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Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium, Nature, 2007.

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Presentation on theme: "Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium, Nature, 2007."— Presentation transcript:

1 Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls The Wellcome Trust Case Control Consortium, Nature, 2007 Presented by Group 4: Jessica Larson, Irene Shui, and Lucia Sobrin

2 Outline Introduction Methods Case-control structure Population stratification Data analysis Results Diabetes (Type II) Crohn’s disease Rheumatoid arthritis Coronary artery disease Discussion and Conclusion

3 Introduction Several common (complex) diseases with evidence for heritability but incomplete knowledge of causal genes Genome-wide association studies (GWAS) would help ‘unlock’ the genetic basis for common diseases Requires large sample sizes (for sufficient power) HapMap resource This study validates the GWA method

4 Introduction, continued WTCCC combines 50 research groups throughout the UK Large selection of cases and controls Seven common diseases: Type II diabetes (T2D) Crohn’s disease (CD) Coronary artery disease (CAD) Rheumatoid arthritis (RA) Type I diabetes (T1D) Hypertension (HT) Bipolar disorder (BD) Multiple diseases studied so that WTCCC could look at differences between the diseases themselves (not just between cases and controls for each disease)

5 Cases and Controls 2,000 cases for each disease 3,000 shared controls 1500 from 1958 British Birth Cohort 1500 from UK Blood Services

6 Two Control Groups Purpose: To assess possible bias in ascertaining control samples

7 Shared Controls Potential Issues Misclassification bias Inflation of type 1 error rate from failure to match on socio-demographic variables This study provides compelling case for the suitability and efficiency of this design in Britain.

8 Population Stratification Only included self-identified white Europeans Further excluded 153 individuals with evidence of recent non-European ancestry. Still possible heterogeneity; waves of immigration Analyzed allele frequency differences in 12 geographic regions 13 genomic regions with strong geographic variation (NW/SE axis; London set apart) Geographic correlation not apparent in 7 diseases studied Principal components analysis Conclude that population stratification not much of a problem once individuals with non-European ancestry excluded Adjusting for principal components and stratifying by geographic region did not make a big difference in overdispersion; p-values with and without structure correction were similar

9 Figure 2, Wellcome Trust Case Control Consortium, 2007 NADSYN1 (11q13—possible role in prevention of pellagra) TLR1 (4p14 toll-like receptor 1—possible role in biology of TB and leprosy) LCT (Iactase digestion) HLA (Major histocompatibility complex) LCT 4p14 HLA Previously implicated in Europeans 11 df test for differences in allele frequency between geographic regions

10 SNP genotyping and Data Analysis GeneChip 500K Affymetrix arrays Gene-calling algorithm CHIAMO For polymorphic SNPs Trend tests General genotype tests between cases and controls Sex-differentiation test Loci affecting more than one disease, combine the cases vs. the controls CAD+HT+T2D (metabolic overlap) RA+T1D (known to share common loci) CD+RA+T1D (autoimmune diseases)

11 Data Analysis Significance levels were chosen not to directly correct for multiple tests (to obtain a ‘genome-wide significance level’), but to still have a low FDR Strong: regions with at least one SNP’s P-val<5x10^-7 (Table 3) Single disease: 21 signals Sex diff: RA Combined cases: RA+T1D 25 total  12 of which previously described  Rest have been confirmed, except one Moderate: 5x10^-7< P-val< 1 x10^-5 (Table 4) Nominal: 1x10^-5< P-val< 1 x10^-4 (Supplementary Table 7)

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13 Notes on interpretation of this data Replication needed Failure to detect an association does not mean that a given gene is unassociated with disease Help define regions of interest, cannot clearly identify causal genes

14 Overall Results Figure 4, Wellcome Trust Case Control Consortium, 2007

15 Type 2 Diabetes Detected all three previously widely replicated associations TCF7L2 SNP with strongest etiological claims not on Affy chip, but imputation analysis confirms it is the SNP with strongest association effect PPARG and KCNJ11 (p~0.001 for both) Genuine disease susceptibility genes can generate signals in GWS that would not attract immediate attention

16 Type 2 Diabetes Compared to French GWAS Findings Confirms finding on Chromosome 10 Three other findings cannot be replicated One SNP is poorly covered by Affy chip and extensive recombination in region limits data imputation Two other SNPs cannot be confirmed by either genotyped or imputed SNPs from the WTCCC

17 Crohn’s Disease Common form of chronic inflammatory bowel disease Pathogenesis poorly understood Dysregulated immune response to intestinal bacterial and possibly defects in mucosal barrier function or bacterial clearance Genetic predisposition is strong (lambda-s 17-35; twin studies: 50% concordance in monozygotic vs 10% in dizygotic twins)

18 Crohn’s Disease GWAS Results Previously defined susceptibility loci (6) all replicated Four new strong association signals (p-value <5X10-7) Successfully replicated in other studies Eight less strong evidence for associati on markers (p-value >5X10 -7 and <1X10 -5 ) Several with biological candidacy Majority of associations modest RR<2 Functional mechanism: autophagy Newly identified susceptibility gene (IRGM) proposed to control the spread of intracellular pathogens by autophagy (ATG16L1 also involved in autophagy) Possible functional mechanism of autophagy and Crohn’s Disease supported by molecular genetic studies

19 Figure 4, Wellcome Trust Case Control Consortium, 2007 WTCCC SNP in LD with SNP T300A RED—Replicated defined markers/possible genes GREEN-Novel Markers/possible genes IL23R ATG16L1 CARD155q31 10q215q13.1IRGM BSN/ MST1 NKX2-3 PTPN2 Crohn’s Disease Strong Associations IL23R Interleukin-23 receptor ATG16L1 Involved in autophagy 5q13.1 Gene desert 5q31 Causative gene in dispute b/c of high LD in region 10q21 Non coding CARD15 1 st confirmed susceptibility gene IRGM Involved in autophagy BSN/MST1 Many genes in this region: BSN closest (but brain related); MST1: encodes a protein that induces phagocytosis by resident peritoneal macrophages. NKX2-3 Lymphoid tissue abnormalities PTPN2 Negative regulator of inflammatory resp.

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21 Coronary artery disease (CAD) Plaque buildup in arteries Environmental (diet) and genetic factors Previously associated genes not replicated here (APOE, p-val:1.7x 10^-1) Found a new region of interest 9p21.3 (1.8x10^-14) and several moderate associations

22 Rheumatoid arthritis (RA) Chronic inflammatory disease, destruction of joints, severe disability Again, environmental and genetic factors Previously associated genes replicated here (HLA- DRB1, p-vals: 10^-27; PTPN22, p-vals: 10^-25) Found two new regions of interest and several moderate associations Most interesting is the sex effect (p-val: 3.9 x 10^-7), additive effect in females only

23 Common Loci for Autoimmune Diseases CD25 region Encodes IL-2 receptor Association with both RA and T1D (p~10 -8 and p~10 -6, respectively) PTPN2 Encodes a key negative regulator of inflammatory responses Strong association with CD and T1D (p~10 -8 ) and weaker but consistent association with RA (p~10 -2 )

24 Discussion/Conclusions GWAS yielded multiple association findings for multiple diseases, many of them novel Large study; still power issues for OR<1.2 Extensive quality control Used both linear trend and 2 df genotypic test Replication is key “winner’s curse”; ORs will tend to be overestimated for loci discovered Several studies have replicated; more work needs to be done Incomplete coverage of Affy chip for some SNPs (T1D INS) Functional studies needed to make inferences about molecular and physiological mechanisms involved and causal variants No real gene-gene/gene-environment interactions tested Findings to date only explain a small proportion of the genetic variation in these diseases Information is publicly available! http://www.wtccc.org.uk/info/access_to_data_samples.shtml

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26 Thank You!


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