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

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CT

LA

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RA

PP

TP

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IL1

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1970-2000 20092001-2006 2007-2008

Locus

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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