Physiological Genomics

from Rats to Human

Monika Stoll, Ph.D

Director, Genetic Epidemiology of vascular disordersLeibniz-Institute for Arteriosclerosis Research, Münster

Genetic Variation influences

- disease susceptibility- disease progression- therapeutic response- unwanted drug effects

The use of genetic variation for diagnosticpurposes and targeted treatment

Genome-oriented Medicine

“Heterogeneity “ of complex diseases

complexphenotype

“polygenic with genetic Heterogeneity”

“Environmental factors”

Salt intake

PsychosocialStress

Diet

others

Gene+

Gene +Gene -Gene+

Gene+

Gene -

Epistasis

Gene-environment interactions and CVD

Genetic factors

Diet, Smoking, Stress

Hypertension, Diabetes, Obesity,Age, Lipids, Genetic Background

Atherosclerosis

Myocardial infarction

Stroke Peripheralvasculardisease

Environment

Risk factors

Trait

Phenotype

Complex Diseases do not have a clear phenotype but may or may

not share some featuresExample: metabolic syndrome (syndrome X)

hypertension

hyperglycemia

dislipidemia

obesity

athero-sclerosis

Insulin resistance

vascular disease

Polygenic:modest effects of single genes

Incomplete penetranceAge-of-onsetEnvironmental componentGenetic Heterogeneity

High Complexity

DifficultiesDisease Etiology

Family studies/ Sib-Pair Analysis:large number of patients(2,500 sibpairs)Modest resolution

Multiple Genes:Interaction, Epistasis

Lack of Power

DifficultiesHuman Linkage Analysis

Genetics of Multifactorial Diseases

Large scale association studies

Transmission Disequilibrium TestsSib - TDT

Association studies on quantitativetraits

Increased statistical powerHigh density typing necessary

Animal models e.g. rat

Controlled genetic backgroundControlled environmentControlled experimental settingLarge number of progenies

Decreased heterogeneityProvide candidate regions

SolutionsReduction of complexity

SolutionsAssociation studies

Comparative Maps

Positional candidate loci for high density genotyping

Genetics of Multifactorial Diseases

Comparative Genomics with Biology Human

Mouse Rat

Genes, Physiology and Pharmacologyrelevant to human diseaseGenes, Physiology and Pharmacologyrelevant to human disease

Genes and Genetic Manipulation relevant to human diseaseGenes and Genetic Manipulation relevant to human disease

Ability to avoid many biological barriersunique to one speciesAbility to avoid many biological barriersunique to one species

Why ‚Comparative Genomics‘?

Take advantage of the wealth of genome informationfrom the various Genome Projects

Genomic regions are evolutionary conserved between mammalian species(Synteny)

Sequence is highly conserved between species(Homology)

The genomic sequence of human, rat and mouse genomes are available

QTLs/Genes identified in rodent models are predictive for human loci

Rodent models can help to elucidate the function of novel disease genese.g. implicated by human linkage studies or expression profiling

Strategies for ‚comparative genomics‘• Map ‚novel‘ genes identified e.g. in expression profiling and

anchor on existing comparative maps (www.rgd.edu/VCMap)

• Sequence positional candidate genes in mouse, rat and human to identify conserved mutations and/or regulatory elements

• Predict potential target regions for human linkage studies based on model organisms

• Characterize candidate genes from human studies in representative experimental model (inbred strains, congenics, transgenics, conditional knock-outs)

Experimentelles Modell Monogene Erkrankung

Geschwisterpaar-Untersuchungen:Bestätigung Kandidatengen-Locus

Assoziationsstudien:Identifizierung von Kandidatengen-Polymorphismen

(polygene) komplexe Erkrankung

Cross design

SHR-SP SHR or WKY

F1Backcross

F2

F2

x

SHR-SP SHR or WKY

F1

Human Chromosome Regions Implicated inHypertension via a Cross-Species Comparison

Blood Pressure Phenotypes

27 independent blood pressure phenotypes

• Baseline Blood Pressure

• Maximal Response

• MAP, DBP, SBP, PP

• MAP, DBP, SBP, PP after salt-load

• Drug Challenges

• Delta BPs

Rat Models for Genetic Hypertension

SHR x WKYSHR x DNYSHR x BN

GH x BN

SS x BN

LH x LN

Spontaneously Hypertensive Rat (SHR)High blood pressure Cardiovascular disease

Genetically Hypertensive Rat (GH)Hypertension, cardiac hypertrophyVascular disease, not salt-sensitive

Dahl Salt-Sensitive Rat (SS)Salt-sensitive hypertensionHyperlipidemia, insulin resistance

Lyon Hypertensive Rat (LH)Mild hypertension, hyperlipidemia

Fawn-hooded Hypertensive Rat (FHH)Systolic hypertensionRenal failure

FHH x ACI

Linkage Analysis for Blood Pressure QTLs

Independent total genome scans in 7 intercrosses representing a model for genetic hypertension

200-300 SSLP markers10-20 cM spacing57- 390 animals

Linkage analysis using MAPMAKER/QTL computer package

LOD score >2.8 suggestiveLOD score >4.3 significant

Integration of QTLs on integrated map based on genotyping information from crosses used for linkage analysis

QTL #1 QTL #2 QTL #3

QTL cluster

Drop of 1.6 LODunits =95% confidenceinterval

Analysis of QTL Clustering

Establishment of Syntenic Regions in Human Genome Identification of syntenic regions and evolutionary breakpoints using comparative maps between rat,

mouse and human

Definition of positional candidate regions in human genome

based on QTLs identified in rat models of hypertension

Designation of ‘first priority’ and ‘second priority’ regions

first priority region

based on QTLs frommultiple rat crosses

second priority region

based on QTLs from single rat cross

QTLs identified in Rat

LOD score > 4.3 13LOD score 2.8-4.3 44LOD score 2.5-2.8 11

68 blood pressure QTLs total

13 QTL clusters total7 QTL clusters 2 or more crosses6 QTL clusters within one cross10 single QTLs

Baseline BP 2Max. response 7MAP, DBP, SBP, PP 19Salt MAP, DBP, SBP, PP 22Drug challenge 7Delta BP 11

Coverage of rat genome in cM500 cM (31%)

First priority regionsSecond priority regions

Syntenic Regions in Human

36 syntenic regions total

highest: 7 regions (14 QTLs)high: 20 regions (38 QTLs) moderate: 5 regions (10 QTLs)conversion incomplete or impossible 6 QTLs

23 ‘first priority’ regions13 ‘second priority’ regions

Coverage of human genome in cM~800 cM (~24%)

Classification Confidence level

Identification of Syntenic Regions and Evolutionary Breakpoints

Framework comparative maps

RATMAP serverhttp://ratmap.gen.gu.seOxford Mapshttp://www.well.ox.ac.ukMIT Mapshttp://www.genome.wi.mit.edu/rat/

RATMAP serverMouse Genome Databasehttp://www.informatixs.jax.orgUniGene http://www.ncbi.nlm.nih.gov/UniGene/index.htmlGenome Databasehttp://gdbwww.gdb.org

Identify homologous genes mappedin rat, mouse and/or human

Preliminary comparative maps of genes in common on the genetic maps of rat and mouse

Preliminary comparative maps of genes in common on the genetic maps of mouse and human

VC-MAP : Bioinformatics-‘Tool‘ for comparative maps

Stoll et al., Genome Res. 10: 473 – 482, 2000http://www.genome.org/cgi/content/full/10/4/473Free access

Kwitek et al. Genome Res. 11: 1935 – 1943, 2001http://www.genome.org/cgi/content/full/11/11/1935Free access

www.rgd.mcw.edu

Comparative Mapping

Human chr. 22 and its homologies to rat chr. 11, 20, 6, 14 and 7

D HS - LS SBPD HS - LS MAP HS basaler DBP HS aktiver MAP Tag 2 DBP TPM Alpha2HS Prot ExcrHDL

D18Rat85

D18Mgh3

D18Rat9

GJA1, D18Mit16D18Mit8

D18Rat57D18Rat18D18Mit5

D18Mgh9D18Mgh7

1.5

19.0

cM

6.7

D18Mit3D18Mit14, D18Mgh8

D18Mit12

D18Mit1

10

13

16.2

2.56.72.72.51.63.4

7.6

18

5q

ADRB2DRD1PDGFRB

GRL1FGF1EGR1

Ratte Chr. 18

Humane Homologie

MBPMC5RFECH

Comparative mapping of BP QTLs

Chr.1

Chr.2

51,52,53,54

30,31,32,33,3834,35,36,37

45,46,47,48

13,14,15,1617,18,19

20,21,22,23,24,25,26

Mansfield et al.

Krushkal et al.

39,40,41,42

20,21,22,23,24,25,26

51,52,53,54

27,28,29 13,14,15,1617,18,19

Chr.3Chr.4

Predicted susceptibility loci in the human genome

Stoll et al. Genome Res. 10: 473 – 482, 2000http://www.genome.org/cgi/content/full/10/4/473Free access

Mouse

Rat

Conclusion

The regions in the human genome implicated forhypertension may be useful as primary targets

1. Large scale testing in human populations Association studies TDT, Sib-TDT Linkage studies 2. High density mapping Targeted genome scans Single Nucleotide Polymorphisms (SNPs)

Genetic studies in human populations

Is there a genetic component ?

Mendelian Disease:

Exhibits Mendelian mode of inheritance Complex Disease:

Appears to cluster in familiesFamily, twin, adoption studies show greater risk to relatives of affecteds than the population incedenceSegregation analysis can provide estimates of genetic and environmental contribution to disease

Where is the gene ?

Linkage analysis:

Cosegregation of mapped marker with the diseaseFine mapping to narrow the region

In Complex Disease:

Requires a defined genetic modelRequires classifying people as affects and unaffectedsAllele sharing methods (sib pairs etc.)

Population association studies

Genetic Methods

Genomwide linkage(ca. 400 Mikrosatellites, 10cM)

Fine mapping(Saturation with Mikrosatellites, 1cM)

Association and Linkage Disequilibrium(SNPs, 3-50kB, Transmission Disequilibrium, LD, Haplotype analysis)

Association in Case/Control Design(SNPs, Haplotype Case/Controls, ethnically divergent populations)

Traditional

1 12

2 2 2 2

1

*

Linkage analysisLinkage Disequilibrium

Linkage analysis

Non-parametric linkage studies

Looking at a marker Association in between families

1/2 3/4

1/3 2/4

1/2 3/4

1/3 2/4

Extended familiesAffected relative pairsDiscordant pairs

1/2 3/4

1/3 1/4

1/2 3/4

1/3 2/4

Affected sib pairs

Problem: late onset of CAD

Non-Parametric Linkage Analysis

m1

m2

m3

m4

Disease gene

Chromosome LOD= log10 [L()/L(1/2)]

= log10 [Prob. Linkage/Prob. No Linkage]

See Figure 1 from Broekel et al.

Nature Genetics 30, 210 - 214 (2002)

http://www.nature.com/ng/journal/v30/n2/full/ng827.html

Free access

Several examples for hypertension linkage in human study populations

How to get from linkage to the causative gene variant ?

What is Linkage Disequilibrium ?

Linkage - property of the relative position of loci, not their alleles. Linkage is the cosegregation of a disease or trait with a specific genomic region in multiple families (it can involve any allele at the marker locus in a given family)

Association - property of alleles: a specific allele of a gene or marker is found with a disease or trait in a population

Linkage Disequilibrium – the presence of linkage AND association Cosegregation of a specific allele with the disease in a significant number of families

Why do we care about Linkage Disequilibrium ?

It is a tool for fine mapping

Affected sib pair analysis may not be sensitive enough to detect minor genes

Association test may be sensitive but the association detected may not be due to linkage disequilibrium. It could be caused by population stratification (confounding due to race, admixture, heterogeneity in the population for some other reason)

How do you analyze for Linkage Disequilibrium ?

Transmission Disequilibrium Test (TDT):

TDT tests for equal numbers of transmissions of specific alleles and all others from heterozygous parents to an affected offspring GENEHUNTER: Transmitted vs. Untransmitted alleles TRANSMIT: Expected vs. Observed alleles

TDT test is McNemar‘s Chi-square test = (b-c)2/(b+c)

Trans UntransAllele 1 211 138 Chi-square= 15.27Allele 2 138 211 p=0.000093

Limitations: locus heterogeneity, allelic heterogeneity, need for specific polymorphisms, can only detect linkage in the presence of association, need to be very close to disease gene

What‘s all that Fuzz about Haplotypes ?

Linkage Disequilibrium decays with time (No. of recombinations)

X

X X

1

2

2

11 1 1

1

2

2

2 2 2

1

1

X2

1 2

1

1 12 2 2 2 2 2

m1

m2

1

LD

t

= (1-)t

Size of Haplotype blocks depends on population history

L. Kruglyak (1998): need 1 SNP/3kb for genomewide association

D. Reich (2001): haplotype block size in Caucasians 60-120kb due to bottle neck in population history 50,000 years ago haplotype block size in Africans 10-30 kb

M. Daly (2001): haplotype block structure in human genome

2003: haplotype structure varies. Blocks of long range LD interspersed with recombination hot spots

Human Haplotype Map – will be finished in 2005

Hierachical Linkage Disequilibrium Mapping

See figures from Stoll et al.

Nature Genetics 36 (5): 476-480, 2004

http://www.nature.com/ng/journal/v36/n5/index.html

Subscription access only

296 multiplex icelandic families (713 individuals)Linkage on 13q12-13LOD score: 2.86

14 additional microsatellitesLOD score 2.48 (p=0.0036) at D13S289

Haplotype based case-control association using150 microsatellites

Haplotype with association to MI (p=0.00004)

Gene within haplotype ALOX5AP

144 SNPs identified by resequencing 97 individuals2 haplotype blocks in strong LD

Association testing in case/control study design

ALOX5AP is a susceptibility gene for MI and stroke

See figure from

Helgadottir A. et al.

Nature Genetics 36 (3): 233-239 (2004)

http://www.nature.com/ng/journal/v36/n3/index.html

Subscription required

ALOX5AP is a susceptibility gene for MI and stroke

See Table 1 from Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004)http://www.nature.com/ng/journal/v36/n3/index.htmlSubscription required

See Table 2 from Helgadottir A. et al. Nature Genetics 36 (3): 233-239 (2004)http://www.nature.com/ng/journal/v36/n3/index.htmlSubscription required

Conclusion

Success stories for Comparative Genomics Obesity:

Discovery of Leptin as the human homologue of the mouse (ob) mutantLeptin receptor and db/db mice (diabetes and obesity phenotype)Melanocortin-4 receptor and severe obesity in mice and man

Diabetes:

Cd36 as a susceptibility factor for insuline resistance in the SHR ratCblb (ubiquitin-protein ligase) as susceptibility factor for Type I Diabetes

Atherosclerosis:APOAI/CIII/AIV gene cluster and lipid metabolism in mice and man

Hypertension:Predictive power of QTLs from rodents for human hypertension

Phenotype

Gene

PositionalCloning

Candidate Gene Approach

TransgenicsKnock-outsKnock-ins

CongenicsConsomicsENU-Mutagenesis

Case-control StudiesTotal Genome Scan