1 2 rs10233425 C 0.01 1.88 7.46×10-6 1.44 0.391 1.83 6.15×10
-6
10 BUB3 1 rs11248416 G 0.11 1.23 3.64×10-5 1.27 0.103 1.24 9.19×10
-6
17 17p13.2 1 rs12950017 C 0.24 1.16 1.47×10-5 1.11 0.293 1.15 9.19×10
-6
Per locus, the number of SNPs with p < 1×10-5 is indicated, and association results for the SNP with the most significant p value (top SNP) are
presented. The ALS analysis was based on 4,377 sporadic ALS patients and 13,017 controls. The FTD analysis was based on 435 sporadic FTD-TDP cases and 1,414 population controls. The meta-analysis comprises a total of 4,811 patients with either ALS or FTD, and 14,428 controls. Chr = chromosome; MAF = weighted minor allele frequency across all datasets; OR = odds ratio.
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
Using a restricted maximum likelihood analysis, we estimated trait heritability for both ALS and FTD co-
horts separately, as well as the cross-traits heritability between ALS and FTD-TDP. We estimated a SNP
heritability for ALS of 0.21 (standard error 0.02), while for FTD-TDP no reliable heritability estimate could
be calculated due to lack of statistical power. The genetic correlation between ALS and FTD-TDP was mod-
est, but significant (0.20, standard error 0.098, p = 0.012). The SNP-based coheritability was estimated
at 0.02 (standard error 0.007).
To further demonstrate shared susceptibility to both ALS and FTD-TDP for the C9orf72 and UNC13A loci, we
selected top-associated SNPs from one disease and tried to replicate their association in the other disease.
We selected 191 SNPs with p < 1×10-4 in 75 independent loci from the ALS meta-analysis, and looked up
association results for these SNPs in the FTD analysis, applying a Bonferroni multiple-testing correction for
the number of independent loci tested. We thus identified six SNPs in two loci (UNC13A and C9orf72) that
88
Table 2
Replication of top SNPs from ALS analysis in FTD data and vice versa
Top SNPs from ALS replicated in FTD
Chr Locus SNP Minor allele ALS analysis FTD analysis
OR p OR p p Bonferroni
19 UNC13A rs12608932 C 1.18 1.70×10-8 1.46 6.57×10
-6 4.93×10
-4
9 9p21.2 rs2453554 T 1.21 3.06×10-9 1.39 3.35×10
-4 0.025
9 9p21.2 rs700791 A 1.22 1.51×10-9 1.39 4.21×10
-4 0.032
9 9p21.2 rs3849942 T 1.22 9.12×10-10
1.38 5.41×10-4 0.041
9 9p21.2 rs3849943 C 1.22 5.48×10-10
1.38 5.53×10-4 0.041
9 9p21.2 rs774359 C 1.18 1.09×10-7 1.37 5.67×10
-4 0.042
Top SNPs from FTD replicated in ALS
FTD analysis ALS analysis
9 9p21.2 rs3849943 C 1.38 5.53×10-4 1.22 5.48×10
-10 3.16×10
-7
9 9p21.2 rs10967965 T 1.39 8.41×10-4 1.24 5.80×10
-10 3.34×10
-7
9 9p21.2 rs3849942 T 1.38 5.41×10-4 1.22 9.12×10
-10 5.25×10
-7
9 9p21.2 rs700791 A 1.39 4.21×10-4 1.22 1.51×10
-9 8.71×10
-7
9 9p21.2 rs17779457 G 1.36 8.35×10-4 1.21 2.93×10
-9 1.69×10
-6
9 9p21.2 rs2453554 T 1.39 3.35×10-4 1.21 3.06×10
-9 1.76×10
-6
19 UNC13A rs12608932 C 1.46 6.57×10-6 1.18 1.70×10
-8 9.77×10
-6
9 9p21.2 rs774359 C 1.37 5.67×10-4 1.18 1.09×10
-7 6.30×10
-5
Top SNPs from ALS replicated in FTD without MND signs
Chr Locus SNP Minor allele ALS analysis FTD without MND signs analysis
OR p OR p p Bonferroni
19 UNC13A rs12608932 C 1.18 1.70×10-8 1.39 4.52×10
-4 0.034
Top SNPs from FTD without MND signs replicated in ALS
FTD without MND signs analysis ALS analysis
19 UNC13A rs12608932 C 1.39 4.52×10-4 1.18 1.70×10
-8 8.63×10
-6
The upper part of the table shows SNPs with p < 1×10-4 in the ALS analysis and with p < 0.05 after Bonferroni correction for the
number of independent loci (n = 191) tested in the FTD analysis. Conversely, the second part of the table shows SNPs with
p < 1×10-3 in the FTD analysis and with p < 0.05 after Bonferroni correction for the number of independent loci (n = 658) tested in
the ALS analysis. Subsequently, SNPs are shown with p < 1×10-4 in the ALS analysis, and with p < 0.05 after Bonferroni correction
for the number of independent loci (n = 191) tested in the FTD-TDP without MND signs analysis. The lower part of the table shows
SNPs with p < 1×10-3 in the FTD-TDP without MND signs analysis and with p < 0.05 after Bonferroni correction for the number of
independent loci (n = 1,374) tested in the ALS analysis. Results are sorted by increasing Bonferroni-corrected p value. Chr = chromosome; OR = odds ratio; MND = motor neuron disease.
were significantly replicated in the FTD-TDP data (Table 2). Conversely, we selected 1,450 SNPs with p
< 1×10-3 in 658 independent loci from the FTD analysis and replicated these SNPs in the ALS data. We,
again, identified two loci (seven SNPs near C9orf72 and one in UNC13A) that were significantly replicated
in the ALS data (Table 2).
Subsequently, we used a third approach to investigate whether the associations near C9orf72 and
UNC13A were not exclusively driven by one of the disease cohorts. Because of the large sample size of
the ALS strata, the combined meta-analysis signals might be largely driven by ALS patients. To take this
into account, we used a conservative, sample size-independent, rank products (RP) analysis. This analysis
identifies SNP associations whose p values that are ranked highest in both diseases, thus allocating equal
weight to both ALS and FTD results. Supplementary Table 4 shows the top ten SNPs from this analysis,
arranged according to increasing RP. The -log10 p values of SNPs with the lowest RPs are ranked highest
in both diseases. Empirical p values for the rank products, as determined by 100,000,000 permutations,
were highly significant for the top six SNPs (p < 5×10-8). SNP rs12608932 in UNC13A had the strongest
signal in both diseases. The following nine most significant SNPs were all in the C9orf72 locus. Fig 3 shows
a more visual representation of the results from both studies; associations present in both ALS and FTD
strata clearly stand out from the disease-specific and non-significant associations.
89
Figure 3
Association results in amyotrophic lateral sclerosis (ALS) versus frontotemporal dementia (FTD). Plot shows -log10 p
values of single nucleotide polymorphism (SNP) associations in ALS (x-axis) versus -log10 p values in FTD (y-axis). SNP
rs12608932 is marked by UNC13A, and a gray-colored area designates a cluster of SNPs located within the chromo-
some 9p21.2 locus. Only SNPs with the same direction of effect have been included in the figure. Based on -log10 p
values, rs12608932 was ranked highest in both diseases. chr = chromosome.
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
Our study most likely includes a substantial number of C9orf72 repeat expansion carriers, however, we are
not able to retrieve repeat expansion status for all patients included. As a proxy for C9orf72 repeat expan-
sion status, we adjusted association tests for rs3849943 genotype (the top SNP at the 9p21.2 locus). The
association at UNC13A was not dependent on rs3849943 SNP genotype in ALS (OR 1.18, p = 8.64×10-9),
FTD (OR 1.45, p = 9.09×10-6), and the ALS-FTD meta-analysis (OR 1.21, p = 5.68×10-12). Instead, the
statistical significance of the association with UNC13A increased. Similarly, for the 8q24.13 locus (top SNP
rs13268726), association results that were corrected for rs3849943 genotype in ALS (OR 0.80, p =
6.04×10-6), FTD (OR 0.71, p = 0.029), and the ALS-FTD meta-analysis (OR 0.79, p = 6.47×10-7) were
very similar to the unadjusted results (Table 1). Furthermore, we performed a systematic, genome-wide
conditional analysis to identify possible additional, independent association signals in our meta-analysis of
ALS and FTD-TDP. The conditional analysis did not yield any additional independent association signals at
the chromosome 9p21.2 locus, gene UNC13A and chromosome 8q24.13. For each locus, association sig-
nals were driven by the SNP with the lowest p value. Adjusted, joint p values, did not differ notably from the
original p values, indeed indicating these three SNPs represent true independent signals (data not shown).
ANALYSES INCLUDING ONLY FTD-TDP CASES WITHOUT MOTOR NEURON SYMPTOMS
As noted previously, a substantial proportion of FTD-TDP patients also have motor neuron symptoms. In
order to determine if association signals from the FTD-TDP cohort were not solely driven by patients with
motor neuron symptoms, we repeated the ALS and FTD meta-analysis after removing all FTD-TDP patients
with signs of motor neuron disease (n = 99) from the FTD stratum. Of course, statistical power was attenu-
ated for the set of FTD-only patients. However, signals at C9orf72 and UNC13A were still enhanced by adding
the FTD-only cases into the meta-analysis (Fig 1). Also, the rank products analysis showed consistent
ranking of the top 6 markers (Supplementary Table 4).
NEW SIGNAL ON CHROMOSOME 8q24.13
In the combined ALS and FTD meta-analysis, there was one new signal on chromosome 8q24.13 with
top SNPs rs13268726 (imputed, OR 0.79, p = 3.91×10-7), and rs12546767 (genotyped, OR 0.80, p =
4.68×10-7) and mapping to a region with strong LD comprising genes SQLE, KIAA0196, and NSMCE2 (Fig
4). Both top SNPs are in strong LD (r2 = 0.95, D’ = 0.975). Additionally, in the analysis of FTD-TDP cases
without motor neuron symptoms, we found consistent associations for rs13268726 (OR 0.69, p = 0.029)
and rs12546767 (OR 0.67, p = 0.022) compared to the full FTD stratum (Table 3).
We followed up rs13268726 (in SQLE) and rs12546767 (in KIAA0196) in a replication set of
4,056 sporadic ALS patients and 3,958 controls. We replicated the associations between the two SNPs
in this locus and ALS susceptibility, with the lowest p value for rs12546767 (OR 0.88, p = 0.026) in gene
KIAA0196 (Fig 2). Joint analysis of both genome-wide and replication data reached a p value of 1.01×10-7
for this locus, but did not reach genome-wide significance (p < 5×10-8). Detailed results are shown in Table 3.
90
DISCUSSION
In the present study, we conducted a large genome-wide meta-analysis in a combined cohort of nearly
5,000 patients and over 14,000 controls. We aimed at finding new genetic variants that would affect
susceptibility to both sporadic ALS and TDP-43 positive FTD. With three complementary methods, to avoid
only picking up effects of the larger ALS cohort, we identified not only the known 9p.21 locus including
C9orf72, but also the UNC13A locus as shared between both neurodegenerative diseases. By combining
results from ALS and FTD datasets in a joint meta-analysis, we found a strong increase of association
signals at both loci. Furthermore, by replication of top-associated SNPs from one disease in the other and
91
Figure 4
Regional association plot for association signals at chromosome 8q24.13 in the combined amyotrophic lateral
sclerosis and frontotemporal dementia meta-analysis. The y-axis presents -log10 p values for each single nucleotide
polymorphism (SNP); the x-axis shows the chromosomal position in megabases. Dot colors indicate the amount of
linkage disequilibrium (r2) with the index SNP rs12546767 (which is the strongest associated genotyped SNP in the
locus). Both imputed and genotyped SNPs are shown. The lower panel shows gene positions; arrows indicate the
transcriptional direction.
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
vice versa, we found both the C9orf72 and UNC13A loci to be significantly associated in the ALS as well as in
the FTD analysis. We also showed that signals at both loci are not solely driven by the relatively large num-
ber of ALS patients. Furthermore, by repeating the meta-analysis selecting only FTD patients without motor
neuron disease, we demonstrated that the signals from the meta-analysis are not unique to individuals
with motor neuron symptoms in both groups. Lastly, we found a modest, but significant genetic correlation
between ALS and FTD-TDP.
The observation that the UNC13A association signal is shared between ALS and FTD-TDP co-
horts, highlights UNC13A not only as susceptibility gene in ALS, but also as a susceptibility factor for FTD-
TDP, further corroborating the role of UNC13A in neuronal degeneration. Previously we have shown that
rs12608932 acts as a modifier of survival in ALS, which was recently replicated in an Italian cohort of ALS
patients.30,31 UNC13A, therefore, poses an interesting therapeutic target. The protein encoded by UNC13A
is a member of a family of presynaptic proteins present throughout the nervous system and involved in
the priming of presynaptic vesicles containing neurotransmitters before their release.32 Aberrant function
of UNC13A disrupts the exocytosis of excitatory and inhibitory neurotransmitters. This not only affects
the biochemical communication between neurons, but also triggers structural changes in existing neu-
ronal networks.32 Furthermore, changes in the release of neurotransmitters as a consequence of defects
in UNC13A are in line with the glutamate excitotoxicity hypothesis, previously implicated in motor neuron
degeneration. Thus, our findings implicate changes in neurotransmitter release and synaptic function as a
converging mechanism in the pathogenesis of ALS and FTD.33
The highly significant signal generated by 19 SNPs at the chromosome 9p21.2 locus is most likely
linked to C9orf72 repeat expansion status. As we have C9orf72 repeat expansion status available for only a
few of the strata included in the present meta-analysis, we were not able to perform an analysis with C9orf72
wild-type carriers only to search for a residual effect of this locus in ALS-FTD. As a proxy for C9orf72 repeat
expansion status, we conditioned on rs3849943 genotype and showed that the association signal at
UNC13A increased. Moreover, it is interesting to note that C9orf72 is structurally related to DENN RabGEFs
and that Rabs cooperate with UNC13A to regulate synaptic functions.34-36 Therefore, these observations
92
Table 3
Association results for top SNPs at the chromosome 8q24.13 locus
SNP Gene Minor allele
MAF ALS analysis FTD analysis ALS + FTD meta-analysis
Replication ALS
Meta-analysis + replication combined
OR p OR p OR p OR p OR p
rs13268726 SQLE G 0.10 0.80 4.69×10-6 0.79 0.020 0.79 3.91×10
-7 0.89 0.044 0.82 1.85×10
-7
rs12546767 KIAA0196 C 0.10 0.80 6.63×10-6 0.69 0.015 0.79 4.78×10
-7 0.88 0.026 0.82 1.01×10
-7
Association results are shown for a weighted inverse-variance meta-analysis of 12 sporadic ALS cohorts (4377 ALS cases, 13017 controls), for logistic regression analysis in the FTD-TDP cohort (435 FTD-TDP cases, 1414 controls), for the combined ALS-FTD meta-analysis (4811 cases, 14428 controls), and for an independent replication cohort of sporadic ALS (4056 ALS, 3958 controls). In the righthand column, results for a combined analysis of both the ALS-FTD meta-analysis data and replication cohort are shown (8867 cases, 18386 controls). MAF = weighted minor allele frequency across all datasets; OR = odds ratio.
hint at the exciting possibility that defects in UNC13A and C9orf72 in ALS and FTD converge upon the
same synaptic mechanisms.
The present study identified one locus with increased disease association signal in the com-
bined ALS and FTD meta-analysis on chromosome 8q24.13, nearing the genome-wide significance
threshold. We successfully replicated disease association for the two top SNPs in this locus (rs13268726
and rs12546767) in an independent cohort of sporadic ALS patients. The 8q24.13 locus maps to a re-
gion of high LD encompassing SQLE, KIAA0196 and NSMCE2 (Fig 4). Of these genes KIAA0196 appears to
be of particular interest. First, mutations in KIAA0196 cause hereditary spastic paraplegia, which shares
clinical upper motor neuron dysfunction with ALS.37 Second, KIAA0196 (alias SPG8) encodes for strumpellin,
a valosin-containing protein (VCP) binding partner in the human central nervous system.38 Mutations in
VCP have previously been identified in both ALS and FTD patients.13 Third, protein aggregates containing
strumpellin have been found in patients with inclusion body myopathy associated with Paget disease of
bone and frontotemporal dementia (IBMPFD).38 Nevertheless, since the combined analysis of the discovery
and replication samples did not show a genome-wide significant association (p=1.01×10-7), further work is
needed to establish the role of KIAA0196 and other genes in the associated locus in ALS-FTD pathogenesis.
Our study is the largest GWAS including sporadic ALS and TDP-43 positive FTD patients in search
of new loci for neurodegeneration. With over 4,300 ALS patients and over 14,000 controls the study was
well powered to detect associations of common variants with modest effect size. For example, we estimat-
ed 97% power for the detection of an association similar to rs3849943 on chromosome 9p.21 at α =
5×10-8. In terms of sample sizes required for GWAS, the FTD-TDP cohort was relatively small, but unique
due to the homogenous TDP-43 pathology. The relatively small increase in statistical power obtained by
adding 435 TDP-43 positive FTD cases might provide an important explanation for why we did not identify
new shared susceptibility loci with genome-wide significance.
Also, to replicate our findings in TDP-43 positive FTD cohorts, one would require a minimum of
1,000 to 1,950 TDP-43 pathology-proven FTD cases and controls to achieve 80% power for detecting an
effect with OR 0.8 and 1.2 (with minor allele frequency 0.1 – 0.35 at α=0.05), which is clearly challeng-
ing and requires further international collaborations. In addition, future studies implementing a combined
analysis with large cohorts of patients with neurodegenerative disease including FTD, late-onset Alzheim-
er’s disease or Parkinson’s disease might add more statistical power and improve chances of finding new
shared susceptibility loci for neurodegeneration.
For the FTD-TDP cohort, clinical information was available allowing us to identify patients with and
without motor neuron signs, although we cannot definitively rule out the possibility that a small proportion
of FTD cases classified as “without motor neuron signs” still developed motor neuron disease after the last
clinical follow-up. For the ALS strata, however, data on frontotemporal dementia or cognitive impairment in
ALS patients were not available. Therefore, the extent to which signals from the meta-analysis are driven by
signs of frontotemporal dementia or cognitive impairment is not known exactly, although previous studies
93
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
have estimated a proportion of 5-10% of ALS patients also having FTD.1,2 Furthermore, previously, an as-
sociation was reported of variants in TMEM106B with susceptibility to FTD and with cognitive impairment in
ALS patients.18,19 In the present meta-analysis we did find this locus in FTD, but we did not find a significant
association of variants in TMEM106B with ALS. It is possible that an association with TMEM106B exists within
a subset of ALS patients with cognitive impairment. Careful deep phenotyping of samples in future GWAS
studies will help to shed light on the genetic determinants of motor neuron dysfunction versus cognitive
impairment.
In conclusion, our meta-analysis identifies UNC13A as a novel link between ALS and TDP-43 pos-
itive FTD, which identifies synaptic defects as a shared disease mechanism and further corroborates the
role of UNC13A and synaptic mechanisms in neuronal degeneration. Our results provide a novel starting
point for further dissection of shared pathogenic pathways underlying ALS and FTD.
94
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SUPPLEMENTARY INFORMATION
97
Supplementary Table 1
Details of included cohorts and quality control
Disease, cohorts
Pre Q
C
Q
C p
er cohort: SN
Ps
QC p
er cohort: samples
Q
C p
er stratum: S
NPs
Q
C p
er stratum: sam
ples
After Q
C
Country
Source
Platform
PAT
CO
N
SN
Ps
MAF
Call rate H
WE
Call
rate
Hetero-
zygosity
Gend
er
check
Stratum
M
issing
PAT/C
ON
Missing b
y
haplotyp
e
Dup
li-
cates
Related
Pop
.
outliers
PA
T CO
N
SN
Ps GC
ALS
Netherlands
UM
C U
trecht 1,2 Illum
ina 317K 461
450
317503
8782
2139 678
3
10
3
NL1
42 264
0
3 6
450
436
305880
1.0
13
Netherlands
UM
C U
trecht 1,2 Illum
ina 370K
583 629
37040
4
26547 8670
10
11
9 2
6
NL2
7218 6145
24 553
48
566 6631
283276 1.0
67 N
etherlands U
MC U
trecht 1,2 Illum
ina 550K
0
704
561466
53369 6818
3413
1 10
32
N
etherlands RS-I cohort, The
Rotterdam
Study
1,2 Illum
ina 550K
0
5974 561466
53815
6847 8441
0
5 3
Belgium
U
niversity Hospital
Gasthuisberg
1,2 Illum
ina 370K
312 371
37040
4
26300
9349 768
35
7 8
BE1
53 211
2
3 3
30
1 324
323549 1.0
17
France Evry
1-3 Illum
ina 317K 251
724 30
7790
8146
9010
1488
19
6 4
FR
1 3471
269
0
0
9
228 70
9 28640
0
1.015
UK
King's College
London1-3
Illumina 317K
245 221
307790
9661 22929
2491
0
4 5
U
K1
177 433
0
0
5
239 213
275439 1.0
17
UK
MN
D databank
4 Illum
ina 550K
663 0
584414
62160
13 3351
0
10
15
UK2
902
253
2 1
20
621 2743
489491 1.0
22 U
K W
TCCC2 1958 B
irth Cohort
Illumina1.2M
0
1512 9340
10
7540
8 40
5 3558
18
12 3
UK
WTC
CC2 N
ational Blood S
ervice Illum
ina1.2M
0
1270
934848
77443 463
2332
0
0
0
Ireland Beaum
ont Hospital,
Dublin
1,2 Illum
ina 550K
221 211
561466
55500
3180
930
0
2 0
IR
1 227
186
5 0
1
216 20
8 50
2065
1.026
Ireland Beaum
ont Hospital,
Dublin
1,2 Illum
ina 610K
103
127 620
901
73255
8526 778
0
0
1
IR2
15 95
3
0
0
10
0
126 519936
1.053
Sw
eden U
meå
University
Hospital 1,2
Illumina 370
K
493 50
0
37040
4
27718 8735
942
1 22
16
SW
1 16
186
4 12
24
460
454 322639
1.023
USA
MG
H &
Atlanta1-3
Illumina 317K
753
811 30
7790
7890
140
49 290
6
0
11 11
U
S1
2012
2221
149 1
19
691 682
281224 1.0
13
USA
NIH
5 Illum
ina 550K
276 271
555351
52754 6280
1474
0
4 0
U
S2
2588 985
0
1 0
249
252 5490
71 1.0
30
Italy Turin
6 Illum
ina 550K
277 263
535468
50439
1733 991
0
12 6
IT1
151 186
5
11 11
256
239 482513
1.034
Total A
LS
4638
14038
4377
130
17
FTD
Multiple
FTD collaboration
7 Illum
ina 550K,
610K
453 0
551767
51164 2683
1231
2 6
0
FTD
28652 1444
2 2
10
435 1414
447608
1.052
UK
WTC
CC1 1958 B
irth Cohort
Illumina 550
K
0
1438 555137
53469
3383 2352
0
12 6
Total FTD
453 1438
435 1414
ALS
replication
Netherlands
UM
C U
trecht KAS
Par n.a.
n.a.
n.a.
N
L3 n.a.
n.a.
634
1159 2
n.a.
Germ
any U
lm U
niversity
Hospital, C
harité
University H
ospital Berlin
KASPar
n.a.
n.a.
n.a.
DE1
n.a.
n.a.
1194 730
2
n.a.
Sw
eden U
me å
University
KASPar, TaqM
an n.a.
n.a.
n.a.
SW
2 n.a.
n.a.
192
141 2
n.a.
Sw
itzerland Kantonsspital S
t. G
allen KAS
Par, TaqMan
n.a.
n.a.
n.a.
CH1
n.a.
n.a.
198 231
2 n.a.
Italy SLAG
EN C
onsortium
Illumina 660
K, in silico
n.a.
n.a.
n.a.
IT2 n.a.
n.a.
1838
1697 2
n.a.
Total A
LS rep
lication
4056
3958
QC = quality control; PAT = patient; C
ON
= control; MAF = m
inor allele frequency; HW
E = Hardy
-Weinberg Equilibrium
; GC = genom
ic inflation factor based on logistic regression results per stratum; n.a. = not applicable.
1. van Es M
A, Van V
ught PW, B
lauw H
M, et al. ITPR
2 as a susceptibility gene in sporadic amyotrophic lateral sclerosis: a genom
e-wide association study. Lancet N
eurol 2007;6:869-877.
2. van Es M
A, Veldink JH
, Saris C
GJ, et al. G
enome-w
ide association study identifies 19p13.3 (UN
C13A) and 9p21.2 as susceptibility loci for sporadic am
yotrophic lateral sclerosis. Nat G
enet 2009;41:10
83-1087.
3. Landers JE, M
elki J, Meininger V
, et al. Reduced expression of the Kinesin-Associated Protein 3 (KIFAP3) gene increases survival in sporadic am
yotrophic lateral sclerosis. Proc N
atl Acad S
ci U S
A 20
09;10
6:9004-90
09.
4. Shatunov A, M
ok K, New
house S, et al. C
hromosom
e 9p21 in sporadic amyotrophic lateral sclerosis in the U
K and seven other countries: a geno me-w
ide association study. Lancet Neurol 20
10;9:986-994.
5. Schym
ick JC, S
cholz SW
, Fung H-C
, et al. Genom
e-wide genotyping in am
yotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet N
eurol 2007;6:322-328.
6. Chi ò
A, Schym
ick JC, R
estagno G, et al. A tw
o-stage genome-w
ide association study of sporadic amyotrophic lateral sclerosis. H
um M
ol Genet 20
09;18:1524
-1532. 7.
Van D
eerlin VM
, Sleim
an PMA, M
artinez-Lage M, et al. C
omm
on variants at 7p21 are associated with frontotem
poral lobar degeneration with TD
P-43 inclusions. Nat G
enet 2010
;42:234-239.
λ
λ
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
98
Supplem
entary Table 2
Allele and genotype frequencies, and results per stratum
for significantly associated SN
Ps
Strata are nam
ed according to supplementary table 1
. Minor allele frequencies, call rates, H
ardy-Weinberg equilibrium
calculations and genotype frequencies are based on hard-called genotypes (call threshold 0
.9) derived from genotype dosages obtained by either genom
e-wide beadchip genotyping or S
NP im
putation. For replication strata, genotype statistics are based on genotypes derived from
direct genotyping. Genotype distributions are presented as AA / AB
/ BB, w
here A represents the major allele, and B
the minor allele. The table show
s that association results using hard-called genotypes do not essentially differ from
association results using dosage data. PAT = patient; C
ON
= control; MAF = m
inor allele frequency; HW
E = Hardy-W
einberg equilibrium; O
R = odds ratio; N
A = not
available; repl = replication cohort.
IR1
0.0
7 10
0.0
0
0.612
85.2 / 14.8 / 0.0
0.10
99.52
0.70
0
81.2 / 18.4 / 0.5
0.74
0.241
0.74
0.244
IR2
0.0
9 10
0.0
0
1.000
82.0 / 18.0
/ 0.0
0.0
8 10
0.0
0
1.000
84.1 / 15.9 / 0.0
1.0
7 0.857
1.08
0.853
SW
1 0.10
98.91
0.0
69 80
.2 / 19.6 / 0.2
0.12
100.0
0
0.510
77.5 / 20
.7 / 1.8 0.85
0.314
0.84
0.284
US1
0.0
9 99.71
1.000
82.3 / 17.0 / 0
.7 0.11
99.27 0.334
79.2 / 19.2 / 1.6 0.82
0.121
0.83
0.141
US2
0.0
9 98.39
0.129
83.3 / 15.1 / 1.6 0.11
100.0
0
0.749
79.4 / 19.8 / 0.8
0.88
0.564
0.86
0.487
IT1 0.0
8 97.66
0.673
84.4 / 14.8 / 0.8
0.10
98.75
0.468
81.8 / 16.9 / 1.3 0.73
0.188
0.72
0.160
FTD
0.0
8 99.54
0.175
85.2 / 13.6 / 1.2 0.10
99.43
0.756
81.8 / 17.5 / 0.8
0.70
0.0
20
0.71
0.0
27 N
L3 (repl) 0.10
97.79
0.0
60
82.4 / 16.0 / 1.6
0.11
99.83 0.663
78.4 / 20.5 / 1.1
NA
NA
0.83
0.10
8 DE1 (repl)
0.0
9 99.75
0.60
3 82.2 / 17.1 / 0
.7 0.11
99.04
0.845
79.7 / 19.4 / 1.0
NA
NA
0.85
0.150
SW
2 (repl) 0.10
98.44
0.228
79.4 / 20.6 / 0
.0
0.12
99.29 1.0
00
77.9 / 20.7 / 1.4
NA
NA
0.85
0.539
CH
1 (repl) 0.10
10
0.0
0
0.410
81.8 / 16.7 / 1.5
0.10
98.27
0.70
3 81.1 / 18.5 / 0
.4 N
A
NA
1.02
0.939
IT2 (repl) 0.0
7 10
0.0
0
0.757
86.5 / 13.0 / 0
.5 0.0
7 10
0.0
0
0.335
85.9 / 13.4 / 0.7
NA
NA
0.95
0.288
rs12546767 (minor allele C
)
NL1
0.0
9 99.11
0.574
82.3 / 16.6 / 1.1 0.10
99.0
8 1.0
00
80.1 / 19.0
/ 0.9
0.90
0.50
8 0.91
0.539
NL2
0.0
7 99.65
1.000
85.8 / 13.7 / 0.5
0.10
99.22
0.946
80.7 / 18.3 / 1.0
0.69
1.53 ×10
-30.70
2.52 ×
10-3
BE1
0.0
8 97.34
0.240
83.3 / 16.7 / 0
.0
0.11
99.69 0.0
42 80
.2 / 17.3 / 2.5 0.79
0.223
0.77
0.189
FR1
0.0
8 99.12
0.374
83.6 / 16.4 / 0.0
0.10
98.45
0.0
94 81.5 / 16.9 / 1.6
0.70
0.20
7 0.72
0.233
UK1
0.11
99.16 0.0
53 80
.2 / 17.3 / 2.5 0.0
8 98.59
1.000
84.3 / 15.2 / 0.5
1.42 0.122
1.42 0.125
UK2
0.0
7 10
0.0
0
1.000
85.7 / 13.8 / 0.5
0.10
10
0.0
0
0.0
46 81.1 / 17.5 / 1.4
0.72
4.23×10
-30.72
4.23×10
-3
IR1
0.0
7 10
0.0
0
0.612
85.2 / 14.8 / 0.0
0.10
10
0.0
0
0.70
0
80.8 / 18.8 / 0
.5 0.72
0.211
0.72
0.211
IR2
0.0
9 10
0.0
0
1.000
82.0 / 18.0
/ 0.0
0.0
8 10
0.0
0
1.000
84.1 / 15.9 / 0.0
1.0
8 0.853
1.08
0.853
SW
1 0.10
99.35
0.0
69 79.9 / 19.9 / 0
.2 0.12
99.34 0.513
77.4 / 20.8 / 1.8
0.84
0.286
0.85
0.297
US1
0.0
9 99.71
1.000
82.3 / 17.0 / 0
.7 0.11
98.83 0.336
79.1 / 19.3 / 1.6 0.82
0.121
0.82
0.131
US2
0.0
9 10
0.0
0
0.123
83.5 / 14.9 / 1.6 0.11
100.0
0
1.000
79.8 / 19.4 / 0.8
0.86
0.487
0.86
0.487
IT1 0.0
8 10
0.0
0
0.680
84.4 / 14.8 / 0
.8 0.0
9 10
0.0
0
0.446
82.4 / 16.3 / 1.3 0.77
0.266
0.77
0.266
FTD
0.0
8 10
0.0
0
0.167
85.5 / 13.3 / 1.1 0.10
10
0.0
0
0.879
81.6 / 17.6 / 0.9
0.69
0.0
15 0.69
0.0
15 N
L3 (repl) 0.0
9 99.21
0.0
14 83.3 / 14.9 / 1.7
0.11
99.48 0.653
79.0 / 19.9 / 1.0
N
A
NA
0.82
0.0
98 DE1 (repl)
0.0
9 99.92
0.60
2 82.4 / 16.9 / 0
.7 0.10
99.45
0.844
80.0
/ 19.0 / 1.0
N
A
NA
0.86
0.172
SW
2 (repl) 0.11
98.96 0.136
77.9 / 22.1 / 0.0
0.13
99.29 1.0
00
75.7 / 22.9 / 1.4 N
A
NA
0.83
0.461
CH
1 (repl) 0.10
10
0.0
0
0.10
1 82.3 / 15.7 / 2.0
0.10
98.27
0.70
3 81.1 / 18.5 / 0
.4 N
A
NA
1.02
0.940
IT2 (repl)
0.0
7 10
0.0
0
0.70
9 87.1 / 12.4 / 0
.5 0.0
7 10
0.0
0
0.469
86.1 / 13.3 / 0.6
NA
NA
0.91
0.147
99
Stratum
PA
T (ALS
or FTD)
CO
N
Dosage
Hard
-called
MAF
Call rate
(%)
p H
WE
Genotyp
es ( %)
MAF
Call rate
(%)
p H
WE
Genotyp
es ( %)
OR
p
OR
p
rs3849943 (m
inor allele C)
NL1
0.26
100.0
0
0.178
56.0 / 36.0
/ 8.0
0.23
100.0
0
0.10
4 60
.8 / 32.6 / 6.7 1.20
0.0
94 1.20
0.0
94 N
L2 0.27
100.0
0
0.390
54.6 / 37.6 / 7.8
0.23
99.98 0.0
80
59.4 / 35.8 / 4.8 1.23
3.56×10
-31.23
3.49×10
-3
BE1
0.25
100.0
0
0.279
57.8 / 34.9 / 7.3 0.25
100.0
0
0.181
57.7 / 34.6 / 7.7 1.0
1 0.934
1.01
0.935
FR1
0.30
99.56
0.750
50
.2 / 40.5 / 9.3
0.23
99.86 0.754
58.9 / 35.5 / 5.6 1.26
0.159
1.27 0.153
UK1
0.30
10
0.0
0
0.0
30
46.0 / 48.1 / 5.9
0.20
10
0.0
0
0.398
64.3 / 30.5 / 5.2
1.70
1.42×10
-31.70
1.41×
10-3
UK2
0.30
10
0.0
0
0.850
48.3 / 42.7 / 9.0
0.24
100.0
0
0.30
1 56.7 / 37.7 / 5.6
1.36 1.80 ×
10-5
1.36 1.77 ×
10-5
IR1
0.24
100.0
0
0.855
56.9 / 37.5 / 5.6 0.20
10
0.0
0
0.275
65.4 / 29.3 / 5.3 1.27
0.144
1.27 0.146
IR2
0.23
100.0
0
0.0
90
56.0 / 42.0
/ 2.0
0.27
100.0
0
1.000
54.0 / 38.9 / 7.1
0.78
0.30
4 0.78
0.30
6 SW
1 0.26
100.0
0
0.186
52.8 / 41.5 / 5.7 0.19
100.0
0
0.878
65.9 / 30.8 / 3.3
1.58 2.0
7×10
-41.58
2.06×
10-4
US1
0.24
100.0
0
1.000
57.2 / 36.8 / 5.9 0.24
100.0
0
0.599
57.5 / 37.2 / 5.3 1.0
3 0.739
1.03
0.756
US2
0.28
100.0
0
0.635
51.8 / 41.4 / 6.8 0.23
100.0
0
0.719
60.3 / 34.1 / 5.6
1.30
0.0
79 1.31
0.0
79 IT1
0.27
99.61 0.637
53.7 / 38.4 / 7.8 0.28
99.58 0.522
52.5 / 38.7 / 8.8 0.90
0.491
0.90
0.495
FTD
0.31
99.77 0.0
58 44.9 / 47.2 / 7.8
0.25
100.0
0
0.315
56.3 / 38.1 / 5.5 1.38
5.53 ×10
-41.38
5.93 ×10
-4
rs12608932 (m
inor allele C)
NL1
0.39
100.0
0
0.325
37.8 / 45.6 / 16.7 0.38
100.0
0
0.360
37.8 / 49.1 / 13.1
1.08
0.417
1.08
0.417
NL2
0.41
100.0
0
0.60
2 35.9 / 47.2 / 17.0
0.34
100.0
0
0.478
43.4 / 44.6 / 11.9 1.31
2.02 ×
10-5
1.31 2.0
2 ×10
-5
BE1
0.43
100.0
0
0.559
32.9 / 47.5 / 19.6 0.37
100.0
0
0.0
03
43.8 / 38.6 / 17.6 1.31
0.0
17 1.31
0.0
17 FR
1 0.36
100.0
0
0.474
41.7 / 43.9 / 14.5 0.32
100.0
0
0.932
45.8 / 43.6 / 10.6
1.11 0.50
7 1.11
0.50
7 U
K1 0.40
10
0.0
0
0.0
45 38.9 / 41.8 / 19.2
0.34
100.0
0
0.545
44.1 / 43.2 / 12.7 1.27
0.0
76 1.27
0.0
76 U
K2 0.37
100.0
0
0.391
38.8 / 48.3 / 12.9 0.35
100.0
0
0.70
7 42.1 / 45.3 / 12.6
1.08
0.261
1.08
0.261
IR1
0.36
100.0
0
0.10
4 44.0
/ 40.7 / 15.3
0.34
100.0
0
0.537
44.7 / 42.8 / 12.5 1.0
9 0.536
1.09
0.536
IR2
0.33
100.0
0
0.261
48.0 / 39.0
/ 13.0
0.37
100.0
0
0.186
42.1 / 41.3 / 16.7 0.81
0.299
0.81
0.299
SW
1 0.39
100.0
0
0.770
36.5 / 48.5 / 15.0
0.35
100.0
0
0.60
4 43.4 / 44.1 / 12.6
1.22 0.0
46 1.23
0.0
46 U
S1
0.35
100.0
0
0.279
42.8 / 43.8 / 13.5 0.31
100.0
0
0.719
47.5 / 43.4 / 9.1 1.23
0.0
11 1.24
0.0
10
US2
0.36
100.0
0
0.0
00
46.6 / 34.1 / 19.3 0.31
100.0
0
0.0
77 44.8 / 48.0
/ 7.1 1.26
0.0
78 1.26
0.0
78 IT1
0.34
100.0
0
0.20
8 45.7 / 41.0
/ 13.3 0.32
100.0
0
0.143
43.5 / 48.1 / 8.4 1.0
0
0.973
1.01
0.973
FTD
0.43
100.0
0
0.0
05
35.4 / 42.3 / 22.3 0.35
100.0
0
0.10
1 41.6 / 47.3 / 11.1
1.46 6.57 ×
10-6
1.46 6.40 ×
10-6
rs13268726 (m
inor allele G)
NL1
0.0
9 98.89
0.40
4 82.7 / 16.2 / 1.1
0.10
99.77
1.000
80.0
/ 19.1 / 0.9
0.88
0.450
0.88
0.431
NL2
0.0
7 99.47
1.000
86.1 / 13.3 / 0.5
0.10
99.0
5 0.838
80.9 / 18.1 / 1.0
0.68
1.33 ×10
-30.69
1.81 ×10
-3
BE1
0.0
8 99.67
0.238
83.7 / 16.3 / 0.0
0.11
98.46 0.0
40
80.3 / 17.2 / 2.5
0.75
0.142
0.75
0.142
FR1
0.0
8 98.25
0.374
83.5 / 16.5 / 0.0
0.10
98.17
0.125
82.2 / 16.4 / 1.4 0.74
0.271
0.76
0.328
UK1
0.11
97.91 0.0
32 81.2 / 16.2 / 2.6
0.0
8 99.53
1.000
84.4 / 15.1 / 0.5
1.42 0.127
1.37 0.175
UK2
0.0
8 99.68
1.000
85.5 / 14.1 / 0.5
0.10
99.56
0.0
75 81.0
/ 17.7 / 1.4 0.72
4.12×10
-30.73
5.74×10
-3
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
100
Supplem
entary Table 3
Association results for SN
Ps previously associated w
ith ALS
or FTD
Chr
Locus SN
P Previously associated
with
Minor allele
ALS
analysis FTD
analysis M
eta-analysis
OR
p
OR
p
OR
p
12 ITP
R2
rs230
6677 ALS
A
1.11 0.0
26 0.77
0.0
75 1.0
7 0.12
1 FG
GY
rs6700125
ALS
T
1.06
0.0
67 1.0
7 0.41
1.06
0.0
45 7
DP
P6
rs10260
404
ALS
C
1.08
0.0
12 1.0
4 0.67
1.07
0.0
12 9
9p21.2 rs281470
7 ALS
T
1.22 1.77×
10-9
1.34 1.77×
10-3
1.23 1.89×
10-11
9 9p21.2
rs3849942 ALS
T
1.22 9.12 ×
10-10
1.38 5.41×
10-4
1.24 4.40×
10-12
9 9p21.2
rs90360
3 ALS
A
0.89
2.56×10
-5 0.89
0.18
0.89
1.02×
10-5
19 U
NC
13A
rs12608932
ALS
C
1.18 1.70×
10-8
1.46 6.57×
10-6
1.21 1.0
2×10
-11 7
TME
M10
6B
rs1006869
FTD
G
1.01
0.88
0.78
0.0
25 0.98
0.59
7 TM
EM
106B
rs1990
602
FTD
C
1.03
0.49
0.80
0.0
61 1.0
0
0.94
7 TM
EM
106B
rs10
226395 FTD
C
1.02
0.61
0.84
0.12
1.00
0.99
7 TM
EM
106B
rs10
03433
FTD
G
1.00
0.96
0.77
4.40×10
-3 0.98
0.40
7 TM
EM
106B
rs6952272
FTD
T 1.0
2 0.58
0.76
0.0
1356 0.99
0.81
7 TM
EM
106B
rs12671332
FTD
C
1.02
0.57
0.72
1.90 ×10
-3 0.99
0.67
7 TM
EM
106B
rs1468915
FTD
G
1.01
0.68
0.71
1.16×10
-3 0.98
0.55
7 TM
EM
106B
rs10
20004
FTD
C
1.00
0.91
0.68
3.46 ×10
-5 0.96
0.16
7 TM
EM
106B
rs6966915
FTD
T 0.97
0.25
0.68
7.27×10
-6 0.93
0.0
12
7 TM
EM
106B
rs10
488192 FTD
A
1.02
0.55
0.71
2.04×
10-3
0.99
0.72
7 TM
EM
106B
rs1990
622 FTD
G
0.97
0.26
0.68
5.93×10
-6 0.93
0.0
13
7 TM
EM
106B
rs694590
2 FTD
A
0.95
0.11
0.78
0.0
151 0.93
0.0
22
Results are show
n per disease, and for the combined A
LS and FTD
meta-analysis. The A
LS analysis is based on 4,377 sporadic A
LS patients and 13,0
17 controls. The FTD analysis is based on 435
sporadic FTD-TD
P cases and 1,414 population controls. The meta-analysis com
prises a total of 4,811 patients with either ALS
or FTD, and 14,428 controls. C
hr, = chromosom
e; CO
N = controls; M
AF = minor
allele frequency; OR = odds ratio.
101
Supplem
entary Table 4
Results for rank products analysis of A
LS and FTD
ALS
vs. FTD
Chr
SN
P N
earest gene M
inor allele G
enotyped
Association in A
LS
Association in FTD
Rank p
roducts results
OR (p
) Rank
OR (p
) Rank
RP
p
19 rs1260
8932 U
NC
13A
C
+ 1.18 (1.70×
10-8)
11 1.46 (6.57×
10-6)
15 12.85
<1×10
-8 9
rs3849943 C
9orf72
C
- 1.22 (5.48 ×
10-10)
1 1.38 (5.53×
10-4)
843 29.0
3 <1×
10-8
9 rs10
967965 M
OB
KL2
B
T -
1.24 (5.80×10
-10) 2
1.39 (8.41×10
-4) 1245
49.90
5.0×10
-8 9
rs3849942 C
9orf72
T
+ 1.22 (9.12×
10-10)
3 1.38 (5.41×
10-4)
834 50
.02
5.0×10
-8 9
rs700791
C9orf7
2
A
- 1.22 (1.51 ×
10-9)
4 1.39 (4.21×
10-4)
681 52.19
3.0×10
-8 9
rs2453554 C
9orf72
T
- 1.21 (3.0
6×10
-9) 7
1.39 (3.35×10
-4) 542
61.60
3.0×10
-8 9
rs17779457 M
OB
KL2
B
G
- 1.21 (2.93 ×
10-9)
6 1.36 (8.35×
10-4)
1226 85.77
1.0×10
-7 9
rs774359 C
9orf72
C
+ 1.18 (1.0
9×10
-7) 12
1.37 (5.67×10
-4) 859
101.53
1.0×10
-7 9
rs2814707
MO
BK
L2B
T
+ 1.22 (1.77 ×
10-9)
5 1.34 (1.77×
10-3)
2496 111.71
1.5×10
-7 9
rs895021
MO
BK
L2B
A
- 1.21 (5.26 ×
10-9)
10
1.35 (1.06×
10-3)
1537 123.98
1.3×10
-7
ALS
vs. FTD w
ithout motor neuron sym
ptom
s
Chr
SN
P N
earest gene M
inor allele G
enotyped
Association in A
LS
Association in FTD
without M
ND
signs Rank p
roducts results
OR (p
) Rank
OR (p
) Rank
RP
p
19 rs1260
8932 U
NC
13A
C
+ 1.18 (1.70×
10-8)
11 1.39 (4.52×
10-4)
643 84.1
8.00×
10-8
9 rs3849943
C9orf7
2
C
- 1.22 (5.48×
10-10)
1 1.23 (0
.042)
55629 235.8
7.20×10
-7
9 rs10
967965 M
OB
KL2
B
T -
1.24 (5.80×10
-10) 2
1.24 (0.0
50)
66299 364.1
1.61×10
-6
9 rs3849942
C9orf7
2
T +
1.22 (9.12×10
-10) 3
1.23 (0.0
40)
54196 40
3.1 1.90×
10-6
9 rs70
0791
C9orf7
2
A
- 1.22 (1.51×
10-9)
4 1.25 (0
.032)
43501
417.0
2.07×
10-6
9 rs2453554
C9orf7
2
T -
1.21 (3.06×
10-9)
7 1.26 (0
.026)
34651 492.3
2.92×10
-6
17 rs80
70348
CD
RT7
G
-
1.35 (0.0
60)
78682 6.27 (8.80×
10-6)
3 479.4
2.99×10
-6
4 rs6853653
SH
RO
OM
3 C
+ 0.92 (0
.019)
25693 0.61 (1.55×
10-5)
13 570
.4 4.29×
10-6
9 rs17779457
MO
BK
L2B
G
-
1.21 (2.93×10
-9) 6
1.22 (0.0
49) 65343
626.0
4.91×10
-6
9 rs774359
C9orf7
2
C
+ 1.18 (1.0
9×10
-7) 12
1.25 (0.0
28) 37372
669.4 5.30×
10-6
The upper section of the table shows results for the rank products analysis in A
LS and FTD
strata, while the low
er part of the table shows results in A
LS strata and in the FTD
stratum after rem
oval of 99 FTD
cases with m
otor neuron symptom
s. Results are sorted by increasing rank product. Em
pirical p values are estim
ated for each rank product after 100,0
00,0
00 perm
utations. SN
Ps with an em
pirical p
value < 5×10
-8 were considered to be significantly top-ranked for both diseases. For each S
NP is indicated w
hether genotypes were obtained by direct genotyping (+) or by im
putation (-). Chr, =
chromosom
e; OR = odds ratio; M
ND = m
otor neuron disease; RP = rank product.
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
6
THE INTERNATIONAL COLLABORATION FOR FRONTOTEMPORAL LOBAR DEGENERATION
Vivianna M Van Deerlin, Patrick M A Sleiman, Maria Martinez-Lage, Alice Chen-Plotkin, Li-San Wang, Neill R
Graff-Radford, Dennis W Dickson, Rosa Rademakers, Bradley F Boeve, Murray Grossman, Steven E Arnold,
David M A Mann, Stuart M Pickering-Brown, Harro Seelaar, Peter Heutink, John C van Swieten, Jill R Murrell,
Bernardino Ghetti, Salvatore Spina, Jordan Grafman, John Hodges, Maria Grazia Spillantini, Sid Gilman,
Andrew P Lieberman, Jeffrey A Kaye, Randall L Woltjer, Eileen H Bigio, Marsel Mesulam, Safa al-Sarraj,
Claire Troakes, Roger N Rosenberg, Charles L White III, Isidro Ferrer, Albert Lladó, Manuela Neumann, Hans
A Kretzschmar, Christine Marie Hulette, Kathleen A Welsh-Bohmer, Bruce L Miller, Ainhoa Alzualde, Adolfo
Lopez de Munain, Ann C McKee, Marla Gearing, Allan I Levey, James J Lah, John Hardy, Jonathan D Rohrer,
Tammaryn Lashley, Ian R A Mackenzie, Howard H Feldman, Ronald L Hamilton, Steven T Dekosky, Julie van
der Zee, Samir Kumar-Singh, Christine Van Broeckhoven, Richard Mayeux, Jean Paul G Vonsattel, Juan C
Troncoso, Jillian J Kril, John B J Kwok, Glenda M Halliday, Thomas D Bird, Paul G Ince, Pamela J Shaw, Nigel J
Cairns, John C Morris, Catriona Ann McLean, Charles DeCarli, William G Ellis, Stefanie H Freeman, Matthew
P Frosch, John H Growdon, Daniel P Perl, Mary Sano, David A Bennett, Julie A Schneider, Thomas G Beach,
Eric M Reiman, Bryan K Woodruff, Jeffrey Cummings, Harry V Vinters, Carol A Miller, Helena C Chui, Irina
Alafuzoff, Päivi Hartikainen, Danielle Seilhean, Douglas Galasko, Eliezer Masliah, Carl W Cotman, M Teresa
Tuñón, M Cristina Caballero Martínez, David G Munoz, Steven L Carroll, Daniel Marson, Peter F Riederer,
Nenad Bogdanovic, Gerard D Schellenberg, Hakon Hakonarson, John Q Trojanowski, Virginia M-Y Lee.
THE SLAGEN CONSORTIUM
Isabella Fogh, Antonia Ratti, Cinzia Gellera, Kuang Lin, Cinzia Tiloca, Valentina Moskvina, Lucia
Corrado, Gianni Sorarù, Cristina Cereda, Stefania Corti, Davide Gentilini, Daniela Calini, Barbara
Castellotti, Letizia Mazzini, Giorgia Querin, Stella Gagliardi, Roberto Del Bo, Francesca Luisa
Conforti, Cosenza, Gabriele Siciliano, Maurizio Inghilleri, Francesco Saccà, Paolo Bongioanni,
Silvana Penco, Massimo Corbo, Sandro Sorbi, Massimiliano Filosto, Alessandra Ferlini, Anna
Maria Di Blasio, Stefano Signorini, Nicola Ticozzi, Mauro Ceroni, Elena Pegoraro, Giacomo P
Comi, Sandra D’Alfonso, Franco Taroni, Ammar Al-Chalabi, John Powell and Vincenzo Silani.
102
Supplementary Table 5
Contributors List
C9ORF72 AND UNC13A ARE SHARED RISK LOCI FOR ALS AND FTD: A GENOME-WIDE META-ANALYSIS
103
6
PART III
Genetic disease
modifiers
* These authors contributed equally to the manuscript
7
UNC13A IS A MODIFIER OF SURVIVAL IN
AMYOTROPHIC LATERAL SCLEROSIS
NEUROBIOL AGING. 2012;33(3):630.E3-8
Frank P Diekstra, Paul WJ van Vught, Wouter van Rheenen, Max Koppers,
R Jeroen Pasterkamp, Michael A van Es, H Jurgen Schelhaas,
Marianne de Visser, Wim Robberecht, Philip Van Damme,
Peter M Andersen, Leonard H van den Berg*, Jan H Veldink*
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a fatal adult-onset neurodegenerative disorder characterized by
progressive muscle weakness due to the loss of upper and lower motor neurons. No cure is available for
ALS and the underlying pathogenesis remains largely elusive. Sporadic ALS is attributed to a combination
of genetic and environmental risk factors. Recently, a twin study of sporadic ALS patients has estimated
hereditability to be considerable (0.38-0.76), indicating an important genetic component in disease etiol-
ogy.1 Multiple genome-wide association studies (GWAS) have been performed in ALS and to date, several
loci have been shown to be associated, including UNC13A and a locus on chromosome 9p21.2.2-8 The
association with UNC13A was found in a two-stage GWAS comprising 4,855 ALS patients and 14,953
unaffected controls (rs12608932, p = 2.50 × 10-14, for the combined analysis of two stages).8 Replication
of these results has proven difficult because of very small effect sizes.5, 9
In ALS, where etiology is largely unknown, risk factors that, in addition, possess disease-modifying
properties provide promising therapeutic targets.10 These risk factors can best be studied in a popula-
tion-based cohort of incident ALS patients to reduce referral bias and overrepresentation of patients with
better prognosis (spinal onset, young age) as found in cohorts selected from tertiary care institutions or
with prevalent cases only.11-14
In the present study we examined whether UNC13A has disease-modifying properties, including
association with age at onset and with survival, possibly further corroborating its role in ALS. For this pur-
pose, we recruited an independent, population-based cohort of incident Dutch ALS patients and controls.
Subsequently, survival data were collected for cohorts from the previous GWAS and tested for association
with ALS.
108
ABSTRACT
A large genome-wide screen in patients with sporadic amyotrophic lateral sclerosis (ALS) showed
that the common variant rs12608932 in gene UNC13A was associated with disease susceptibility.
UNC13A regulates the release of neurotransmitters, including glutamate. Genetic risk factors that,
in addition, modify survival, provide promising therapeutic targets in ALS, a disease whose etiology
remains largely elusive. We examined whether UNC13A was associated with survival of ALS patients
in a cohort of 450 sporadic ALS patients and 524 unaffected controls from a population-based
study of ALS in The Netherlands. Additionally, survival data were collected from individuals of Dutch,
Belgian or Swedish descent (1,767 cases, 1,817 controls), who had participated in a previously
published genome-wide association study of ALS. We related survival to rs12608932 genotype. In
both cohorts, the minor allele of rs12608932 in UNC13A was not only associated with susceptibility,
but also with shorter survival of ALS patients. Our results further corroborate the role of UNC13A in
ALS pathogenesis.
METHODS
POPULATION-BASED COHORT SUBJECTS
Patient characteristics are outlined in Table 1. For the population-based cohort, patients were included
from the neuromuscular centers of the University Medical Center Utrecht, the Academic Medical Center
Amsterdam, and the Radboud University Nijmegen Medical Center as part of an ongoing population-based
study of ALS in The Netherlands.15 This study is performed in the Netherlands (41,528 km2, population
16,455,911 people). For the present study, incident ALS cases were identified from January 1, 2006
to December 31, 2009. Prevalent cases were all cases diagnosed before December 31, 2008 and still
alive at that date. To ensure complete case ascertainment multiple sources were used. In addition to the
University Medical Centers (UMC) cooperating in the Netherlands ALS Center (Amsterdam, Utrecht and
Nijmegen), all remaining UMCs not participating in the Netherlands ALS Center and the 30 largest of the
83 general hospitals were visited each year to screen their registers for ALS patients. After diagnosis has
been made, patients in the Netherlands are referred to one of the 46 rehabilitation centers specialized
in the care of ALS. All centers were visited every year to scrutinize their registers for ALS patients. Lastly,
patients were recruited by the Dutch Patient Advocacy Group for Neuromuscular Diseases. Patients who
had not participated in previous GWASs were selected (n = 450). All patients fulfilled the El Escorial criteria
for possible, probable or definite ALS.16 Cases with a family history of ALS or non-Caucasian descent were
excluded. Control samples were recruited in the population-based study through the general practitioner of
the participating patient. The Dutch health care system ensures that all people are registered with a general
practitioner. The general practitioner was asked to send information about our study to five people following
the patient in the alphabetized register, matched for gender and age plus or minus five years. We included
524 controls whose DNA was available for analysis.
GWAS COHORT SUBJECTS
In addition, we collected data on survival and age at onset of sporadic ALS patients and unaffected indi-
viduals from a previously published GWAS.8 These data were available for two Dutch cohorts, one Belgian
and one Swedish cohort.
UNC13A IS A MODIFIER OF SURVIVAL IN AMYOTROPHIC LATERAL SCLEROSIS
109
7
Table 1
Patient characteristics
Cohort Country
ALS patients Controls
n Sex, F
Mean age at onset, yr (range)
Site of onset, bulbar n Sex, F
Mean age, yr (range)
Population-based The Netherlands 450 39.6%� 61.3 (16-88) 35.5% 524 43.5%� 63.4 (21-92) GWAS The Netherlands 1012 40.5%� 60.7 (20-88) 32.3%� 1038 41.3%� 62.2 (21-92) Belgium 299 40.1%� 58.2 (18-85) 25.9%� 323 55.7%� 63.2 (6-88) Sweden 458 41.7%� 61.2 (20-87) 40.1%� 456 45.6%� 59.7 (20-94) Total GWAS 1769 40.8%� 60.3 (18-88) 32.7%� 1817 45.0%� 61.7 (6-94)
In the population-based cohort there was missing data on either age at onset or site of onset in 5 patients, while in the GWAS cohort there were 224 patients and 1 control with missing data on either age at onset or site of onset. ALS: amyotrophic lateral sclerosis; GWAS: genome-wide association study; F: female.
Patients in the Dutch cohorts were included through the neuromuscular centers of the University Medical
Center Utrecht, the Academic Medical Center Amsterdam, or the Radboud University Nijmegen Medical
Center. Approximately 50% of the Dutch patients were included through the above-described ongoing pop-
ulation-based study of ALS in The Netherlands. ALS patients had been screened for superoxide dismutase
1 (SOD1) and angiogenin (ANG) gene mutations, and only patients without mutations in these genes were
included. All four grandparents of patients were originating from The Netherlands. Control subjects were
unrelated volunteers who were spouses of patients or who accompanied patients to the general neurology
outpatient clinic. For patients participating in the ongoing population-based study, controls were collected
through the general practitioner. Only controls with no medical history of neurological disorders were in-
cluded and they were matched to patients for age and sex.
For the Belgian cohort, patients that had been referred to the University Hospital Gasthuisberg,
Leuven were included. Patients reported to be of Flemish descent for at least three generations. The Bel-
gian controls included unrelated, healthy Flemish individuals who had married into families that participated
in genetic studies of other neurological disorders.
Sporadic ALS patients from Sweden were referred to the Umeå University ALS Clinic and had
self-reported Swedish descent for at least three generations. Control samples in the Swedish cohort were
spouses of ALS patients or age and gender-matched, unrelated, healthy controls recruited through the
neurological outpatient clinic.
For all cohorts, the diagnosis of probable or definite ALS was made according to the 1994 El Es-
corial criteria16, by specialized neuromuscular neurologists. Patients with a positive family history for ALS
were excluded.
GENOTYPING
Genotyping of the rs12608932 SNP in the independent population-based cohort was carried out by use
of capillary sequencing. Detailed sequencing methods are available in the Supplementary Data.
For patients in the GWAS cohort, genotypes were extracted from Illumina HumanHap 300K and
HumanCNV 370K SNP chip data for rs12608932 only, since none of the other SNPs were in linkage
disequilibrium (LD) with this variant. Quality control was applied as described previously.8 Concordance be-
tween direct sequencing and SNP chip genotyping techniques was determined by additionally sequencing
61 randomly selected samples that had been used as control subjects in the previous GWAS.8
STATISTICAL METHODS
Survival analyses were carried out using Cox regression models, adjusted for gender, age at onset and site
of onset (bulbar or spinal). Additionally, in the analysis of the GWAS cohorts, we included a dummy-coded
country variable to adjust for possible heterogeneity between countries. Duration of survival was defined
as the interval between the age at first symptoms (limb muscle weakness or difficulties with swallowing/
110
speech) and age at death or tracheostomy. We tested the following genetic models; additive (AA vs. AB vs.
BB genotypes), dominant (AA vs. AB + BB) and recessive (AA + AB vs. BB), where A represents the major
allele and B the minor. Cox regression models were tested for non-proportional hazards using a χ2 test for
correlation between the scaled Schoenfeld residuals of each covariate and survival time. Since survival
data showed proportional hazards (p > 0.05), results were derived from Cox regression, and there was no
need for using a Peto-Prentice Generalized Wilcoxon test, which would weigh earlier events more heavily.
ANOVA was used to determine which genetic model was best to fit the data (additive, dominant or reces-
sive). Kaplan-Meier survival curves were estimated for rs12608932 genotypes according to a recessive
model. Survival analyses were carried out using the survival package for R statistical software v2.10 (R
Foundation for Statistical Computing, Vienna, Austria) and SPSS v15.0 (SPSS Inc, Chicago, IL).
For association with disease susceptibility we performed logistic regression in PLINK v1.07.17 In
the population-based cohort the logistic model was adjusted for gender. For the GWAS cohort gender,
dummy-coded nationality and ancestry (defined by the first two dimensions of a multidimensional scaling
analysis of genome-wide data) were included as covariates. The covariates used in this model were similar
to those in the original GWAS.8
Tests for deviation from Hardy-Weinberg equilibrium in controls were performed in PLINK using
the program’s default exact test.18
RESULTS
DISEASE SUSCEPTIBILITY
The genotyping rate in the population-based cohort was 92% in both cases and controls. After quali-
ty control, the overall genotyping rate for rs12608932 in the GWAS cohort was 99.9%. Comparison of
rs12608932 genotypes obtained by direct sequencing and from Illumina SNP chip data in 61 control
samples yielded a 100% genotype concordance rate.
UNC13A IS A MODIFIER OF SURVIVAL IN AMYOTROPHIC LATERAL SCLEROSIS
111
Table 2
Results for survival and disease susceptibility analyses
Cohort Country
Genotyped, n MAF Survival Susceptibility
ALS CON ALS CON Mortality HR (95% CI) OR (95%�CI)
Population-based The Netherlands 412 481 0.41 0.36 47.7% 1.62 (1.16-2.26)** 1.91 (1.31-2.79)** GWAS The Netherlands 1011 1038 0.40 0.36 66.3%� 1.18 (0.97-1.44) 1.46 (1.14-1.87)** Belgium 298 323 0.43 0.37 83.1%� 1.22 (0.89-1.67) 1.20 (0.79-1.81) Sweden 458 456 0.39 0.35 100%� 1.46 (1.00-2.12)* 1.22 (0.83-1.79) Total GWAS 1767 1817 0.40 0.36 74.5% 1.23 (1.06-1.43)* 1.33 (1.10-1.60)**
In the survival analysis 5 cases were excluded from the population-based cohort and 263 cases from the GWAS cohort due to missing covariate data. Results are shown for analyses under a recessive model. GWAS: genome-wide association study; MAF: minor allele frequency; ALS: amyotrophic lateral sclerosis patients; CON: control subjects; HR: hazard ratio; CI: confidence interval; OR: odds ratio; * = p < 0.05; ** = p < 0.005.
7
ALS susceptibility association test results are presented in Table 2. In both population-based and GWAS
cohorts, there was a significant association with rs12608932 (p = 0.001 and p = 0.002, respectively).
The minor allele in ALS patients showed a slightly, but non-significantly, higher frequency in the popula-
tion-based cohort compared to the GWAS cohort (0.41 vs. 0.40; p = 0.66, Pearson χ2), while among
controls, frequencies were equal between cohorts (p = 1, Pearson χ2). In both cohorts, rs12608932 was in
Hardy-Weinberg equilibrium (population-based cohort controls p = 0.11, GWAS cohort controls p = 0.36).
SURVIVAL
Survival analyses results are shown in Table 2. Association with survival was tested in 412 patients in the
population-based cohort. We found association with survival for both additive (p = 0.01, hazard ratio (HR)
= 1.28) and recessive genetic models. ANOVA analyses showed that association between rs12608932
and survival fitted the recessive model best (p < 0.001). Figure 1 shows Kaplan-Meier survival curves
for the population-based cohort according to rs12608932 genotype status in a recessive model. Sub-
sequently, we collected survival data for the GWAS cohort and tested for an effect of UNC13A on survival.
Data on survival were available for 1,504 ALS patients in the GWAS cohort. Again, patients homozygous for
the minor allele of rs12608932 had significantly shorter survival compared to the other genotype groups
(recessive model, p = 0.01, HR 1.23). The difference in median survival between genotypic groups was
10.0 months in the population-based cohort and 5.0 months in the GWAS cohort.
112
Figure 1
Kaplan-Meier curves for rs12608932 genotypes according to a recessive genetic model in
the population-based cohort. The black curve is for AA or AC genotypes, the grey curve is
for the CC genotype. C is the minor allele. The curves are adjusted for the covariates used
in the survival analysis.
AGE AT ONSET
There was no significant association with age at onset in either of the tested cohorts for any of the tested
genetic models. Results for these analyses are reported in Suppl. Table 1.
DISCUSSION
In the present study, we report the association of rs12608932 in UNC13A with shorter survival of spo-
radic ALS patients in an independent, population-based incident Dutch cohort. The effect on survival was
also present in patients from our previously published GWAS for whom survival data were available. This
indicates that UNC13A might act as a disease-modifying gene, further corroborating its role in ALS patho-
genesis. Furthermore, the minor allele of rs12608932 was associated consistently with susceptibility to
ALS in the independent, population-based cohort. Two genetic variants are in LD with rs12608932 (r2 >
0.5, CEU 1000 genomes pilot 1).19 All of these variants map to UNC13A, strongly implicating this gene in
ALS susceptibility and survival.
The UNC13A gene encodes protein unc-13 homolog A that is part of a family of presynaptic pro-
teins in the brain. The UNC13A protein is involved in the regulation of neurotransmitter release at synapses,
including at neuromuscular junctions. Neurotransmitters including glutamate are released by exocytotic
fusion of presynaptic vesicles, a process triggered by membrane depolarization and concomitant influx of
Ca2+ ions.20 Prior to fusion with the presynaptic membrane, vesicles are recruited to the membrane and
primed for exocytosis, which forms an important regulatory step in neurotransmitter release.21 Disruption
of this priming process by altered function of UNC13A could lead to changes in neurotransmitter release
and might, ultimately, lead to death of motor neurons.22 Since UNC13A can directly regulate glutamate
release, this mechanism provides support for the glutamate excitotoxicity hypothesis in ALS.23 Notably, the
only drug with proven effect on survival in ALS is riluzole, a glutamate release inhibitor.24
In the present study, we found a higher minor allele frequency (MAF) in cases (0.41) than was
found in previously published populations from France (0.34) and the UK (0.37).5, 9 In control subjects,
the MAF was comparable to other studies (0.34 - 0.36).5, 8, 9 The higher minor allele frequency might be
explained by the following. Previous studies, including studies of KIFAP3 as a modifier of survival, have
shown that estimations of determinants of disease characteristics such as survival are strongly dependent
on patient selection.11, 13, 14, 25 These studies demonstrated greater proportions of short survivors in incident,
population-based cohorts than in prevalent cohorts. In the present study, the rs12608932 minor allele was
associated with shorter survival. Given this association, MAF may be strongly dependent on patient selec-
tion criteria, leading to higher frequencies in patients in incident, population-based cohorts, which include
ALS patients with shorter median survival times. Patients in the independent population-based cohort in
the present study, and to a certain extent in the GWAS cohort, were selected from a population-based study
of ALS in The Netherlands, which most probably explains the relatively high MAF of rs12608932 in our co-
horts. Conversely, the use of referral-based or prevalent cases would imply a lower proportion of carriers of
UNC13A IS A MODIFIER OF SURVIVAL IN AMYOTROPHIC LATERAL SCLEROSIS
113
7
the rs12608932 minor allele, and thus could lead to non-replication of UNC13A as a susceptibility gene for
ALS. Therefore, referral bias, combined with reduced power (18 and 52%, respectively), forms a plausible
explanation for non-replication of this variant in the French and British cohorts.5, 9
As UNC13A might act as a modifier of disease survival, patient selection methods form an impor-
tant aspect of study design when trying to replicate findings. Larger independent studies may be needed to
establish a definite role for UNC13A in ALS susceptibility and survival. Ideally, study cohorts are derived from
population-based studies. Functional studies could provide insight into a pathogenic mechanism linking
the genetic variant rs12608932 in UNC13A to motor neuron degeneration in ALS. Ultimately, therapeutic
targets in the UNC13A pathophysiological pathway might be identified and targeted to prolong the survival
of ALS patients.
114
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study of sporadic amyotrophic lateral sclerosis. Hum Mol Genet 2009;18:1524-1532.
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5. Shatunov A, Mok K, Newhouse S, Weale ME, Smith B, et al. Chromosome 9p21 in sporadic
amyotrophic lateral sclerosis in the UK and seven other countries: a genome-wide association study.
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6. van Es MA, Van Vught PW, Blauw HM, Franke L, Saris CG, et al. ITPR2 as a susceptibility gene in sporadic
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7. van Es MA, van Vught PWJ, Blauw HM, Franke L, Saris CGJ, et al. Genetic variation in DPP6 is associated
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identifies 19p13.3 (UNC13A) and 9p21.2 as susceptibility loci for sporadic amyotrophic lateral
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10. Cheung YK, Gordon PH, Levin B. Selecting promising ALS therapies in clinical trials. Neurology
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11. Chiò A, Logroscino G, Hardiman O, Swingler R, Mitchell D, et al. Prognostic factors in ALS: A critical review.
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Neurology 2007;68:600-602.
14. Traynor BJ, Nalls M, Lai S-L, Gibbs RJ, Schymick JC, et al. Kinesin-associated protein 3 (KIFAP3) has
no effect on survival in a population-based cohort of ALS patients. Proc Natl Acad Sci U S A
2010;107:12335-12338.
15. Huisman MHB, de Jong SW, van Doormaal PTC, Weinreich SS, Schelhaas HJ, et al. Population based
epidemiology of amyotrophic lateral sclerosis using capture-recapture methodology. J Neurol Neurosurg
Psychiatry 2011.
16. Brooks BR. El Escorial World Federation of Neurology criteria for the diagnosis of amyotrophic lateral
sclerosis. Subcommittee on Motor Neuron Diseases/Amyotrophic Lateral Sclerosis of the World Federation
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of Neurology Research Group on Neuromuscular Diseases and the El Escorial “Clinical limits of amyo
trophic lateral sclerosis” workshop contributors. J Neurol Sci 1994;124 (suppl.):96-107.
17. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, et al. PLINK: a tool set for whole-genome
association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575.
18. Wigginton JE, Cutler DJ, Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum
Genet 2005;76:887-893.
19. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, et al. SNAP: a web-based tool for
identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008;24:2938-2939.
20. Augustin I, Rosenmund C, Südhof TC, Brose N. Munc13-1 is essential for fusion competence of glutama-
tergic synaptic vesicles. Nature 1999;400:457-461.
21. Zikich D, Mezer A, Varoqueaux F, Sheinin A, Junge HJ, et al. Vesicle priming and recruitment by ub
Munc13-2 are differentially regulated by calcium and calmodulin. J Neurosci 2008;28:1949-1960.
22. Varoqueaux F, Sons MS, Plomp JJ, Brose N. Aberrant morphology and residual transmitter release at the
Munc13-deficient mouse neuromuscular synapse. Mol Cell Biol 2005;25:5973-5984.
23. Rothstein JD. Excitotoxicity hypothesis. Neurology 1996;47:S19-26.
24. Miller RG, Mitchell JD, Lyon M, Moore DH. Riluzole for amyotrophic lateral sclerosis (ALS)/motor neuron
disease (MND). Cochrane Database Syst Rev 2007:CD001447.
25. Landers JE, Melki J, Meininger V, Glass JD, van den Berg LH, et al. Reduced expression of the Kinesin-
Associated Protein 3 (KIFAP3) gene increases survival in sporadic amyotrophic lateral sclerosis. Proc
Natl Acad Sci U S A 2009;106:9004-9009.
116
SUPPLEMENTARY DATA
SEQUENCING METHODS
Genotyping of the rs12608932 SNP in the independent population-based cohort was carried out by use
of capillary sequencing. We used the following primers for PCR amplification: forward 5’ ATGAAATGTTG-
GATGAGCAG; reverse 5’ CACACCCACCCATCTAACTAC. Primers were designed using LIMSTILL (http://
limstill.niob.knaw.nl).
PCR was carried out using a touchdown thermocycling program (92 °C for 60 s; 15 cycles of 92
°C for 20 s, 65 °C for 30 s with a decrement of 0.5 °C per cycle, 72 °C for 60 s; followed by 30 cycles of
92 °C for 20 s, 58 °C for 30 s and 72 °C for 60 s; 72 °C for 180 s; GeneAmp9700, Applied Biosystems,
Foster City, California, USA). PCR reaction consisted of 5 µl amplified DNA (5 ng/µl), 0.2 µM of each prim-
er, 200 µM of each dinucleotide triphosphate (dNTP), 25 mM Tricine, 7.0% glycerol (w/v), 1.6% dimethyl
sulfoxide (DMSO, w/v), 2 mM MgCl2, 85 mM ammonium acetate pH 8.7 and 0.04 U Taq Polymerase in a
total volume of 10 µl.
PCR products were diluted in 20 µl H2O and 1 µl was directly used as template for the sequencing
reactions. Sequencing reactions contained 0.1 µl BigDYE (v3.1; Applied Biosystems), 1.99 µl 2.5x dilution
buffer (Applied Biosystems) and 0.4 µM of the primer used in the PCR reaction (either forward or reverse)
in a total volume of 5 µl. The reactions were performed using cycling conditions as follows: 40 cycles of 92
°C for 10 s, 50 °C for 5 s and 60 °C for 120 s. Sequencing products were purified by ethanol precipitation
in the presence of 40 mM sodium-acetate and analyzed on a 96-capillary 3730XL DNA analyzer (Applied
Biosystems), using the standard RapidSeq protocol on 36 cm array. Traces were analyzed to determine
rs12608932 genotypes using PolyPhred and in-house developed software.
UNC13A IS A MODIFIER OF SURVIVAL IN AMYOTROPHIC LATERAL SCLEROSIS
117
Supplementary Table 1
Results for age at onset analyses
Cohort
ALS Age at onset, yr Cox regression results
n mean (range) Additive, HR (p) Dominant, HR (p)
Recessive, HR (p)
Population-based 412 61.3 (16-88) 1.07 (0.36) 1.04 (0.69) 1.17 (0.22) GWAS 1767 60.3 (18-88) 1.00 (0.97) 0.97 (0.52) 1.06 (0.37)
In the age at onset analyses 5 cases were excluded from the population-based cohort and 263 cases from the GWAS cohort due to missing covariate data. Results are shown for the three genetic models tested. GWAS: genome-wide association study; ALS: amyotrophic lateral sclerosis; HR: hazard ratio.
7
8
GENETIC MODIFIERS IN C9ORF72 REPEAT
EXPANSION CARRIERS:
A GENOME-WIDE ANALYSIS
MANUSCRIPT IN PREPARATION
Frank P Diekstra, Michael A Nalls, Vivianna M Van Deerlin†, Raffaele Ferrari,
Michael A van Es, John C van Swieten†, Peter Heutink, Aleksey Shatunov,
Ammar Al-Chalabi, Orla Hardiman, Pamela J Shaw, Karen E Morrison, Philip
van Damme, Wim Robberecht, Julie van der Zee, Christine van Broekhoven,
Alexis Brice, Isabelle Le Ber, Caroline Graff, Stuart Pickering-Brown, Adriano
Chio, Andrew B Singleton, Bryan J Traynor, John Hardy, Rosa Rademakers,
Leonard H van den Berg*, Jan H Veldink*
These authors were joint senior authors on this work *
on behalf of the International Collaboration for Frontotemporal Lobar †
Degeneration; see Appendix for full list of contributors
INTRODUCTION
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of motor
neurons in both brain and spinal cord leading to progressive muscle weakness. There is no cure for the dis-
ease and patients typically die due to respiratory insufficiency, with a median survival time of approximately
three years.1 Approximately 50 percent of ALS patients have a certain degree of cognitive symptoms, while
about 5-15 percent are diagnosed with frontotemporal dementia (FTD) in the course of the disease.2-5
Conversely, about 3-14 percent of FTD cases develop motor neuron symptoms.3
Frontotemporal dementia is a relatively rare form of dementia characterized by changes in cog-
nition, behavior and language. Although the population incidence rates are relatively low (approximately
3/100,000 per year), FTD is the second most common form of dementia under the age of 65 years.6, 7
Besides the clinical coincidence of ALS and FTD, the two diseases have important neuropathologi-
cal and genetic overlap. The two major subtypes of FTD are characterized by cellular inclusion of either tau
(FTD-tau) or TDP–43 (FTD-TDP). TDP–43 inclusions have been identified in neurons of both ALS and FTD-
TDP patients.8 Genetic studies have found mutations in VCP and FUS in both diseases, but the discovery of
120
ABSTRACT
A hexanucleotide repeat expansion in C9orf72 is one of the most important genetic causes of both
amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). C9orf72 repeat expansion
carriers show great phenotypic heterogeneity, ranging from pure motor symptoms to solely behavio-
ral or cognitive symptoms. To date, the factors that determine the C9orf72 expansion phenotype are
still largely unknown, although there is evidence for genetic variants in TMEM106B (previously implicat-
ed in the susceptibility to FTD) and ATXN2 as genetic modifiers in patients with a C9orf72 expansion.
We sought to identify additional genetic variants that may act as a genetic switch to determine the
onset of either ALS or FTD in C9orf72 repeat expansion carriers.
We collected previously published genome-wide data from 477 ALS and 285 FTD patients carrying
a C9orf72 expansion, while also adding a new cohort of 69 C9orf72 expansion carriers with ALS. We
used genome-wide imputation of genetic variants to increase genome coverage and comparability.
Genetic variants were tested using logistic regression models for association with either ALS or FTD.
Our study did not yield genome-wide significant associations, but we found a nominally significant
association with rs3173615 in TMEM106B and onset of either ALS or FTD (OR 0.74, p=0.015). In a
similar candidate gene approach, we found a nominally significant association with rs11065979 in
ATXN2 and C9orf72 disease phenotype (OR 1.35, p=0.009). Although our dataset provides a large
and unique set of both ALS and FTD cases all carrying a C9orf72 repeat expansion, statistical power
was limited to discover associations with small effect sizes (OR < 2). Therefore, further international
collaboration will be needed to increase sample size and power.
a hexanucleotide repeat expansion in an intron of the C9orf72 gene on chromosome 9p21.2 provides the
strongest genetic link between the two clinical entities.9-12
The chromosome 9p21.2 locus was first identified in linkage studies in families with ALS and FTD
cases.13, 14 Subsequently, GWAS have been able to fine map the locus to three genes, of which C9orf72 has
been discovered to harbor the causal variant.9, 10, 15-17 Approximately 6% of sporadic ALS and FTD patients
carry the expanded C9orf72 repeat, while in familial ALS and FTD this percentage reaches 37% and 25%,
respectively.18
Patients carrying the C9orf72 repeat expansion have a considerable phenotypic heterogeneity,
ranging from pure motor neuron symptoms to a cognitive/behavioral phenotype. Previous studies have
looked at genetic variants in genes that have been implicated in ALS or FTD susceptibility, and compared
their allele frequencies to control subjects. In the present study we sought to identify genetic modifiers
acting as a switch to determine the onset of either FTD or ALS within C9orf72 repeat expansion carriers.
Finding such ‘genetic switches’ may have a large impact on genetic counseling of asymptomatic C9orf72
repeat expansion carriers, and the identification of these switches might provide more insight into the path-
ways involved in the pathogenesis of both ALS and FTD. We, therefore, conducted a genome-wide analysis
of patients with C9orf72 expansions, directly comparing ALS patients to subjects with FTD.
SUBJECTS AND METHODS
SUBJECTS
ALS and FTD subjects were obtained from available and previously published GWASs of ALS or FTD pa-
tients.15-17, 19-26 We included 31 cohorts from The Netherlands, Belgium, France, Italy, Germany, United King-
dom, Ireland, Sweden, Finland, United States and Canada. All individuals were of Caucasian descent. We
excluded cohorts that had genotypes for selected SNP sets only (for example using an Illumina NeuroX
custom chip). All ALS patients were diagnosed with probable or definite ALS according to the revised El
Escorial criteria.27 For the FTD-TDP cohort, FTD diagnosis was confirmed by TDP–43 immunohistochem-
istry, while FTD cases in other cohorts were included based on a clinical diagnosis according to the Neary
criteria.28
Additionally, we included a newly genotyped cohort of 1,226 ALS patients from the Netherlands.
Cases were diagnosed with probable or definite ALS according to the revised El Escorial Criteria by neurol-
ogists specialized in motor neuron diseases. Tertiary referral centers for ALS were University Medical Center
Utrecht, Academic Medical Centre Amsterdam and Radboud University Medical Center Nijmegen.
We only included C9orf72 repeat expansion carriers, diagnosed with either ALS or FTD or both. C9orf72
repeat expansion status was determined at submitting sites by repeat-primed PCR or southern blot, as has
been described previously.9, 10, 29
All participants gave written informed consent and approval was obtained from the local institu-
tional review boards. More detailed information on ALS or FTD subject selection methods has been pub-
lished previously.15-17,19-25
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
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8
Diagnoses of ALS, ALS-FTD or FTD were made by neurologists in specialized neurological centers. As noted
previously, the diagnosis of ALS may coincide with FTD and vice versa. In the present study we aimed at
identifying genetic variants that would influence the course of disease in C9orf72 repeat expansion carriers.
Therefore, for the categorization of patients ultimately having both ALS and FTD, we used the predominant
clinical phenotype (either ALS or FTD) at disease onset, as was provided by the clinicians at the collabo-
rating sites.
GENOTYPES AND QUALITY CONTROL
All cohorts were genotyped using Illumina BeadChip arrays. We formed strata based on array version
and study, while trying to maximize stratum size for quality control procedures. Per stratum, we removed
SNPs that could cause allele swaps (tri-allelic SNPs, A/T or C/G SNPs), SNPs that were not present in
dbSNP137, SNPs with a genotyping call rate < 95%, SNPs with a minor allele frequency (MAF) in the
stratum < 1%, or with a MAF in the 1000 Genomes project < 5%, SNPs strongly deviating from Hardy-
Weinberg Equilibrium (p < 1×10–6), or SNPs where genotypes had differing missing rates between flanking
haplotypes (PLINK mishap test p < 1×10–5). Samples that had a genotyping call rate < 95%, with high or
low heterozygosity rates (± 3 standard deviations (SD) from mean inbreeding coefficient (F value) per
stratum), or where the genetic gender did not match the gender in the phenotype file, were removed.
For the purpose of additional quality control, we formed a merged set of all samples using only
SNPs overlapping all cohorts. We removed duplicate samples (PI_HAT > 0.9) and related samples (PI_HAT
> 0.125), where for duplicated or related sample pairs only the sample with the lowest call rate was re-
moved. Samples were merged with HapMap Phase 3 version 3 individuals, and using EIGENSTRAT v5.0
a principal components analysis (PCA) was conducted to identify population substructure.30 Population
outliers were defined by deviating with ± 10 SD from the mean of the PCA values of the HapMap ‘EUR’ and
‘TSI’ populations for each of the first 4 principal components. All quality control procedures were carried out
using PLINK31 and R (www.r-project.org).
GENOME-WIDE SNP IMPUTATION
In order to increase comparability and coverage, we performed genome-wide SNP imputation using the
1000 genomes Phase I Integrated Release Version 3 reference panel and MaCH v1.0 software.32 Imputa-
tion was carried out per stratum. Datasets were split into chromosomes, and subsequently, chromosomes
were split into chunks of 2500 SNPs with a 500-SNP overlap. Imputation was parallelized by using a
prephasing step (MaCH) and a genotype imputation step (minimac), as described previously.33 All program
parameters were left at their default values. Imputed genotypes were stored as continuous allele dosage
data, which are continuous numerical values indicating the estimated number of minor alleles (ranging
from 0 to 2). Only SNPs with a MAF > 0.01 and an imputation quality score threshold (r2) of > 0.6 were
included for further analyses.
122
ASSOCIATION ANALYSIS
For association analyses between SNP genotypes and susceptibility to either ALS or FTD, we opted to pool
all strata into a single analysis. A weighted meta-analysis of strata was not feasible because of the lack of
balanced strata and little power of the relatively small stratum sizes. In the pooled analysis only SNPs for
which genotype data were present in all strata were included.
We performed logistic regression in PLINK, correcting for gender and the first two principal com-
ponents, which were strongly (p < 1×10–5) associated with phenotypic outcome.
RESULTS
After quality control, there were 15 strata with a total of 402 ALS patients and 253 FTD patients
(Table 1). See Supplementary Table 1 for more details on quality control results. There were 134,258
SNPs with genotypes present across all strata, which we used for population outlier detection and re-
latedness checks (Supplementary Figure 1). We performed genome-wide SNP imputation per stra-
tum using the 1000 genomes phase I reference panel. Because of the limited study sample size, and
in order to prevent spurious association signals, very low-frequency SNPs (MAF < 0.01) were ex-
cluded. Also, in our pooled analysis we only analyzed SNPs that had genotypes in all strata, ultimate-
ly leaving 3,432,133 SNPs for analysis. We tested SNP genotypes for association with suscepti-
bility to either ALS or FTD using logistic regression analysis. Figure 1 shows a Manhattan plot of
association results. The genomic inflation factor was 1.045, indicating adequate quality control. A quan-
tile-quantile plot is shown in Supplementary Figure 2. The top ten most significant hits are shown in
Table 2. We found no SNP markers reaching genome-wide significance (p < 5×10-8).
123
Table 1
Stratum details after quality control
Stratum name Reference n SNPs n ALS n FTD
ALS_BE1 van Es, 200916 310485 28 0
ALS_IR1 Cronin, 200821 454297 8 0
ALS_IR2 van Es, 200916 492023 13 0
ALS_IT1 Chiò, 200922 472105 12 0
ALS_IT2 Traynor, 201023
465902 7 0
ALS_NL1 van Es, 200720
296453 26 0
ALS_NL2 van Es, 200916 313509 20 0
ALS_UK2 Shatunov, 201015 505955 42 0
FTD_TDP Van Deerlin, 201024
486937 6 86
ALS_FI1 Laaksovirta, 201017 308211 86 0
ALS_NIH Johnson, 201426
582676 92 0
ALS_US1 Schymick, 200719 471685 16 0
FTD_CLI1 Ferrari, 201425
485427 1 140
FTD_CLI2 Ferrari, 201425
580927 2 27
ALS_NL3 Present study 572333 43 0
Total 402 253
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
8
Subsequently, we specifically investigated SNPs in genes that have previously been implicated as disease
modifiers in C9orf72 repeat expansion carriers. For rs3173615 in gene TMEM106B, van Blitterswijk et al.
compared minor allele frequencies in ALS or FTD patients with C9orf72 expansions to unaffected controls.34
They found a decreased minor allele frequency for FTD patients (35.5% vs. 43.2%, Cohort 1), in particular
under a recessive genetic model (OR 0.33, p=0.009), while this effect was not significant in C9orf72 repeat
expansion carriers with ALS (OR 0.85, p=0.55). Similar to the findings of van Blitterswijk et al., we found
a lower minor allele frequency for rs3173615 in FTD cases with C9orf72 expansions compared to ALS
cases (OR 0.74, p=0.015). Another SNP in TMEM106B (rs57506017) showed a stronger signal (OR 0.65,
p=0.002). See Table 3 for more detailed results.
124
Figure 1
Manhattan plot
Each dot represents a single nucleotide polymorphism; -log10 p values are shown on the y-axis, and chromo-
somal positions on the x-axis. Chromosomes are numbered along the x-axis and are designated by changing
colors. The threshold for genome-wide significance (p < 5 x 10-8) is indicated by a dotted line.
Table 2
Top 10 association loci from genome-wide analysis
Locus Nearest gene n SNPs Top SNP Minor allele MAF OR p
3p25.1 COLQ 11 rs73146147 C 0.11 0.41 2.60×10-6
12q23.2 C12orf42 19 rs1401994 T 0.47 0.56 4.32×10-6
8p23.1 RP1L1 4 rs10089537 G 0.44 1.79 5.18×10-6
22q13.31 LDOC1L 3 rs135918 A 0.48 0.53 5.90×10-6
22q13.1 TRIOBP 1 rs5750482 T 0.42 0.60 2.15×10-5
7p15.3 DNAH11 4 rs115529292 C 0.21 0.52 6.83×10-6
4p15.2 KCNIP4 1 rs199767347 del 0.45 1.79 9.12×10-6
1q41 HHIPL2 1 rs35763770 A 0.28 0.53 9.39×10-6
3p21.31 SACM1L 1 rs2673050 T 0.44 1.74 1.18×10-5
4q25 - 2 rs2443054 T 0.31 0.54 1.22×10-5
Per locus, the number of SNPs with p < 1×10-4 is indicated, and association results for the SNP with the most significant p-value
(top SNP) are presented. MAF = weighted minor allele frequency across all datasets; OR = odds ratio; del = deletion.
Furthermore, previously an intermediate-length polyglutamate repeat in ATXN2 was more frequently en-
countered in C9orf72 expansion carriers with ALS/ALS-FTD than in expansion carriers with pure FTD.35
We, therefore, explored SNPs in the ATXN2 locus for association with an ALS or FTD phenotype in our
dataset. We found the most significant association with rs11065979, located within a region of strong LD
comprising ATXN2 (OR 1.38, p=0.009, Table 3). The minor allele was associated with an increased risk for
the FTD phenotype.
DISCUSSION
In the present study we aimed at identifying genetic modifiers that would determine the onset of either ALS
or FTD in C9orf72 repeat expansion carriers. Therefore, we have collected the largest genome-wide data
set of C9orf72 repeat expansion carriers with ALS or FTD. We applied stringent quality control to the raw
genotype data and performed genome-wide SNP imputation using the 1000 genomes reference panel,
yielding over 3 million SNPs with high accuracy (i.e. MaCH imputation r2 > 0.9). Our analysis did not yield
any genome-wide significant association signals. Additionally, in a candidate gene approach, we were able
to confirm previously identified disease modifiers (in TMEM106B and ATXN2) in C9orf72 repeat expansion
carriers.
The TMEM106B locus was first identified as a risk locus for FTD in a GWAS of FTD patients with
neuronal TDP-43 inclusions (FTD-TDP).24 Further studies have confirmed this association and, additionally,
implicated genetic variants in TMEM106B as a risk factor for cognitive impairment in ALS patients.36-38 More
recently, TMEM106B has been identified as an important disease modifier in subjects with C9orf72 repeat
expansions, modifying age at onset, age at death and the risk of developing FTD.34, 39 Our study confirms
the previous finding that in C9orf72 expansion carriers, the minor allele of rs3173615 is less frequent in
FTD patients than in patients with ALS. TMEM106B is a transmembrane protein, predominantly localized
at the lysosomal membrane, where it may regulate lysosomal size, motility and responsiveness to stress.40
TMEM106B may interact with MAP6, which is necessary for normal dendritic trafficking of lysosomes,
implicating lysosomal biology in the pathogenesis of the ALS-FTD spectrum.41
As noted previously, intermediate-length repeat expansions in the Ataxin-2 gene (ATXN2) may
modify the disease phenotype in C9orf72 repeat expansion carriers, conferring an increased risk of the
125
Table 3
Association results for loci previously identified as disease
modifiers in C9orf72 repeat expansion carriers
Locus SNP Minor allele MAF FTD MAF ALS OR p value
TMEM106B rs3173615 G 0.368 0.399 0.74 0.015
TMEM106B rs57506017 T 0.227 0.302 0.65 0.002
ATXN2 rs11065979 T 0.472 0.389 1.38 0.009
An odds ratio less than 1 indicates that the minor allele was less frequent in FTD compared to ALS C9orf72 repeat expansion carriers. MAF = minor allele frequency, OR = odds ratio.
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
8
ALS phenotype.35 In the present study, we did not have ATXN2 repeat lengths available, but we used SNP
genotypes as a by-proxy attempt instead. We found a nominally significant association with rs11065979,
of which the major allele was associated with an increased risk of ALS compared to FTD. Further studies,
measuring ATXN2 repeat lengths in different C9orf72 phenotypes, are needed to definitively establish the
role of ATXN2 as a disease modifier in C9orf72 repeat expansion carriers.
For the present study we collected a large and unique set of C9orf72 repeat expansion carriers
through international collaboration. The uniformity of our study dataset (i.e. all patients share the same ge-
netic aberration in C9orf72) forms an important means to increase statistical power. However, for a GWAS,
the numbers of ALS and FTD patients we were able to include are still small. We might have failed to iden-
tify an association with genome-wide significance due to the lack of statistical power. Power calculations
estimated approximately 80% power for the detection of an association with odds ratio 2.0, minor allele
frequency 0.4 at α = 5×10-8. However, power dropped dramatically for smaller effect sizes. For example,
we had about 5% power for an association with OR 1.5. We have collected the largest sample of C9orf72
repeat expansion carriers with genome-wide SNP data, but future efforts should be made, through inter-
national collaboration, and also applying linear mixed model association techniques42 to further explore the
C9orf72 genetic switch hypothesis.
We aimed to categorize C9orf72 expansion carriers according to ALS or FTD phenotype as ac-
curately as possible, using the most reliable and most clinically relevant predominant symptom at disease
onset. However, as previously noted in literature, ALS and FTD may form parts of a spectrum.3 Therefore,
while the pure motor and pure FTD phenotypes will have been accurately categorized, recollection bias
might have lead to inaccurate categorization of some ALS-FTD cases (there were 29 ALS-FTD cases in our
dataset, 4.4%). This might have lead to a decrease in statistical power.
Another explanation for the lack of any genome-wide significant hits in our study might be that
the phenotypic heterogeneity amongst C9orf72 repeat expansion carriers is more strongly modified by
epigenetic or environmental factors. A previous study found that hypermethylation of the C9orf72 promoter
region was associated with longer survival, but methylation was not different between ALS and FTD repeat
expansion carriers.43 Moreover, a somatic heterogeneity of C9orf72 repeat expansions in different brain
regions has been hypothesized as a possible determinant for either developing ALS or FTD, although this
could not be confirmed in another study.44 As a logical step in the study of a repeat-related disorder, several
studies have investigated whether C9orf72 repeat expansion size is associated with either the ALS or FTD
phenotype. However, such an association has not been established so far, although C9orf72 repeat number,
especially in the cerebellum, might determine survival duration from onset in either ALS or FTD.44-46
In conclusion, our study did not identify new genetic variants that determine an ALS or FTD pheno-
type in C9orf72 repeat expansion carriers. Although we collected the largest available sample of C9orf72 ex-
pansion carriers with genome-wide data, lack of statistical power might explain why we could not detect any
genome-wide significant associations. We replicated the association with TMEM106B as a disease modifier
using a candidate gene approach, further corroborating it’s role in the spectrum of ALS and FTD. Further in-
ternational collaboration will be needed to improve sample size and statistical power in order to detect ad-
ditional genetic modifiers that may explain phenotypic heterogeneity in C9orf72 repeat expansion carriers.
126
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129
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
8
SUPPLEMENTARY INFORMATION
130
Supplementary Table 1
Cohorts, strata and quality control metrics Cohorts
Pre QC
QC p
er stratum: S
NPs
Q
C p
er stratum: sam
ples
After Q
C
Country
Reference
Illumina
platform
platform
ALS
FTD
SN
Ps Stratum
'Bad'
SN
Ps
SN
Ps Call
raterate
MAF
MAF
1KG
1KG
HW
E H
ap.
miss.
miss.
Non-
autosom.
autosom.
Call
rate rate
Hetero-
zygosityzygosity
Gend
erer
Dup
l Relat
eded
Pop.
outlier outlier
ALS
FTD
SN
Ps
Belgium
van Es, 20
09
16 370
K 28
0
37040
4 ALS
_BE1
30
592 290
8 6358
10870
0
0
9283
0
0
0
0
0
0
28
0
310485
Ireland Cronin, 20
08
21 550
K 8
0
561466 ALS
_IR
1
3848 90
60
46083
38277 0
0
10687
0
0
0
0
0
0
8
0
454297
Ireland van Es, 20
09
16 610
K 14
0
62090
1 ALS
_IR
2
36687 5644
31609
43662 0
0
11666
1 0
0
0
0
0
13
0
492023
Italy Chi ò
, 2009
22 550
K 12
0
555352 ALS
_IT1
3870
30
87 2660
8 38449
0
0
11420
0
0
0
0
0
0
12
0
472105
Italy Traynor, 20
1023
610K
7 0
62090
1 ALS
_IT2
37132
2710
61010
43622
0
0
10940
0
0
0
0
0
0
7
0
465902
The Netherlands
van Es, 2007
20 317K
30
0
317503
ALS
_N
L1
2208
2473 858
6851 1
0
8675
0
1 0
2 1
0
26
0
296453
The Netherlands
van Es, 2009
16 370
K 40
0
37040
4 ALS
_N
L2
30592
497 560
9 10
870
1 0
9414
0
0
0
16 3
1
20
0
313509
United Kingdom
Shatunov, 20
1015
610K
42 0
584414 ALS
_U
K2
7613
29 15640
430
19 0
0
12163
0
0
0
0
0
0
42
0
505955
Multiple
Van D
eerlin, 2010
24 610
K, 550K
6 91
551767 FTD
_TD
P
3190
2658 9630
3780
5 17
0
11701
0
2 1
1 1
0
6
86 486937
Finland Laaksovirta, 20
1017
370K
96 0
339910
ALS
_FI1
470
7 1452
5676 10
869 121
0
9113
0
2 0
1 7
0
86
0
308211
Multiple
Johnson, 2014
26 O
mniX
press 10
8 0
730525
ALS
_N
IH
10
174 2967
43783 76916
72 36
14096
6
4 0
1 5
0
92
0
582676
United S
tates Schym
ick, 2007
19 550
K 16
0
545066
ALS
_U
S1
3583
3962 170
77 37695
0
0
11236
0
0
0
0
0
0
16
0
471685
Sw
eden Ferrari, 20
1425
660K
0
8 561490
FTD_
CLI1
4941 80
59 10
434 39233
52 10
0
11287
6 3
3 3
4 2
1 140
485427
Italy Ferrari, 20
1425
660K
0
2 561490
Italy Ferrari, 20
1425
660K
0
1 561490
Italy Ferrari, 20
1425
660K
0
4 561490
The Netherlands
Ferrari, 2014
25 660
K 0
25 561490
France Ferrari, 20
1425
660K
1 16
561490
Italy Ferrari, 20
1425
660K
0
2 561490
United Kingdom
Ferrari, 20
1425
660K
0
20
559348
United Kingdom
Ferrari, 20
1425
660K
0
10
561490
Italy Ferrari, 20
1425
660K
0
5 561490
Germ
any Ferrari, 20
1425
660K
0
1 561490
United S
tates Ferrari, 20
1425
660K
0
28 561490
Italy Ferrari, 20
1425
660K
0
4 561490
Germ
any Ferrari, 20
1425
660K
0
5 561490
Canada
Ferrari, 2014
25 660
K 0
2 561490
Italy Ferrari, 20
1425
660K
0
9 561490
Belgium
Ferrari, 20
1425
660K
0
19 561490
United S
tates Ferrari, 20
1425
Om
niXpress
1 16
731442 FTD
_CLI2
10
364 5564
44026
76978 0
0
13784
0
1 0
0
1 0
2
27 580
927 U
nited States
Ferrari, 2014
25 O
mniX
press 1
13 731442
The Netherlands
Present study O
mniX
Press 71
0
719665 ALS
_N
L3
8463 6774
42913 75947
0
0
13590
1
1 0
21 4
1
43 0
572333
Total
48
1 28
1
40
2 253
QC = quality control;
'bad' SN
Ps = tri-allelic SN
Ps, A/T or C
/G S
NPs, S
NPs not present in dbS
NP 137; M
AF = minor allele frequency; 1KG
= 1000 G
enomes; H
WE = H
ardy-Weinberg Equilibrium
; Hap. m
iss. = missing by haplotype;
Non-autosom
. = non-autosomal S
NPs; D
upl = duplicate samples; Pop. outlier = population outliers as determ
ined by principal components analysis.
131
Supplementary Figure 1
Detection of population outliers
Plot for the detection of population outliers based on a principal components analysis in EIGENSTRAT. The first two principal
components are plotted. HapMap Phase 3 release 3 reference samples (CEU, TSI, ASW, MKK, YRI, LWK, GIH, MEX, JPT, CHB,
CHD populations) are shown as colored diamonds (green, red, yellow and blue). Study samples are designated by colored
circles. Samples marked with ‘×’ were identified as population outliers and were marked for removal.
Supplementary Figure 2
Quantile-quantile plot
Quantile-quantile plot for association results after imputation. The genomic inflation
factor (λGC
) is shown at the bottom right.
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
8
APPENDIX
THE INTERNATIONAL COLLABORATION FOR FRONTOTEMPORAL DEMENTIA
Vivianna M Van Deerlin, Patrick M A Sleiman, Maria Martinez-Lage, Alice Chen-Plotkin, Li-San Wang, Neill R
Graff-Radford, Dennis W Dickson, Rosa Rademakers, Bradley F Boeve, Murray Grossman, Steven E Arnold,
David M A Mann, Stuart M Pickering-Brown, Harro Seelaar, Peter Heutink, John C van Swieten, Jill R Murrell,
Bernardino Ghetti, Salvatore Spina, Jordan Grafman, John Hodges, Maria Grazia Spillantini, Sid Gilman,
Andrew P Lieberman, Jeffrey A Kaye, Randall L Woltjer, Eileen H Bigio, Marsel Mesulam, Safa al-Sarraj,
Claire Troakes, Roger N Rosenberg, Charles L White III, Isidro Ferrer, Albert Lladó, Manuela Neumann, Hans
A Kretzschmar, Christine Marie Hulette, Kathleen A Welsh-Bohmer, Bruce L Miller, Ainhoa Alzualde, Adolfo
Lopez de Munain, Ann C McKee, Marla Gearing, Allan I Levey, James J Lah, John Hardy, Jonathan D Rohrer,
Tammaryn Lashley, Ian R A Mackenzie, Howard H Feldman, Ronald L Hamilton, Steven T Dekosky, Julie van
der Zee, Samir Kumar-Singh, Christine Van Broeckhoven, Richard Mayeux, Jean Paul G Vonsattel, Juan
C Troncoso, Jillian J Kril, John B J Kwok, Glenda M Halliday, Thomas D Bird, Paul G Ince, Pamela J Shaw,
Nigel J Cairns, John C Morris, Catriona Ann McLean, Charles DeCarli, William G Ellis, Stefanie H Freeman,
Matthew P Frosch, John H Growdon, Daniel P Perl, Mary Sano, David A Bennett, Julie A Schneider, Thomas
G Beach, Eric M Reiman, Bryan K Woodruff, Jeffrey Cummings, Harry V Vinters, Carol A Miller, Helena C
Chui, Irina Alafuzoff, Päivi Hartikainen, Danielle Seilhean, Douglas Galasko, Eliezer Masliah, Carl W Cotman,
M Teresa Tuñón, M Cristina Caballero Martínez, David G Munoz, Steven L Carroll, Daniel Marson, Peter F
Riederer, Nenad Bogdanovic, Gerard D Schellenberg, Hakon Hakonarson, John Q Trojanowski, Virginia M-Y
Lee.
THE SLAGEN CONSORTIUM
Isabella Fogh, Antonia Ratti, Cinzia Gellera, Kuang Lin, Cinzia Tiloca, Valentina Moskvina, Lucia Corrado,
Gianni Sorarù, Cristina Cereda, Stefania Corti, Davide Gentilini, Daniela Calini, Barbara Castellotti, Letizia
Mazzini, Giorgia Querin, Stella Gagliardi, Roberto Del Bo, Francesca Luisa Conforti, Cosenza, Gabriele
Siciliano, Maurizio Inghilleri, Francesco Saccà, Paolo Bongioanni, Silvana Penco, Massimo Corbo, Sandro
Sorbi, Massimiliano Filosto, Alessandra Ferlini, Anna Maria Di Blasio, Stefano Signorini, Nicola Ticozzi,
Mauro Ceroni, Elena Pegoraro, Giacomo P Comi, Sandra D’Alfonso, Franco Taroni, Ammar Al-Chalabi, John
Powell and Vincenzo Silani.
132
133
GENETIC MODIFIERS IN C9ORF72 REPEAT EXPANSION CARRIERS: A GENOME-WIDE ANALYSIS
8
9
SUMMARY AND GENERAL DISCUSSION
SUMMARY AND GENERAL DISCUSSION
The aim of this thesis was to identify genetic susceptibility factors in ALS. In order to achieve this aim
we have investigated candidate genes, gene-environment interactions, expression quantitative trait loci
(eQTLs), genetic pleiotropy, and disease modifiers in ALS. We have shown an example of gene-environment
interaction for the paraoxonase 1 gene and population density. We have identified an ANG mutation in a ped-
igree of familial ALS, of which one patient, additionally, showed signs of frontotemporal dementia (FTD) and
parkinsonism. By the genetic mapping of gene expression we have implicated CYP27A1 as a susceptibility
gene in ALS. We have found that UNC13A modifies survival in ALS patients and, additionally, is a shared risk
locus for both ALS and FTD. We did not find any shared susceptibility loci for ALS and multiple sclerosis
(MS). Also, we were not able to identify genetic modifiers that determined phenotypic heterogeneity in
C9orf72 repeat expansion carriers.
This thesis largely follows the developments of genetic research in ALS. Early genetic studies in
ALS investigated pedigrees with familial ALS by using linkage studies, while sporadic ALS patients were
studied by using a candidate gene approach based upon hypothesized pathogenic pathways. In 2007,
the era of genome-wide association studies (GWAS) began in the search for genetic susceptibility factors
in sporadic ALS. While these GWASs have implicated several new disease loci, most of them have proven
difficult to replicate. More importantly, the results have been insufficient to explain the estimated heritability
of approximately 60% for sporadic ALS.1 In this thesis, we have explored different methods to search for
this ‘missing heritability’. By partly building on previous GWAS data and adding, for example, gene expres-
sion profiles or GWAS data from neighboring diseases, the aim was to maximize the informative potential
of these tremendous amounts of genetic data. Lastly, we looked into genetic disease modifiers, instead of
studying susceptibility risk factors per se, as these may provide additional clues to pathogenic mechanisms
in ALS or even targets for therapy. The results from the studies in this thesis are summarized and discussed
in more detail.
CANDIDATE GENE APPROACHES
In Chapter 2 we tested whether genetic variants in paraoxonase (PON) genes would interact with the envi-
ronmental exposure to paraoxonase substrates (e.g. pesticides), which has been implicated as an environ-
mental risk factor for ALS. We included 98 patients from a British population-based registry and obtained
genotypes for four single nucleotide polymorphisms (SNPs) in paraoxonase genes that were previously
associated with ALS. Subsequently, we tested for the interaction between SNP genotype and population
density (as a proxy for rural versus urban regions, presuming differential exposures to PON substrates) in a
case-only design. We found a significant gene-environment interaction for rs854560 (amino acid change
L55M) in PON1. The minor allele (M) was more frequent in ALS patients with rural residence, suggesting
that in the rural environment the MM genotype predisposes to ALS susceptibility. Also, the minor allele was
associated with shorter survival. Although the study results draw a compelling conclusion, there are several
136
weaknesses that may be pointed out. The study size is rather small, possibly inducing a type I error. Further-
more, population density may reflect many differences in environmental exposures besides the intended ex-
posure to pesticides. Lastly, a case-only design only measures the gene-environment interaction, while the
effects of gene or environment separately cannot be determined. Most notably, more recent publications on
paraoxonase polymorphisms, including a large meta-analysis, failed to identify significant associations with
sporadic ALS.2,3 The initially reported associations between paraoxonase and ALS might, therefore, have to
be considered false positive findings.4-6 In view of this, the identification of a gene-environment interaction
for the L55M polymorphism in PON1 in ALS patients may be of limited value. Of interest, there appears to be
more evidence for an association between paraoxonase polymorphisms and Parkinson’s disease, although
once again conflicting results have been reported.7
Chapter 3 describes a large pedigree of ALS patients and a patient with a complex phenotype
including motor neuron signs, parkinsonism, and FTD symptoms. We screened a set of 39 unrelated fa-
milial ALS index patients for ANG mutations and identified a (122A>T) mutation in one patient, leading
to a K17I amino acid change in the ANG protein. Further investigation of 44 out of 62 family members
of the proband’s pedigree (five affected individuals) revealed that all affected family members carried the
K17I mutation. There were ten carriers without symptoms, of which 9 were under the age of 50 years.
We also screened a set of 275 unrelated control samples, in which no K17I mutation was detected. We
demonstrated segregation of the ANG K17I mutation with disease in a large pedigree. One family member
with the K17I mutation showed a striking phenotype, with atypical parkinsonism at onset progressing to a
combined ALS and FTD phenotype in the further course of disease. Interestingly, in a later mutation screen-
ing for other familial ALS genes, four out of five affected family members appeared to also carry a N352S
mutation in TARDBP.8 Even more interesting was the finding that the family member with a combined ALS,
parkinsonism, and FTD phenotype carried the ANG K17I mutation without the TARDBP mutation. The
TARDBP N352S mutation is considered to be pathogenic, although there may be incomplete penetrance.8
Later studies have also demonstrated ANG K17I mutations in control subjects. The K17I mutation, how-
ever, may still be relevant to ALS pathogenesis as it is likely to disrupt normal protein function, including
angiogenic and ribonucleolytic activity.9 Furthermore, a large study has demonstrated that ANG mutations
not only form a risk factor for ALS, but also for Parkinson’s disease.10 The latter finding might explain the
combined phenotype in the family member carrying the ANG mutation only. The co-segregation of ANG and
TARDBP mutations in this family supports the possibility of an oligogenic basis for disease in ALS, as has
been described previously.8
GENETIC MAPPING OF GENE EXPRESSION
Gene expression levels can be mapped to genetic variation to form so-called expression quantitative trait
loci or eQTLs. eQTLs can have local effects (cis) or can have distant effects (trans) across the genome.
Trait-associated SNPs are more likely to be eQTLs.11 We, therefore, conducted a genome-wide mapping
SUMMARY AND GENERAL DISCUSSION
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9
of gene expression in order to detect novel disease-causing variants, that may not have been detected in
traditional GWAS designs due to strict multiple-testing correction (Chapter 4). Using a two-stage design
we determined eQTLs in a total of 323 sporadic ALS patients and 413 controls and we prioritized these
eQTLs based on results from a two-stage GWAS, including a total of 3,568 ALS patients and 10,163
unaffected controls. Expression profiles were derived from peripheral whole blood tissue. Ultimate-
ly, we identified one cis eQTL (eight SNP-mRNA transcript pairs) to be associated with ALS (preplication =
1.19×10-47) which explained 48-65% of the variance in gene expression. The SNP minor alleles were
associated with an increased expression of CYP27A1, a gene involved in cholesterol metabolism, and mu-
tations in which are known to cause a rare neurological disorder called cerebrotendinous xanthomatosis
(CTX).12,13 CTX can present with upper motor neuron symptoms and is a known mimic for primary later-
al sclerosis. In addition, cholesterol metabolism has previously been implicated in ALS pathogenesis.14-16
These findings would make CYP27A1 an interesting candidate gene for ALS. Additionally, we have been able
to fine-map the chromosome 9p21.2 locus using eQTLs, confirming C9orf72 as the functionally relevant
gene within this locus. In our data we confirmed that trait-associated SNPs were enriched for eQTLs. The
study was designed to minimize the chance of false-positive associations by meticulous quality control, ex-
cluding expression probes with non-specific mapping, permutation of SNP-transcript pair associations, and
by using a two-stage approach. A drawback of the study is the use of whole blood tissue instead of neuronal
tissue. The overlap of eQTLs between different tissue types appears to be modest. One study has shown
a 21.8% overlap of cis eQTLs between B-cells and monocytes in blood.17 Similarly, a 22% overlap was
found between two studies on brain tissue.18,19 Another study found that 28.7% of cis eQTLs were different
across adipose tissue, muscle, liver and whole blood.20 Furthermore, trait-associated SNPs appear to be
enriched for tissue-specific eQTLs.20 About 37-52% of genes mapped by cis eQTLs in human brain tissue
studies were present in our data. While we considered peripheral whole blood as a valid starting point for
the detection of eQTLs associated with ALS, we must consider the possibility that these might not translate
to neuronal tissues. Or, conversely, we might not have picked up brain-specific eQTLs in peripheral blood.
The two-stage design offered a valuable internal replication step, but may have attenuated statisti-
cal power for the GWAS results that were included in the analysis. For example, the GWAS SNP p-value for
the CYP27A1 eQTL was only just nominally significant (p = 0.049) in the discovery GWAS (2,261 ALS cases
and 8,328 controls), although the association reached p = 1.32×10-4 in the replication GWAS (1,307 ALS
cases and 1,835 controls). To date, no publications exist that have replicated the association of CYP27A1
with ALS, including the most recent and largest GWAS.21 Therefore, the role of CYP27A1 in the pathogenesis
of ALS remains uncertain. Nevertheless, the study has proven that such an integrated approach forms a
valuable method for prioritizing genetic variants from GWAS loci, given the results in the C9orf72 locus.
Furthermore, the technique more directly assesses functional pathways involved in disease pathogenesis.22
138
GENETIC PLEIOTROPY
Genetic pleiotropy occurs when one gene may affect multiple traits, for which many examples exist in hu-
man genetic disorders. In the search for missing the heritability in sporadic ALS we combined GWAS data
from ALS patients with data from multiple sclerosis (MS) patients (Chapter 5) and with a GWAS of patients
with frontotemporal dementia (Chapter 6). Because genetic risk factors may be shared across neighboring
disorders, such undertakings may yield new insights into pathogenic mechanisms that are involved in both
diseases.
ALS AND MS
In Chapter 5, we collected GWAS data from 3,762 sporadic ALS patients and 4,886 controls and combined
these data with 4,088 MS patients and 7,144 controls. After genome-wide SNP imputation, in order to
increase comparability and coverage across study strata, we performed a genome-wide meta-analysis of
both diseases. We could not replicate previously associated genetic variants from one disease in the other
and vice versa. We further looked for association signals that would strengthen each other in the combined
analysis. Only the HLA region on chromosome 6 reached genome-wide significance, and a suggestive
association signal was found on chromosome 17 near NPEPPS (rs2935183, p < 5×10-7). However, both
signals were driven by the MS data. Lastly, we looked for a shared polygenic contribution of multiple SNP
markers collectively for both diseases, but failed to identify a subset of SNPs that explained genetic var-
iance in both ALS and MS. In conclusion, we found no evidence for a shared genetic basis of common
variants in ALS and MS. The study can be considered well powered for the detection of shared common
risk variants in both diseases, although the possibility of a shared rare risk variant cannot be excluded. Pre-
vious studies have reported a concurrence of ALS and MS, in particular in the presence of C9orf72 repeat
expansions.23,24 However, this concurrence might be very well explained by referral bias or, in case of the
Iranian study24, by population-specific effects. A survey of patients with both ALS and MS symptoms in a
population-based study in The Netherlands did not show a higher than expected frequency of concurrence
of ALS and MS.25 In patients with MS symptoms only, no C9orf72 repeat expansions have been identified.23,26
Based on aforementioned results, ALS and MS show little evidence for a shared genetic basis. There is, on
the other hand, more convincing evidence for shared genetics between MS and inflammatory diseases.27
Therefore, although neurodegenerative processes may be involved in multiple sclerosis (either causative or
consequential), they do not appear to be regulated by genes that are involved in the pathogenesis of ALS.
ALS AND FTD
Subsequently, we looked for shared genetic risk loci between ALS and FTD (Chapter 6). We collected all
available previous ALS GWAS data and combined these with a GWAS of pathology-proven FTD with TDP-
43 inclusions (FTD-TDP). A total of 4,377 ALS patients and 13,017 controls were combined with 435
FTD-TDP patients and 1,414 controls. We applied uniform quality control to the raw genotype data and
SUMMARY AND GENERAL DISCUSSION
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9
performed genome-wide SNP imputation using the HapMap3 reference panel to increase comparability
and genome coverage. By using three complementary methods (a joint meta-analysis, replication of top
results from one disease in the other, and a rank-products analysis allocating equal weight to ALS and FTD
sample sizes) we demonstrated that not only C9orf72, but also UNC13A is a shared risk locus for ALS and
FTD. Despite the relatively small size of the FTD-TDP cohort, we found a strong signal at rs12608932
in UNC13A (OR 1.46, p = 6.57×10-6) in the FTD-TDP cases. Similar to the C9orf72 locus, the association
signal at UNC13A was greatly enhanced in a combined meta-analysis of ALS and FTD cohorts. Of course,
one might argue that these association signals were mainly driven by the large ALS cohort in comparison to
the relatively small FTD-TDP cohort. We, therefore, included two analyses that were independent of relative
sample size and we demonstrated that there was a major contribution to the UNC13A signal from the FTD-
TDP cohort. Additionally, we showed that association signals were not solely driven by FTD patients with
motor neuron symptoms. Lastly, we looked into a suggestive association signal at chromosome 8q24.13
that emerged from the joint meta-analysis (lowest p-value = 3.91×10-7, OR 0.79). We replicated associa-
tions with two SNPs in this locus in 4,056 ALS patients and 3,958 controls with nominal significance. The
locus comprises gene KIAA0196 (alias SPG8) that codes for strumpellin, in which mutations are known to
cause hereditary spastic paraplegia.28 However, the combined discovery and replication p-values for this
locus did not reach the widely accepted threshold of genome-wide significance (p < 5×10-8). Therefore,
additional independent replication, preferably in both ALS and FTD cohorts, will be required to definitively
establish the association of the 8q24.13 locus in ALS and FTD.
The results from Chapter 6 further support the hypothesis that ALS and FTD form parts of a
spectrum of neurodegenerative disease.29 We have confirmed the important role of C9orf72 in the ALS-FTD
spectrum. More importantly, our study has been the first to implicate UNC13A in the pathogenesis of FTD-
TDP, further corroborating the role of this gene in neuronal degeneration. The role of UNC13A in ALS and
FTD is discussed in more detail further in this chapter.
DISEASE MODIFIERS
Another way to gain insight into disease pathways in ALS is to investigate modifiers of disease susceptibility
or disease progression. Following the first GWAS in ALS that identified UNC13A as a risk factor for sporadic
ALS we examined whether common variation in this gene might modify age at onset or survival in ALS
(Chapter 7). We genotyped rs12608932 in UNC13A in a new population-based sample of 450 sporadic
ALS patients and 524 unaffected control individuals and included individuals with age at onset and survival
data from previous GWAS cohorts (1,767 ALS patients and 1,817 controls).30 In the newly genotyped co-
hort we found a significant association of rs12608932 with ALS susceptibility (OR 1.91, p = 0.001), and
we confirmed the association in the GWAS cohort. Additionally, in both the population-based cohort and
the GWAS cohort we found a significant effect of UNC13A on survival in ALS patients, with the minor allele
being associated with shorter survival. The difference in median survival between genotypic groups was
140
10.0 months in the population-based cohort and 5.0 months in the GWAS cohort. We found no association
with age at onset. This study highlights the need for population-based cohorts when investigating genetic
susceptibility factors that modify survival, as referral-based prevalent cohorts may miss a proportion of
short survivors, thus influencing allele frequencies in the sampled cohort (“frailty bias”).31-35 The use of
referral-based, prevalent cohorts instead of population-based cohorts may, besides the lack of statistical
power, explain why early replication attempts have failed to replicate an association between UNC13A and
sporadic ALS.36,37 An Italian population-based study has confirmed the association of UNC13A with survival
in sporadic ALS. They found that homozygosity for the rare allele shortened ALS survival with approximately
12 months.38
Chapter 8 describes the results of a search for genetic modifiers that determine the onset of
either ALS or FTD in C9orf72 repeat expansion carriers. Genome-wide SNP genotypes of ALS and FTD
patients with a C9orf72 repeat expansion were collected from previous studies across multiple countries
and we added a newly genotyped cohort of Dutch C9orf72 expansion carriers. Individuals were assigned a
diagnosis group of ALS or FTD based on their predominant clinical symptom at disease onset. After quality
control, there were 402 ALS patients and 253 FTD patients that we used for genome-wide SNP imputation
using the 1000 Genomes reference panel. We then tested for association between SNP genotypes and the
onset of either ALS or FTD. We found no genome-wide significant associations. Subsequently, we focused
on common variants in TMEM106B and ATXN2 that have previously been implicated as disease modifiers
in C9orf72 repeat expansion carriers.39,40 We were able to replicate the association with common variants
in TMEM106B and the ALS or FTD phenotype in C9orf72 carriers with nominal significance (OR 0.79, p =
0.015) and, similarly, we found a nominally significant association for a SNP in ATXN2. Although the study
includes the largest sample of ALS and FTD C9orf72 repeat expansion carriers with genome-wide data
available, and all individuals shared the same genetic aberration, the lack of statistical power may still
explain why we have not identified any novel ‘genetic switches’ that determined the onset of either ALS
or FTD. Common variants in TMEM106B have been identified as a risk factor for FTD-TDP and cognitive
symptoms in ALS.41 Our findings add to the existing evidence that TMEM106B is a disease modifier in C9orf72
repeat expansion carriers.40,42 The TMEM106B protein plays an important role in lysosomal morphology
and trafficking in neurons, and highlights this pathway in neurodegeneration.43,44
UNC13A
One of the main findings in this thesis is the accumulating evidence for UNC13A in the pathogenesis of ALS.
The common variant rs12608932 in UNC13A on chromosome 19p13.11 was first identified in a large
two-stage GWAS of sporadic ALS.30 The association has proven difficult to replicate in mostly small ALS
cohorts, probably due to the small effect size and the use of prevalent, often referral-based cohorts (as
has been explained in detail in Chapter 7).21,36,37 Another explanation may be population-specific effects.45
However, we have been able to replicate the association with disease susceptibility in a Dutch popula-
SUMMARY AND GENERAL DISCUSSION
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9
tion-based cohort of ALS and, most notably in an international dataset of pathology-proven FTD-TDP cases
(Chapter 6 and 7). A forest plot of published association results for rs12608932 is shown in Figure 1,
indicating an overall significant effect. Furthermore, we identified UNC13A as a modifier of survival in ALS
(Chapter 7). This finding has now been replicated in two independent ALS cohorts, one of which consisted
of C9orf72 repeat expansion carriers.38,46 The minor allele of rs12608932 was associated with up to 12
months shorter survival for the rare homozygote, which clearly is a clinically relevant effect. Therefore,
UNC13A poses a very interesting therapeutic target, because intervention in the pathogenic mechanism of
UNC13A may prolong survival of ALS patients. The rs12608932 SNP is located in an intron of the UNC13A
gene and most likely tags rare causal variation in or near the UNC13A gene. However, Sanger sequencing
of the exons of UNC13A has not been able to reveal a likely causal variant.47
The UNC13A protein is involved in priming presynaptic vesicles before their release into the synapse. Dis-
ruption of normal UNC13A function may affect the release of neurotransmitters and, subsequently, relat-
ed neuronal network activity.48 Also, aberrant release of (excitatory) neurotransmitters due to abnormal
142
Figure 1
Forest plot of published association results for rs12608932 in UNC13A
in populations of European ancestry
Association results are given for allelic tests. OR = odds ratio. Meta-analysis results for a fixed effect model
are shown. Box sizes are relative to stratum sample sizes. Horizontal lines indicate 95% confidence intervals.
UNC13A function fits the previously described glutamate excitotoxicity hypothesis in ALS.49 Notably, the
only drug with proven effect on disease progression, riluzole, is a glutamate inhibitor.50 Thus, the association
with UNC13A implicates a role for synaptic function and neurotransmitter release as a converging mecha-
nism in the pathogenesis of ALS and FTD.
FUTURE PERSPECTIVES AND CONCLUSIONS
Following the great success stories of genome-wide association studies in common diseases such as type
2 diabetes and Crohn’s disease, the results from the first rush of GWASs in ALS have been slightly under-
whelming. Most of the identified risk variants could not be confirmed in independent replication efforts.
Possible explanations for the lack of consistent findings include the apparent heterogeneity of the ALS
phenotype (e.g. variable age at onset, survival duration, site of onset), underlying population-specific ef-
fects by using multiple international cohorts, and possible type I errors due to small study sizes. To date, the
most significant result has been the association with C9orf72 in the chromosome 9p21.2 locus (which was
known from linkage studies in ALS-FTD pedigrees). Despite increasing sample sizes of the ALS GWASs, the
need for new analysis methods has arisen in order to account for the still largely missing heritability in ALS.
In this thesis, we have explored several of those methods in the search for additional genetic susceptibility
factors for ALS.
By the genetic mapping of gene expression we have identified CYP27A1 as a risk locus for ALS,
although there have been no follow-up studies to confirm this finding. The use of larger sample sizes for
both GWAS and eQTL data sets may improve statistical power and robustness of results in future studies.
A larger study size will, in addition, allow for the detection of small-effect size cis and trans eQTLs. Further-
more, with the emergence of next-generation sequencing techniques, expression profiling may be done
using direct sequencing of mRNA transcripts (RNA-seq), instead of using the current micro-array platforms.
RNA-seq may be able to identify transcripts with expression levels below the detection level of micro-
arrays, and does not depend on a predefined set of probes, thus enabling the detection of unannotated
transcripts.22 Obviously, gene expression profiling in neuronal tissue will be more informative for ALS than
the use of peripheral blood tissue. However, sampling of brain tissue is not feasible during lifetime of ALS
patients, and post-mortem material most likely shows end-stage disease expression profiles, which may
not be representative for ALS pathogenesis. A better source tissue for the genetic mapping of expression
may be motor neurons or other neuronal cell lineages derived from induced pluripotent stem cells from ALS
patients. Furthermore, genomic data may be integrated with other level data, such as protein functioning,
methylation data, or metabolite levels.
The accumulating evidence for UNC13A warrants further fine-mapping of the GWAS association
signal. Exonic mutation screening has, however, not resulted in plausible causal variants.47 Therefore, the
causal variant might lie in intronic sequence or, in view of the discovery of C9orf72, might represent another
repeat expansion. There are up to 100 simple repeats in the UNC13A gene that usually are not well captured
SUMMARY AND GENERAL DISCUSSION
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9
by next-generation sequencing. Thus, a systematic, manual screening of each of these repeats would be
required. Alternatively, the development of single-strand DNA sequencing techniques promises more accu-
rate capturing of structural variation in genomic DNA.
Recently, a GWAS with the largest number of sporadic ALS patients to date has been published,
including 6,100 ALS cases and 7,125 controls, identifying a novel risk locus on chromosome 17q11.2.21
Also, GWASs in other traits like body mass index (BMI) including 339,224 individuals, or adult human
height (253,288 individuals) have demonstrated that an ever increasing sample size may continue to
yield additional associations.51,52 Larger sample sizes may, especially in view of a heterogeneous disease
like sporadic ALS, continue to improve statistical power to detect associations. Furthermore, the field of
complex genetic traits is shifting towards the study of rare variants, which may have larger effect sizes
and further account for the missing heritability in sporadic ALS. Next-generation sequencing techniques
make it possible to assess the presence of rare variants (MAF < 1%) across the genome. However, these
techniques may be less suitable for the detection of structural variation, such as copy number variants or
large repeat expansions. Whole-exome sequencing has already proven fruitful in familial ALS. By analyzing
multiple pedigrees, several novel familial ALS genes have been identified, including VCP, HNRNPA1 and
HNRNPA2B1, PFN1, MATR3, TUBA4A, CHCHD10, and most recently, TBK1.53,54 In the study of sporadic ALS,
this would call for whole-exome or whole-genome association studies instead of the current genome-wide
association studies, which still does not seem feasible because of the required vast sample size (think of
tens of thousands of individuals), the high costs involved, and the massive computing power that would be
needed. As an alternative, a newly developed genotyping array (the Illumina HumanExome Chip) containing
over 250,000 rare variants obtained from whole-exome sequencing experiments may provide a cheaper
way, to genotype genome-wide rare variants albeit less comprehensive.55 Another hybrid solution would
be to perform whole-genome sequencing on a subset of ALS cases and create a custom reference panel,
enriched for ALS-related variants, which may then be used for the imputation of rare variants in a large set
of GWAS samples. This method has proven fruitful previously and, although still requiring large sample sizes,
may reduce costs.56-58
As noted, in order to detect associations for rare variants, even greater sample sizes are required.
Therefore, extensive international collaboration and data sharing becomes more important than ever. It is
worth mentioning here that in 2013 the international Project MinE initiative (www.projectmine.com) has
been started that raises funds for, ultimately, the sequencing of 15,000 ALS genomes and 7,500 control
genomes. The world-famous ALS Ice Bucket Challenge has greatly boosted the awareness of ALS and may
help to improve the funding for such undertakings.
In conclusion, although ever improving genotyping techniques have greatly increased the number
of known genetic causes for familial ALS, genetic studies have, thus far, only been able to explain a small
part of sporadic ALS cases. This thesis has provided evidence for additional susceptibility loci in sporadic
ALS, but it is clear that many of the genetic causes of ALS remain to be discovered. Increasing sample size,
144
studying rare variants and the integration with other level data may identify novel disease-causing genes.
Perhaps even more importantly, the multiple genetic aberrations have to be linked to pathogenic mecha-
nisms leading to motor neuron degeneration. The identification of such pathways may provide therapeutic
targets enabling the development of an effective treatment for patients suffering from this devastating
disease.
THE MAIN CONCLUSIONS OF THIS THESIS ARE:
- ANG K17I mutations may modify penetrance and phenotype in a large pedigree of ALS and FTD,
in the presence of a TARDBP N352S mutation (Chapter 3)
- By genome-wide mapping of gene expression we have identified CYP27A1 as a susceptibility
gene for sporadic ALS (Chapter 4)
- Genome-wide eQTL mapping may provide a valuable approach to prioritize results from
genome-wide association studies (Chapter 4)
- There is no evidence for a shared genetic basis of common variants in ALS and multiple sclerosis
(Chapter 5)
- UNC13A is a shared risk locus for both ALS and frontotemporal dementia (Chapter 6)
- UNC13A modifies survival in ALS patients (Chapter 7)
- Population-based cohorts are preferred in study of genetic variants that modify survival (Chapter 7)
- We confirmed TMEM106B as a disease modifier in C9orf72 repeat expansion carriers (Chapter 8)
SUMMARY AND GENERAL DISCUSSION
145
9
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SUMMARY AND GENERAL DISCUSSION
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NEDERLANDSE SAMENVATTING
(SUMMARY IN DUTCH)
NEDERLANDSE SAMENVATTING (SUMMARY IN DUTCH)
Amyotrofische laterale sclerose (ALS) is een dodelijke ziekte die zich uit in toenemende spierzwakte, dun-
nere spieren (atrofie), spasticiteit en uiteindelijk ademhalingsspierzwakte. De verschijnselen worden ver-
oorzaakt door het afsterven van zenuwcellen die spieren aansturen, die zich zowel in de hersenen als in
het ruggenmerg bevinden. De symptomen beginnen vaak in één deel van het lichaam, denk bijvoorbeeld
aan atrofie van de hand of onduidelijke spraak, en breiden zich vervolgens uit naar andere delen van het
lichaam. Er is geen genezende behandeling voor ALS en patiënten overleden gemiddeld ongeveer drie jaar
na het begin van de klachten. Er is één medicijn, riluzol, waarvan is aangetoond dat het het ziekteproces
kan vertragen met ongeveer 3-6 maanden. Ongeveer 5-10% van de patiënten heeft familiaire ALS (waar-
bij ALS in de familie voorkomt), terwijl de overige patiënten als sporadische ALS worden beschouwd. Bij
familiaire ALS is er meestal een bepaald gendefect bekend dat de ziekte veroorzaakt, terwijl bij sporadische
ALS patiënten waarschijnlijk een samenspel van meerdere omgevingsfactoren en genetische factoren tot
de ziekte leidt. De ziekteverschijnselen van deze twee vormen zijn daarentegen meestal volledig hetzelfde.
Er is veel onderzoek gedaan naar omgevingsfactoren en ALS, maar alleen voor roken lijkt er voldoende
wetenschappelijk bewijs te zijn.
Sinds de ontdekking van het eerste ALS gen (SOD1) in 1993 bij familiaire ALS patiënten zijn er
steeds meer genen ontdekt die familiaire ALS kunnen veroorzaken. De belangrijkste genen die familiaire
ALS veroorzaken in Nederland zijn C9orf72, TARDBP en FUS. In tegenstelling tot familiaire ALS zijn er bij
sporadische ALS patiënten veel minder genetische oorzaken bekend. Een klein deel heeft genafwijkingen
in de bekende familiaire ALS genen (ongeveer 6-15%). Naar de overige oorzaken wordt veel genetisch
onderzoek gedaan. Aanvankelijk gebeurde dat vooral door te kijken naar kandidaatgenen (genen waarvan
men bijvoorbeeld op basis van de functie een rol in ALS zou kunnen verwachten). Met de komst van nieuwe
genetische onderzoekstechnieken werd het mogelijk om met behulp van ‘chips’ honderdduizenden geneti-
sche varianten in het DNA in één experiment te onderzoeken. Dit bood de mogelijkheid om met genoom-
wijde associatie studies (GWAS) te testen of veel voorkomende varianten in het DNA (zogenaamde single
nucleotide polymorphisms of SNP’s) een verhoogd risico op ALS zouden geven. In een GWAS wordt dit
onderzocht door de DNA varianten in een grote groep patiënten te vergelijken met grote aantallen gezonde
controlepersonen. Op deze manier hoeft men niet meer alleen naar één kandidaatgen te kijken, maar kan
men in een keer variaties verspreid over alle genen onderzoeken (hypotheseloos). Er zijn meerdere inter-
nationale genoomwijde associatiestudies gedaan met ALS patiënten, waarbij onder andere associaties met
het gen UNC13A op chromosoom 19 en met een gebied op chromosoom 9 werden gevonden.
In 2010 werd de zeer belangrijke ontdekking gedaan dat een genafwijking in C9orf72 in het gebied
op chromosoom 9 de oorzaak vormt voor ongeveer 37% van familiaire ALS patiënten en 6% van spora-
dische patiënten. Bovendien blijkt deze genafwijking een belangrijke oorzaak te zijn voor het ontstaan van
frontotemporale dementie (FTD). Frontotemporale dementie is een vorm van dementie die, in tegenstelling
tot de ziekte van Alzheimer, niet zozeer gepaard gaat met geheugenverlies, maar vooral tot veranderingen
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in het karakter (ontremming of initiatiefverlies) of taalfuncties leidt. In ongeveer 5-15% van de gevallen
komen ALS en FTD samen voor. Dit proefschrift beschrijft verschillende onderzoeken die gericht zijn op het
ontdekken van nieuwe genetische risicofactoren voor ALS. Er worden verschillende onderzoeksmethoden
toegepast om dit doel te bereiken.
In Hoofdstuk 2 wordt gekeken of genetische variatie in het paraoxonase gen het risico op ALS beïnvloedt in
gebieden met een hoge of juist lage bevolkingsdichtheid. Omdat een van de mogelijke omgevingsrisicofac-
toren voor ALS blootstelling aan pesticiden zou kunnen zijn, is onderzocht of genen die het verwerken van
pesticiden in het lichaam beïnvloeden een rol zouden kunnen spelen bij het ontstaan van ALS. Paraoxonase
is een stof in het lichaam dat pesticiden afbreekt en in eerdere studies werden inderdaad associaties ge-
vonden tussen paraoxonasegenen (PON) en ALS. In Hoofdstuk 2 hebben we variaties in paraoxonasegenen
in 98 ALS uit Zuidoost Engeland onderzocht en tegelijkertijd op basis van postcode en geografische gege-
vens bepaald of patiënten in een gebied met lage of hoge bevolkingsdichtheid woonden. Uit het onderzoek
bleek dat ALS patiënten met een bepaalde variant van het PON1 gen een verhoogd risico hadden op ALS
in het gebied met een lage bevolkingsdichtheid, terwijl dit niet het geval bleek te zijn in gebieden met een
hoge bevolkingsdichtheid. Het onderzoek toonde een gen-omgeving interactie aan tussen variatie in PON1
en bevolkingsdichtheid.
Hoofdstuk 3 beschrijft een grote familie met familiaire ALS waarin een mutatie in angiogenine (ANG) over-
erft met de ziekte. Deze zogenaamde K17I mutatie kwam voor bij alle aangedane familieleden, maar ook in
enkele niet-aangedane familieleden, waarvan de meeste jonger waren dan 50 jaar en dus theoretisch nog
ALS zouden kunnen ontwikkelen. Eén van de patiënten met de ANG K17I mutatie had een afwijkend klach-
tenpatroon, beginnend met parkinsonisme, maar later ALS verschijnselen gecombineerd met gedragsver-
anderingen passend bij FTD.
In Hoofdstuk 4 hebben we genoomwijde DNA variaties (SNP’s) gecombineerd met data van genexpressie.
Genexpressie is een maat voor de activiteit van genen. In dit onderzoek hebben we gekeken naar DNA
varianten die niet alleen mogelijk het risico op ALS vergrootten, maar ook een veranderde genactiviteit
(of genexpressie) teweeg brachten in het bloed van ALS patiënten. Dergelijke varianten zijn waarschijnlijk
belangrijker voor het ontstaan van een ziekte omdat ze ook functionele veranderingen in een lichaams-
weefsel zoals bloed veroorzaken. Voor dit onderzoek hebben we genoomwijde SNP data van in totaal
3.568 sporadische ALS patiënten en 10.163 controles gebruikt en genexpressie data in bloed van 323
ALS patiënten en 413 controles verkregen. Door middel van een interne replicatiestap vonden we dat
SNP’s in het gen CYP27A1 niet alleen geassocieerd zijn met ALS, maar ook zorgen voor een veranderde
expressie van CYP27A1. CYP27A1 is een gen dat betrokken is bij de cholesterolstofwisseling en mutaties in
het gen kunnen een zeldzame neurologische ziekte cerebrotendineuze xanthomatose (CTX) veroorzaken.
NEDERLANDSE SAMENVATTING (SUMMARY IN DUTCH)
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CTX kan zich presenteren met neurologische verschijnselen die lijken op symptomen van ALS. Er zijn ook
aanwijzingen in de literatuur dat vetstofwisseling een rol speelt bij ALS. Deze bevindingen maken CYP27A1
een interessante kandidaat voor een risicoverhogend gen bij ALS. Idealiter wordt onze bevinding bevestigd
in een ander onafhankelijk onderzoek, maar dit is tot nu toe nog niet gebeurd.
In Hoofdstuk 5 en 6 wordt gekeken naar genetische overlap tussen ALS en twee andere neurologische
ziektebeelden: multipele sclerose (MS) en frontotemporale dementie (FTD). Eventuele genetische overlap
kan meer inzicht bieden in ziektemechanismen die in beide ziektebeelden een rol kan spelen. We hebben
hiervoor ten eerste genoomwijde associatiestudies van ALS (3.762 patiënten en 4.886 controles) gecom-
bineerd met die van 4.088 MS patiënten en 7.144 gezonde controles (Hoofdstuk 5). Na meta-analyse van
de gegevens vonden we geen aanwijzingen voor gedeelde genetische risicofactoren tussen ALS en MS. De
genetische risicofactoren voor MS werden niet gedragen door ALS en vice versa.
In hoofdstuk 6 hebben we genoomwijde SNP data van in totaal 4.377 ALS patiënten en 13.017
controles gecombineerd met een GWAS van 435 FTD-patiënten (allen met na obductie bewezen FTD
kenmerken in de hersenen) en 1.414 controles. In een meta-analyse zagen we dat door toevoeging van
de FTD patiënten aan de ALS data, niet alleen het eerdere associatiesignaal op chromosoom 9 sterk toe-
nam, maar ook het signaal van UNC13A op chromosoom 19 nam sterk toe. Vanwege het grote verschil in
aantallen ALS patiënten en FTD patiënten in de studie, hebben we nog twee methoden toegepast, waar-
mee we hebben laten zien dat de toegenomen associatiesignalen niet alleen door ALS, maar ook door
FTD gedreven werden. Het onderzoek beschrijft voor het eerst een verband tussen genetische variatie in
UNC13A en frontotemporale dementie. UNC13A is betrokken bij de regulatie van afgifte van signaalstoffen
(neurotransmitters) tussen zenuwcellen en een verstoorde werking van UNC13A kan, door verandering
van zenuwnetwerken in de hersenen, leiden tot schade aan zenuwcellen. Een van deze veranderingen kan
zijn dat de neurotransmitter glutamaat verhoogd wordt afgegeven en door ‘overprikkeling’ schade kan aan-
richten aan zenuwcellen in de hersenschors (excitotoxiciteit). Deze overprikkeling door glutamaat is een
mechanisme waarvoor reeds aanwijzingen bestaan in eerdere literatuur. Verder is hier vermeldenswaardig
dat riluzol, het enige medicijn met bewezen effect bij ALS, een glutamaatremmer is. Het is echter niet be-
kend hoe riluzol het beloop van ALS vertraagt.
In Hoofdstuk 7 wordt de rol van UNC13A in ALS verder onderzocht door te kijken naar een mogelijke relatie
met debuutleeftijd van ALS symptomen of een effect op overleving. Hiervoor hebben we 450 ALS patiënten
en 524 controlepersonen geselecteerd uit een prospectief cohortonderzoek naar ALS en een genetische
variant in UNC13A onderzocht. Een SNP in UNC13A bleek geassocieerd met ALS in dit nieuwe cohort. Bo-
vendien hebben we aangetoond dat genetische variatie in UNC13A geassocieerd is met de overlevingsduur
van ALS patiënten. Deze laatste bevinding bleek ook te gelden voor in totaal 1.767 ALS patiënten en 1.817
controles uit Nederland, België en Zweden die aan een eerder gepubliceerd GWAS hadden meegedaan. We
154
vonden geen relatie tussen UNC13A en de debuutleeftijd van ALS symptomen. De bevinding dat UNC13A is
geassocieerd met overleving bij ALS patiënten is bevestigd in een Italiaans onderzoek en kan ook een goed
aangrijpingspunt vormen voor een eventuele behandeling. Hiervoor zal er echter eerst meer inzicht moeten
worden verkregen in de rol die UNC13A speelt in het ziektemechanisme van ALS.
Een genetische afwijking in het gen C9orf72 kan zowel ALS als FTD veroorzaken en is een veelvoorkomen-
de oorzaak bij beide ziektebeelden. De functie van C9orf72 in het zenuwstelsel is vooralsnog onbekend. In
Hoofdstuk 8 is onderzocht of er bij dragers van de genafwijking in C9orf72 andere genetische varianten zijn
die bepalen of iemand vooral ALS symptomen of juist verschijnselen van FTD krijgt. We hebben hiervoor
zoveel mogelijk ALS en FTD patiënten met een genafwijking in C9orf72 verzameld van wie ook GWAS data
beschikbaar was. Door een wereldwijde samenwerking hebben we 402 ALS patiënten en 253 FTD patiën-
ten met een C9orf72 genafwijking en genoomwijde SNP data kunnen verzamelen. We hebben hierbij geen
nieuwe varianten gevonden die bepalen of een persoon ALS of juist FTD verschijnselen ontwikkelt. Eerdere
onderzoeken bij dragers van de C9orf72 genafwijking hebben door kandidaatgenen te bekijken gevonden
dat het gen TMEM106B en een variant in ataxine-2 (ATXN2) het ontstaan van ALS of FTD kan beïnvloeden.
Hiervoor vonden wij ook aanwijzingen in onze data, alhoewel deze effecten minder sterk lijken. In de toe-
komst kan, door verdere internationale samenwerking, een grotere groep ALS en FTD patiënten met een
C9orf72 genafwijking worden verzameld en wordt de kans vergroot dat er genetische varianten gevonden
kunnen worden die het optreden van ALS of FTD kunnen beïnvloeden.
In Hoofdstuk 9 worden de bevindingen van de onderzoeken in dit proefschrift samengevat en in een bredere
context geplaatst.
NEDERLANDSE SAMENVATTING (SUMMARY IN DUTCH)
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DANKWOORD
DANKWOORD
Promoveren doe je niet alleen en ik wil dan ook iedereen danken die mij heeft ondersteund bij het tot stand
laten komen van dit proefschrift.
Allereerst gaat mijn dank uit naar mijn promotoren prof. dr. L.H. van den Berg en prof. dr. J.H. Veldink. Beste
Leonard, het is een voorrecht geweest om onder jouw hoede te kunnen promoveren. Je hebt alle vrijheid
gegeven en de gelegenheid geboden om onderzoek te doen op wereldniveau. Je enthousiasme, creativiteit
en bovenal humor zijn een enorme inspiratiebron geweest en met de gevleugelde woorden “is het al af?”
heb je me steeds weer weten te stimuleren. Ik heb bewonderd hoe je altijd de prioriteiten en de hoofdlijn
binnen een onderzoeksproject haarscherp voor ogen houdt, wat de kwaliteit van de artikelen sterk ten
goede kwam.
Beste Jan, je begeleiding is enorm inspirerend en bovenal leerzaam geweest. Je indrukwekkende statisti-
sche kennis heeft me enorm geholpen om complexe bergen aan data succesvol te analyseren. Ik ben altijd
onder de indruk geweest van het complexity distortion field waarmee je een hidden Markov model weet
uit te leggen en de modelrol die je vervult tijdens de ALS congressen. Tenslotte heb je me door je onuitput-
telijke bron van ideeën laten inzien dat de uitspraak “komt toch niets uit…” nooit zal bijdragen aan nieuwe
baanbrekende onderzoeksresultaten.
Ik wil de leden van de beoordelingscommissie, prof. dr. P.I.W. de Bakker, prof. dr. L. Franke, prof. dr. E.M. Hol,
prof. dr. J.K. Ploos van Amstel, prof. dr. G.J.E. Rinkel, prof. dr. J.C. van Swieten, danken voor het verdiepen in
mijn proefschrift en de gunstige beoordeling.
Mijn promotietraject is doorvlochten geweest met klinische stages in het kader van mijn opleiding tot
neuroloog. Ik wil mijn opleiders, prof. dr. J.H.J. Wokke en dr. T. Seute danken voor de mooie neurologie op-
leiding die ik in het UMC Utrecht mag genieten naast mijn promotie onderzoek. Ik kijk uit naar de komende
jaren in de kliniek.
Beste collega’s van de Neurologie, bedankt voor alle gezelligheid en collegialiteit die onze assistentengroep
biedt, zowel op de werkvloer als daarbuiten tijdens de borrels, assistentenweekenden en natuurlijk de Ba-
binski!
Mijn dank gaat uit naar alle medewerkers van het ALS centrum die het onderzoek in mijn proefschrift
mogelijk hebben gemaakt. In het bijzonder wil ik Nienke en Inge bedanken dankzij wie de polikliniekdagen
soepel verlopen en die een houvast vormen voor patiënten met ALS.
158
Ik wil de co-auteurs danken voor hun waardevolle wetenschappelijke input tijdens de onderzoeksprojecten
en bij het schrijven van de artikelen. Prof. dr. J. Pasterkamp, Jeroen, dank je voor je functioneel biologische
aanvullingen op mijn manuscripten en input tijdens labbesprekingen.
I would like to thank all co-authors for their valuable input during my research projects and the writing of
the article manuscripts. I would like to thank Prof. Ammar Al-Chalabi from the MRC Centre for Neurodege-
neration Research at King’s College London for the opportunity I had to do my 6-month research project as
a visiting student and the fruitful collaboration we had in further joint research projects.
Beste Peter, Raymond en Jelena, dank voor jullie onmisbare hulp in het lab. Van het optimaliseren van Taq-
Mans tot het immer uitbreiden van de hoeveelheid DNA platen, jullie hulp is onmisbaar geweest!
Alle labgenoten van het Lab Experimentele Neurologie, Wouter, Perry, Oliver, Ewout, Max, Gijs, Lotte, Mei-
nie, Annelot, Anna, Dianne, Marc, Sandra, ik heb genoten van onze bijzonder hechte groep. Wouter, Perry,
Oliver, dank voor de scripting tips, veronthelderende discussies, Scootersessies, het op de kaart zetten
van Roosendaal en het verkennen van Dublin en Sheffield by night. Ewout, dank voor het bewaken van de
10.00u – 11.30u – 15.00u momenten, ik neem aan dat dit in Edinborough ook lukt? Anna, je wortel-walno-
tentaart is werkelijk onovertroffen. Paul, Michael, Hylke en Christiaan, a.k.a. de “ALS boys”, jullie hebben de
toon gezet voor een hechte en gezellige onderzoeksgroep en hebben een voorbeeldfunctie gevormd voor
het doen van kwalitatief hoogwaardig onderzoek.
Ik wil ook alle andere neuromusculaire onderzoekers, Sonja, Nadia, Renske, Marloes, Renée, Henk-Jan,
Mark, Anne, Camiel, Bas danken voor de gezellige koffiemomenten, al denk ik dat echte mannen dan ook
iets meer koffie mogen drinken.
Studenten Peter en Femke, dank voor jullie hulp bij DNA pipetteren, uitplaten, ophalen in Leuven of weg-
brengen naar Rotterdam! Die OV-jaarkaart is er goed uitgehaald!
Mijn paranimfen, Wouter Boer en Henk-Jan Prins, jullie hebben mijn onderzoeksperikelen kunnen volgen
vanaf het eerste gelletje trekken in mijn studententijd tot aan het afronden van mijn promotie. Dank jullie
voor het aan mijn zijde staan tijdens de verdediging! En, Henk-Jan, succes met de afronding van je eigen
proefschrift.
Familie en vrienden, Reina, Anne Fleur, bedankt voor het geduldig aanhoren van mijn onbegrijpelijke uitleg
over snips, geewassen, unken en orfen, het begrip voor momenten dat een onderzoeksdeadline even voor
ging, maar ook alle welkome gezellige afleiding.
DANKWOORD
159
Mijn lieve zusje, Meta, ik bewonder je humor en doorzettingsvermogen en ik had nooit gedacht dat je on-
danks al mijn onderzoeksfrustraties later zou eindigen in een geneticalab voor je eigen promotie onderzoek,
maar ik ben maar wat trots! Wellicht is het iets wat dominant overerft… Veel succes met de laatste loodjes
van je onderzoek en uiteraard met Mr. Darcy!
Lieve pap en mam, zonder jullie was ik nooit zover gekomen. Bedankt voor jullie onvoorwaardelijke steun
en geduld, zelfs in het afgelopen jaar. Pap, ik ben zo blij dat je erbij kunt zijn!
Mijn liefste Fre, het leven van de promovendus bestaat niet alleen uit rozen en ik dank je voor je steun en het
geduld dat je hebt gehad in de afgelopen onderzoeksjaren, vooral wanneer er tot laat aan een manuscript
gewerkt werd. Dank je bovenal voor alles waarvan we samen zo kunnen genieten, ik hoop dat daar nog
veel meer van gaat komen!
Frank.
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DANKWOORD
161
LIST OF PUBLICATIONS
LIST OF PUBLICATIONS
THIS THESIS
van Es MA, Diekstra FP, Veldink JH, Baas F, Bourque PR, Schelhaas HJ, Strengman E, Hennekam EAM,
Lindhout D, Ophoff RA, van den Berg LH. A case of ALS-FTD in a large FALS pedigree with a K17I ANG
mutation. Neurology. 2009;72:287–288.
Diekstra FP, Beleza-Meireles A, Leigh NP, Shaw CE, Al-Chalabi A. Interaction between PON1 and population
density in amyotrophic lateral sclerosis. NeuroReport. 2009;20:186–190.
Diekstra FP, Saris CGJ, Van Rheenen W, Franke L, Jansen RC, van Es MA, van Vught PWJ, Blauw HM, Groen
EJN, Horvath S, Estrada K, Rivadeneira F, Hofman A, Uitterlinden AG, Robberecht W, Andersen PM, Melki J,
Meininger V, Hardiman O, Landers JE, Brown RH, Shatunov A, Shaw CE, Leigh PN, Al-Chalabi A, Ophoff RA,
van den Berg LH, Veldink JH. Mapping of gene expression reveals CYP27A1 as a susceptibility gene for
sporadic ALS. PLoS ONE. 2012;7:e35333.
Diekstra FP, van Vught PWJ, Van Rheenen W, Koppers M, Pasterkamp RJ, van Es MA, Schelhaas HJ, de
Visser M, Robberecht W, Van Damme P, Andersen PM, van den Berg LH, Veldink JH. UNC13A is a modifier
of survival in amyotrophic lateral sclerosis. Neurobiol Aging. 2012;33:630.e3–8.
Goris A, van Setten J, Diekstra F, Ripke S, Patsopoulos NA, Sawcer SJ, International Multiple Sclerosis Ge-
netics Consortium, van Es M, Australia and New Zealand MS Genetics Consortium, Andersen PM, Melki J,
Meininger V, Hardiman O, Landers JE, Brown RH, Shatunov A, Leigh N, Al-Chalabi A, Shaw CE, Traynor BJ,
Chiò A, Restagno G, Mora G, Ophoff RA, Oksenberg JR, Van Damme P, Compston A, Robberecht W, Dubois
B, van den Berg LH, de Jager PL, Veldink JH, de Bakker PIW. No evidence for shared genetic basis of com-
mon variants in multiple sclerosis and amyotrophic lateral sclerosis. Hum Mol Genet. 2014;23:1916–1922.
Diekstra FP, Van Deerlin VM, van Swieten JC, Al-Chalabi A, Ludolph AC, Weishaupt JH, Hardiman O, Landers
JE, Brown RH, van Es MA, Pasterkamp RJ, Koppers M, Andersen PM, Estrada K, Rivadeneira F, Hofman A,
Uitterlinden AG, Van Damme P, Melki J, Meininger V, Shatunov A, Shaw CE, Leigh PN, Shaw PJ, Morrison KE,
Fogh I, Chiò A, Traynor BJ, Czell D, Weber M, Heutink P, de Bakker PIW, Silani V, Robberecht W, van den Berg
LH, Veldink JH. C9orf72 and UNC13A are shared risk loci for amyotrophic lateral sclerosis and frontotem-
poral dementia: a genome-wide meta-analysis. Ann Neurol. 2014;76:120–133.
164
Diekstra FP, Nalls MA, Van Deerlin VM, Ferrari R, van Es MA, van Swieten JC, Heutink P Shatunov A, Al-Cha-
labi A, Hardiman O, Shaw PJ, Morrison KE, van Damme P, Robberecht W van der Zee J, van Broekhoven C,
Brice A, Le Ber I, Graff C, Pickering-Brown S, Chiò A, Singleton AB, Traynor BJ, Hardy J, Rademakers R, van
den Berg LH, Veldink JH. Genetic modifiers in C9orf72 repeat expansion carriers: a genome-wide analysis.
Manuscript in preparation.
OTHER PUBLICATIONS
Landers J, Shi L, Cho T, Glass J, Shaw C, Nigel Leigh P, Diekstra F, Polak M, Rodriguez-Leyva I, Niemann S,
Traynor B, McKenna-Yasek D, Sapp P, Al-Chalabi A, Wills A, Brown R. A common haplotype within the PON1
promoter region is associated with sporadic ALS. Amyotroph Lateral Scler. 2008;10:1–9.
Landers JE, Melki J, Meininger V, Glass JD, van den Berg LH, van Es MA, Sapp PC, van Vught PWJ, McK-
enna-Yasek DM, Blauw HM, Cho T-J, Polak M, Shi L, Wills A-M, Broom WJ, Ticozzi N, Silani V, Ozoguz A,
Rodriguez-Leyva I, Veldink JH, Ivinson AJ, Saris CGJ, Hosler BA, Barnes-Nessa A, Couture N, Wokke JHJ,
Kwiatkowski TJ, Ophoff RA, Cronin S, Hardiman O, Diekstra FP, Nigel Leigh P, Shaw CE, Simpson CL, Hansen
VK, Powell JF, Corcia P, Salachas F, Heath S, Galan P, Georges F, Horvitz HR, Lathrop M, Purcell S, Al-Chalabi
A, Brown RH. Reduced expression of the Kinesin-Associated Protein 3 (KIFAP3) gene increases survival in
sporadic amyotrophic lateral sclerosis. Proc Natl Acad Sci USA. 2009;106:9004–9009.
Blauw HM, Al-Chalabi A, Andersen PM, van Vught PWJ, Diekstra FP, van Es MA, Saris CGJ, Groen EJN, Van
Rheenen W, Koppers M, van’t Slot R, Strengman E, Estrada K, Rivadeneira F, Hofman A, Uitterlinden AG,
Kiemeney LA, Vermeulen SHM, Birve A, Waibel S, Meyer T, Cronin S, McLaughlin RL, Hardiman O, Sapp PC,
Tobin MD, Wain LV, Tomik B, Slowik A, Lemmens R, Rujescu D, Schulte C, Gasser T, Brown RH, Landers JE,
Robberecht W, Ludolph AC, Ophoff RA, Veldink JH, van den Berg LH. A large genome scan for rare CNVs in
amyotrophic lateral sclerosis. Hum Mol Genet. 2010;19:4091–4099.
van Es MA, Schelhaas HJ, van Vught PWJ, Ticozzi N, Andersen PM, Groen EJN, Schulte C, Blauw HM, Kop-
pers M, Diekstra FP, Fumoto K, LeClerc AL, Keagle P, Bloem BR, Scheffer H, van Nuenen BFL, Van Blitterswijk
M, Van Rheenen W, Wills A-M, Lowe PP, Hu G-F, Yu W, Kishikawa H, Wu D, Folkerth RD, Mariani C, Goldwurm
S, Pezzoli G, Van Damme P, Lemmens R, Dahlberg C, Birve A, Fernández-Santiago R, Waibel S, Klein C,
Weber M, Van Der Kooi AJ, de Visser M, Verbaan D, van Hilten JJ, Heutink P, Hennekam EAM, Cuppen E,
Berg D, Brown RH, Silani V, Gasser T, Ludolph AC, Robberecht W, Ophoff RA, Veldink JH, Pasterkamp RJ, de
Bakker PIW, Landers JE, van de Warrenburg BP, van den Berg LH. Angiogenin variants in Parkinson disease
and amyotrophic lateral sclerosis. Ann Neurol. 2011;70:964–973.
LIST OF PUBLICATIONS
165
Groen EJN, Van Rheenen W, Koppers M, van Doormaal PTC, Vlam L, Diekstra FP, Dooijes D, Pasterkamp RJ,
van den Berg LH, Veldink JH. CGG-repeat expansion in FMR1 is not associated with amyotrophic lateral
sclerosis. Neurobiol Aging. 2012;33:1852.e1–e3.
Ahmeti KB, Ajroud-Driss S, Al-Chalabi A, Andersen PM, Armstrong J, Birve A, Blauw HM, Brown RH, Brui-
jn L, Chen W, Chiò A, Comeau MC, Cronin S, Diekstra FP, Soraya Gkazi A, Glass JD, Grab JD, Groen EJ,
Haines JL, Hardiman O, Heller S, Huang J, Hung W-Y, ITALSGEN Consortium, Jaworski JM, Jones A, Khan
H, Landers JE, Langefeld CD, Leigh PN, Marion MC, McLaughlin RL, Meininger V, Melki J, Miller JW, Mora
G, Pericak-Vance MA, Rampersaud E, Robberecht W, Russell LP, Salachas F, Saris CG, Shatunov A, Shaw
CE, Siddique N, Siddique T, Smith BN, Sufit R, Topp S, Traynor BJ, Vance C, Van Damme P, van den Berg LH,
van Es MA, Van Vught PW, Veldink JH, Yang Y, Zheng JG, ALSGEN Consortium. Age of onset of amyotrophic
lateral sclerosis is modulated by a locus on 1p34.1. Neurobiol Aging. 2013;34:357.e7–19.
Van Rheenen W, Diekstra FP, van Doormaal PTC, Seelen M, Kenna K, McLaughlin R, Shatunov A, Czell D, van
Es MA, van Vught PWJ, Van Damme P, Smith BN, Waibel S, Schelhaas HJ, Van Der Kooi AJ, de Visser M,
Weber M, Robberecht W, Hardiman O, Shaw PJ, Shaw CE, Morrison KE, Al-Chalabi A, Andersen PM, Ludolph
AC, Veldink JH, van den Berg LH. H63D polymorphism in HFE is not associated with amyotrophic lateral
sclerosis. Neurobiol Aging. 2013;34:1517.e5–e7.
Fogh I, Ratti A, Gellera C, Lin K, Tiloca C, Moskvina V, Corrado L, Sorarù G, Cereda C, Corti S, Gentilini D,
Calini D, Castellotti B, Mazzini L, Querin G, Gagliardi S, Del Bo R, Conforti FL, Siciliano G, Inghilleri M, Saccà
F, Bongioanni P, Penco S, Corbo M, Sorbi S, Filosto M, Ferlini A, Di Blasio AM, Signorini S, Shatunov A, Jones
A, Shaw PJ, Morrison KE, Farmer AE, Van Damme P, Robberecht W, Chiò A, Traynor BJ, Sendtner M, Melki
J, Meininger V, Hardiman O, Andersen PM, Leigh NP, Glass JD, Overste D, Diekstra FP, Veldink JH, van Es
MA, Shaw CE, Weale ME, Lewis CM, Williams J, Brown RH, Landers JE, Ticozzi N, Ceroni M, Pegoraro E,
Comi GP, D’Alfonso S, van den Berg LH, Taroni F, Al-Chalabi A, Powell J, Silani V, SLAGEN Consortium and
Collaborators. A genome-wide association meta-analysis identifies a novel locus at 17q11.2 associated
with sporadic amyotrophic lateral sclerosis. Hum Mol Genet. 2014;23:2220–2231.
Smith BN, Ticozzi N, Fallini C, Gkazi AS, Topp S, Kenna KP, Scotter EL, Kost J, Keagle P, Miller JW, Calini D,
Vance C, Danielson EW, Troakes C, Tiloca C, Al-Sarraj S, Lewis EA, King A, Colombrita C, Pensato V, Cas-
tellotti B, de Belleroche J, Baas F, Asbroek ten ALMA, Sapp PC, McKenna-Yasek D, McLaughlin RL, Polak M,
Asress S, Esteban-Pérez J, Muñoz-Blanco JL, Simpson M, SLAGEN Consortium, Van Rheenen W, Diekstra
FP, Lauria G, Duga S, Corti S, Cereda C, Corrado L, Sorarù G, Morrison KE, Williams KL, Nicholson GA, Blair
IP, Dion PA, Leblond CS, Rouleau GA, Hardiman O, Veldink JH, van den Berg LH, Al-Chalabi A, Pall H, Shaw
PJ, Turner MR, Talbot K, Taroni F, García-Redondo A, Wu Z, Glass JD, Gellera C, Ratti A, Brown RH, Silani
V, Shaw CE, Landers JE. Exome-wide rare variant analysis identifies TUBA4A mutations associated with
familial ALS. Neuron. 2014;84:324–331.
166
Van Rheenen W, Diekstra FP, van den Berg LH, Veldink JH. Are CHCHD10 mutations indeed associated with
familial amyotrophic lateral sclerosis? Brain. 2014;137:e313.
Cats EA, van der Pol W-L, Tio-Gillen AP, Diekstra FP, van den Berg LH, Jacobs BC. Clonality of anti-GM1 IgM
antibodies in multifocal motor neuropathy and the Guillain-Barré syndrome. J Neurol Neurosurg Psychiatr.
2015;86:502–504.
Kremer PHC, Koeleman BPC, Rinkel GJE, Diekstra FP, van den Berg LH, Veldink JH, Klijn CJM. Susceptibility
loci for sporadic brain arteriovenous malformation; a replication study and meta-analysis. J Neurol Neurosurg
Psychiatr. Accepted for publication.
SUBMITTED
De Muynck L, Diekstra FP, Borroni B, Medic J, Thijs V, Camuzat A, Van Den Bosch L, van den Berg LH,
Robberecht W, Le Ber I, Ravits J, Veldink JH, Van Damme P. C9orf72 expression levels modify survival in
sporadic and familial ALS. Submitted.
van Doormaal PTC, Diekstra FP, van den Heuvel DMA, van Rheenen W, Overste D, Dekker AM, Schellevis RD,
van Damme P, de Bakker PIW, Francioli LC, Pasterkamp RJ, van den Berg LH, Veldink JH. The role of de novo
mutations in the development of sporadic amyotrophic lateral sclerosis. Submitted.
van Rheenen W, Shatunov A, Dekker AM, McLaughlin RL, Diekstra FP, Pulit SL, de Jong S, Andres CR,
van Doormaal PTC, Tazelaar GH, Koppers M, Blokhuis AM, Sproviero W, Jones A, Kenna KP, van Eijk KR,
Harschnitz O, Robinson MR, Vosa U, Medic J, Schellevis R, Brands W, Menelaou A, Rogelj B, Millechamps S,
de Carvalho M, Mora JS, Rojas-García R, Chandran S, Colville S, Morrison K, Shaw PJ, Hardy J, Orrell RW,
Petri S, Sendtner M, Meyer T, Staats KA, Ophoff RA, Van Deerlin VM, Basak N, Parman Y, Uitterlinden AG,
Rivadeneira F, Estrada K, Hofman A, Curtis C, Blauw HM, de Visser M, van der Kooi AJ, Goris A, Weber M,
Shaw CE, Smith BN, Fogh I, Silani V, Powell J, SLAGEN consortium, FALS sequencing consortium, Casale
F, Chio A, Beghi E, Pupillo E, Logroscino G, Yang J, Wray NR, Visscher P, Franke L, Ludolph AC, Weishaupt J,
Robberecht W, van Damme P, Brown Jr RH, Landers JE, Hardiman O, Andersen PM, Corcia P, Vourch P, de
Bakker PIW, Pasterkamp JR, van Es MA, Lewis C, Breen G, Al-Chalabi A, van den Berg LH, Veldink JH. Novel
risk variants and genetic architecture in amyotrophic lateral sclerosis. Submitted.
167
LIST OF PUBLICATIONS
CURRICULUM VITAE
CURRICULUM VITAE
Frank Paul Diekstra werd geboren op 4 augustus 1983 te Nijmegen. Hij behaalde zijn VWO-diploma aan
het Stedelijk Gymnasium te Nijmegen in 2001. In hetzelfde jaar begon hij aan de studie Geneeskunde aan
de Universiteit Utrecht. Tijdens zijn studie deed hij wetenschappelijk onderzoek bij het laboratorium voor
experimentele Neurologie in het UMC Utrecht onder begeleiding van dr. Michael van Es en prof. dr. Leonard
van den Berg. In laatste jaar van zijn studie deed hij een half jaar onderzoek aan het MRC Centre for Neuro-
degeneration Research van het Institute of Psychiatry van het King’s College in Londen onder begeleiding
van prof. Ammar Al-Chalabi. Na het afleggen van zijn artsexamen in de zomer van 2008 werd hij in 2009
aangenomen voor de opleiding Neurologie in het UMC Utrecht. Hij keerde terug naar het laboratorium
experimentele Neurologie voor zijn promotieonderzoek naar genetische risicofactoren voor ALS onder su-
pervisie van prof. Leonard van den Berg en prof. dr. Jan Veldink, waarvan de resultaten zijn beschreven in
dit proefschrift.
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171
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