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The genetics of type 1 diabetes: More insights or just more genes?
SEAPC
January 31, 2013
Lab project areas Cellular Radiation Sensitivity
Radiation therapy and second cancers Malnutrition
Type 1 diabetes susceptibility
Expected effect size and frequency of risk alleles dictate genetic mapping strategies
Linkage studies
Unlikely to exist
Frequency in population
Effe
ct S
ize
Unlikely to be funded
T1DGC linkage scan on 3,998 affected sib pair families
Chromosome
LO
D
Suggestive
Significant
Expected effect size and frequency of risk alleles dictate genetic mapping strategies
Association studies
Unlikely to exist
Frequency in population
Effe
ct S
ize
Unlikely to be funded
GWA studies have significantly accelerated the pace of gene discovery in T1D
HL
AIN
SP
TP
N2
2IL
2R
AC
10
orf
59
SH
2B
3E
RB
B3
GR
B1
0P
TP
N2
CT
RB
2C
LE
C1
6A
CT
LA
4IL
18
RA
PP
TP
N2
IL1
0C
CR
5C
6o
rf1
73
C1
4o
rf1
81
PR
KD
2IF
IH1
CT
SH
CD
22
6IL
27
GA
B3
IL2
RA
SK
AP
2G
LIS
3O
RM
DL
3P
RK
CQ
IL2
BA
CH
2U
BA
SH
3A
RG
S1
IL7
RA
CIQ
TN
F6
SIR
PG
TN
FA
IP3
PG
M1
UM
OD
LO
C7
29
98
0T
NF
AIP
34
p1
5C
D6
91
4q
22
TA
GA
P2
p2
4
1.0
1.5
2.0
2.55.5
6.0
6.5
7.0
1970-2000 20092001-2006 2007-2008
Locus
Od
ds r
ati
o
Challenges in the post-GWAS era
• Fine mapping – How do we identify the true causative variants?
• Functional studies – How do causative variants exert their effects and how
does this contribute to disease pathogenesis?
• Translation – Can we use genetic information to better predict risk of
T1D • Facilitate prevention trials
– Will knowledge of the biochemical pathways involved in T1D risk help in the development of novel therapies?
T1D associated regions contain a wide range of potential causative genes
Median number of genes per region = 3, range 0-28
Challenges of causative gene and variant identification
Fine mapping autoimmune risk loci: The ImmunoChip Consortium
Specific to 12 immunologically related human diseases: – Type 1 Diabetes
– Autoimmune thyroid disease
– Ankylosing spondylitis
– Crohn’s disease
– Celiac disease
– IgA deficiency
– Multiple sclerosis
– Primary biliary cirrhosis
– Psoriasis
– Rheumatoid arthritis
– Systemic lupus erythematosus
– Ulcerative colitis
ImmunoChip Content
• 186 distinct loci (all reported index markers meet the genome wide significance criteria P<5x10-8)
• SNPs were chosen from: – 1000 Genomes Project pilot CEU population variants
within 0.1 cM (HapMap3 CEU) recombination blocks around each index SNP
– ~6,000 SNPs from the HLA region – Investigator contributed variants from re-sequencing
data – follow-up of WTCCC2 (stroke, Parkinson's, etc.) – Limited investigator-specific wildcard content
Immunochip Meta-analysis approach
• T1DGC families – Family-based association test – Restriction to MAF > 0.05 & HWE & MIE leaves 164,643 SNPs – After QC, 2,682 families: 1,670 families with both parents,
652 with one parent and 360 with neither parent
• UKGrid cases, 1958 Birth Cohort controls – Logistic regression with sex and UK region as covariates – Restriction to MAF > 0.05 and HWE (controls) leaves 163,924
SNPs – After QC, 6,670 cases and 9,416 controls
• Meta-analysis – Stouffer-Listak method (METAL) – Critical threshold P ≤ 3.23 x 10-7
Summary results from T1D ImmunoChip
• T1DBase – 45 non-MHC loci with prior association with genome-wide
significance (P ≤ 5 x 10-8) (Group 1) – 10 non-MHC loci with genome-wide significance in another
autoimmune disease and P < 1 x 10-4 in T1D (Group 2)
• ImmunoChip – 36 previously reported non-MHC loci were significantly
associated with T1D – 4 novel T1D associated loci were identified – 4 loci had 2 or more independent significantly associated
variants – Within the T1D associated regions, 19,839 SNPs were reduced
to 396 statistically indistinguishable candidates for causative variants.
ImmunoChip replication of reported T1D loci
Significant reduction in numbers of candidate SNPs and genes after ImmunoChip
Median number of candidate SNPs per region = 4, range = 1-56 Median number of genes per region = 1, range = 0-3
Chromosome 1: PTPN22
Chromosome 6q22.32
Mining the ImmunoChip data: Use of SNP-based risk scores for T1D prediction
• Risk score computation
– Based on the set of alleles carried by an individual
– Linear combination of genotypes at associated SNPs, with weights being the regression coefficients
• Risk score models
– 1) 27 MHC + 46 non-MHC SNPs
– 2) 27 MHC SNPs
– 3) 46 non-MHC SNPs
• T1D Assessment in case-controls
– 2330 cases and 2330 controls used as a training set
– Assess the other 1190 cases and 3470 controls
Wei-Min Chen, Ph.D. UVA
ROC Plot in Case-Control Validation Set
• ROC: Proportions of cases and controls with the risk score greater than a threshold (indicated in color)
• Accuracy (area under curve) – All 73 SNPs = 0.918
– 27 MHC SNPs = 0.888
– 46 non-MHC SNPs = 0.741
Functional effects of causative variants
• Molecular effects – the immediate effects of a change in DNA sequence
• Regulatory: affecting expression of single or networks of genes • Coding: affecting protein structure and/or function
• Cellular effects – alterations in cellular functions, proliferation or
differentiation
• Organismic effects – changes in organ function or development that predispose
to T1D – broader health implications of causative variants and their
effects
Pilot e-QTL study of cell types from freshly drawn PBMC samples
• 150 ml blood draws from 25 T1D cases and 25 controls with no first degree history of autoimmunity – Matching on age, ethnicity and sex – immunophenotyping data available
• Negative selection for CD4+ T cells, CD8+ T cells, B cells, monocytes and NK cells
• Genotyping on ImmunoChip • Expression measurements
– Affymetrix exon arrays on all samples – RNAseq on CD4 T cells
• Expression analysis by RMA – (no significant differences observed between cases and controls)
• E-QTL analysis using PLINK
Mapping gene expression as a quantitative trait
Dermitzakis E.T. Nat. Rev. Genet. 13:215-220. 2012
Significant cis and trans e-QTLs detected
Unique and shared cis e-QTLs across cell types
• The majority of cis-eQTLs detected are cell type specific
• Shared e-QTLs tend to mirror developmental or functional relationships between cell types
• Some shared e-QTLs differ in direction of effect by cell type
Challenges of causative gene and variant identification
Most significant T1D associated SNP at 17q12 regulates multiple genes in different cell types
Expected effect size and frequency of risk alleles dictate genetic mapping strategies
Unlikely to exist
Frequency in population
Effe
ct S
ize
Unlikely to be funded
Rare variants
High risk families as sequencing targets
Multiple affecteds with early age at onset
Targeted exon sequencing
- Re-sequenced all exons in all reported T1D risk loci (excluding the HLA region)
- Captured exons using Agilent SureSelect Target Enrichment System - Total of 3,629 exons (347 genes)
- Sequencing in 8 pools of 10 individuals - Selection scheme:
- 3 or more affected siblings - no affected parents (exclude MODY) - early age at onset but greater than 1 year (exclude neonatal diabetes)
- Illumina paired-end sequencing - Deconvolution and confirmation by Sanger sequencing
- Genotype variants of interest in all remaining families
Variant Gene Impact
rs74163663 PTPN22 non-synonymous
rs56048322 PTPN22 splicing; non-synonymous
ex13delT PTPN22 frameshift
c.878_881delAGAT PTPN22 frameshift
c.143T>G CENPW stopgain
rs144756065 IL20 splicing; non-synonymous
rs35744605 IFIH1 stopgain
rs148010539 SIRPG stopgain
Selected deleterious variants detected from sequencing in high risk T1D families
PTPN22: a risk locus for multiple autoimmune diseases
• Encodes Lyp, a 110kD lymphoid-specific tyrosine phosphatase
• Binds to C-terminal Src tyrosine kinase (CSK)
• Dephosphorylates activation loops of LCK, FYN and ZAP70
• Negative regulator of T-cell activation
• A coding region variant (1858C>T, R620W) is associated with RA, SLE, thyroiditis and T1D
Bottini et al. Sem. Immunol. 18:207-213, 2006.
blunted TCR signal
Genetic Variant
Molecular Effect
Cellular Effect
Disease Pathology
PTPN22 1858T* (LYP R620W))
Expansion CD4 memory IL-10, IL-2, IL-4 AICD
blunted BCR signal B cell proliferation transitional B cells clonal deletion
How do these alterations in immune function increase risk of autoimmunity?
The PTPN22 1858T variant impacts multiple immune pathways
Jane H Buckner, MD
Do they also impact the response of individuals to infection and immunization?
Rare variants in PTPN22 from re-sequencing
R620W R748G K750N
Q293fs
I348fs
Phosphatase domain 1-300
Proline –rich repeats 600-800
Marker Allele Freq Family # Z P
Q293fs 0.00 2 0.447 0.655
I348fs 0.00 1 1.000 0.317
K750N 0.01 85 3.268 0.001
R748G 0.001 6 2.309 0.021
The rare allele of rs56048322 generates alternate PTPN22 transcripts
Stop codon created
Stop codon at +49 nt
Alternatively spliced PTPN22 transcripts produce stable truncated protein products that can interact with CSK
Conclusions and Directions
• ~40 genomic regions can be reproducibly shown to contribute to risk for T1D – Specific genes in these regions are implicated by fine mapping studies – Studies focused on the molecular effects of the putative causative
variants on gene expression and function are needed to identify the causative genes unambiguously
– Genes identified by GWAS also contain rare deleterious variants that can be disease associated and provide functional insights
• Future directions – Exome sequencing to identify rare risk variants in multiplex families – Broader e-QTL studies incorporating more cell types and subjects
• RNAseq to identify differential splicing and allele-specific expression effects
– Translational studies to elucidate the role of risk variants in pathogenesis
• Mouse knock-in models under construction for coding region variants
Acknowledgements
Concannon Lab Members T1D Achilleas Pitsillides Yan Ge Joe Tomlinson Matthew Mika DNA damage Sharon Teraoka Jie Wen Larry Mesner Jo Wright Malnutrition Don Mackay Genotyping Emily Farber Ben Artale Dan Gallo Jordan Davis
UVA Collaborators Suna Onengut-Gumuscu Charles Farber Wei-Min Chen Aaron Mackey Joe Mychaleckyj Ira Hall Aaron Quinlan Steve Rich eQTL study Grant Morahan Flemming Pociot Cecile Julier Brad Stone Claire Vandiedonck
GWAS John Todd Vincent Plagnol Chris Wallace Jo Howson Jason Cooper David Clayton Neil Walker CNVs Matt Hurles Jeff Barrett The T1DGC