David G. Clayton

Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes

The Wellcome Trust Case Control Consortium (WTCCC) primary genome-wide association (GWA) scan on seven diseases, including the multifactorial autoimmune disease type 1 diabetes (T1D), shows associations at P < 5 × 10−7 between T1D and six chromosome regions: 12q24, 12q13, 16p13, 18p11, 12p13 and 4q27. Here, we attempted to validate these and six other top findings in 4,000 individuals with T1D, 5,000 controls and 2,997 family trios independent of the WTCCC study. We confirmed unequivocally the associations of 12q24, 12q13, 16p13 and 18p11 (Pfollow-up ≤ 1.35 × 10−9; Poverall ≤ 1.15 × 10−14), leaving eight regions with small effects or false-positive associations. We also obtained evidence for chromosome 18q22 (Poverall = 1.38 × 10−8) from a GWA study of nonsynonymous SNPs. Several regions, including 18q22 and 18p11, showed association with autoimmune thyroid disease. This study increases the number of T1D loci with compelling evidence from six to at least ten.

Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes

Type 1 diabetes (T1D) is a common autoimmune disorder that arises from the action of multiple genetic and environmental risk factors. We report the findings of a genome-wide association study of T1D, combined in a meta-analysis with two previously published studies. The total sample set included 7,514 cases and 9,045 reference samples. Forty-one distinct genomic locations provided evidence for association with T1D in the meta-analysis (P < 10−6). After excluding previously reported associations, we further tested 27 regions in an independent set of 4,267 cases, 4,463 controls and 2,319 affected sib-pair (ASP) families. Of these, 18 regions were replicated (P < 0.01; overall P < 5 × 10−8) and 4 additional regions provided nominal evidence of replication (P < 0.05). The many new candidate genes suggested by these results include IL10, IL19, IL20, GLIS3, CD69 and IL27.

Haplotype tagging for the identification of common disease genes

Genome-wide linkage disequilibrium (LD) mapping of common disease genes could be more powerful than linkage analysis if the appropriate density of polymorphic markers were known and if the genotyping effort and cost of producing such an LD map could be reduced. Although different metrics that measure the extent of LD have been evaluated, even the most recent studies have not placed significant emphasis on the most informative and cost-effective method of LD mapping—that based on haplotypes. We have scanned 135 kb of DNA from nine genes, genotyped 122 single-nucleotide polymorphisms (SNPs; approximately 184,000 genotypes) and determined the common haplotypes in a minimum of 384 European individuals for each gene. Here we show how knowledge of the common haplotypes and the SNPs that tag them can be used to (i) explain the often complex patterns of LD between adjacent markers, (ii) reduce genotyping significantly (in this case from 122 to 34 SNPs), (iii) scan the common variation of a gene sensitively and comprehensively and (iv) provide key fine-mapping data within regions of strong LD. Our results also indicate that, at least for the genes studied here, the current version of dbSNP would have been of limited utility for LD mapping because many common haplotypes could not be defined. A directed re-sequencing effort of the approximately 10% of the genome in or near genes in the major ethnic groups would aid the systematic evaluation of the common variant model of common disease.

Genome-wide association studies: theoretical and practical concerns.

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors — including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases — that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region.

In this study we report convincing statistical support for a sixth type 1 diabetes (T1D) locus in the innate immunity viral RNA receptor gene region IFIH1 (also known as mda-5 or Helicard) on chromosome 2q24.3. We found the association in an interim analysis of a genome-wide nonsynonymous SNP (nsSNP) scan, and we validated it in a case-control collection and replicated it in an independent family collection. In 4,253 cases, 5,842 controls and 2,134 parent-child trio genotypes, the risk ratio for the minor allele of the nsSNP rs1990760 A → G (A946T) was 0.86 (95% confidence interval = 0.82–0.90) at P = 1.42 × 10−10.

Shared and Distinct Genetic Variants in Type 1 Diabetes and Celiac Disease

BACKGROUND Two inflammatory disorders, type 1 diabetes and celiac disease, cosegregate in populations, suggesting a common genetic origin. Since both diseases are associated with the HLA class II genes on chromosome 6p21, we tested whether non-HLA loci are shared. METHODS We evaluated the association between type 1 diabetes and eight loci related to the risk of celiac disease by genotyping and statistical analyses of DNA samples from 8064 patients with type 1 diabetes, 9339 control subjects, and 2828 families providing 3064 parent-child trios (consisting of an affected child and both biologic parents). We also investigated 18 loci associated with type 1 diabetes in 2560 patients with celiac disease and 9339 control subjects. RESULTS Three celiac disease loci--RGS1 on chromosome 1q31, IL18RAP on chromosome 2q12, and TAGAP on chromosome 6q25--were associated with type 1 diabetes (P<1.00x10(-4)). The 32-bp insertion-deletion variant on chromosome 3p21 was newly identified as a type 1 diabetes locus (P=1.81x10(-8)) and was also associated with celiac disease, along with PTPN2 on chromosome 18p11 and CTLA4 on chromosome 2q33, bringing the total number of loci with evidence of a shared association to seven, including SH2B3 on chromosome 12q24. The effects of the IL18RAP and TAGAP alleles confer protection in type 1 diabetes and susceptibility in celiac disease. Loci with distinct effects in the two diseases included INS on chromosome 11p15, IL2RA on chromosome 10p15, and PTPN22 on chromosome 1p13 in type 1 diabetes and IL12A on 3q25 and LPP on 3q28 in celiac disease. CONCLUSIONS A genetic susceptibility to both type 1 diabetes and celiac disease shares common alleles. These data suggest that common biologic mechanisms, such as autoimmunity-related tissue damage and intolerance to dietary antigens, may be etiologic features of both diseases.

Genetic association studies

We review the rationale behind and discuss methods of design and analysis of genetic association studies. There are similarities between genetic association studies and classic epidemiological studies of environmental risk factors but there are also issues that are specific to studies of genetic risk factors such as the use of particular family-based designs, the need to account for different underlying genetic mechanisms, and the effect of population history. Association differs from linkage (covered elsewhere in this series) in that the alleles of interest will be the same across the whole population. As with other types of genetic epidemiological study, issues of design, statistical analysis, and interpretation are very important.

Population structure, differential bias and genomic control in a large-scale, case-control association study

The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure. In genetics the first of these, in the form of population structure, has dominated recent debate. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method by applying a variable downweighting to each SNP.

Genetically Distinct Subsets within ANCA-Associated Vasculitis

BACKGROUND Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a severe condition encompassing two major syndromes: granulomatosis with polyangiitis (formerly known as Wegeners granulomatosis) and microscopic polyangiitis. Its cause is unknown, and there is debate about whether it is a single disease entity and what role ANCA plays in its pathogenesis. We investigated its genetic basis. METHODS A genomewide association study was performed in a discovery cohort of 1233 U.K. patients with ANCA-associated vasculitis and 5884 controls and was replicated in 1454 Northern European case patients and 1666 controls. Quality control, population stratification, and statistical analyses were performed according to standard criteria. RESULTS We found both major-histocompatibility-complex (MHC) and non-MHC associations with ANCA-associated vasculitis and also that granulomatosis with polyangiitis and microscopic polyangiitis were genetically distinct. The strongest genetic associations were with the antigenic specificity of ANCA, not with the clinical syndrome. Anti-proteinase 3 ANCA was associated with HLA-DP and the genes encoding α(1)-antitrypsin (SERPINA1) and proteinase 3 (PRTN3) (P=6.2×10(-89), P=5.6×10(-12,) and P=2.6×10(-7), respectively). Anti-myeloperoxidase ANCA was associated with HLA-DQ (P=2.1×10(-8)). CONCLUSIONS This study confirms that the pathogenesis of ANCA-associated vasculitis has a genetic component, shows genetic distinctions between granulomatosis with polyangiitis and microscopic polyangiitis that are associated with ANCA specificity, and suggests that the response against the autoantigen proteinase 3 is a central pathogenic feature of proteinase 3 ANCA-associated vasculitis. These data provide preliminary support for the concept that proteinase 3 ANCA-associated vasculitis and myeloperoxidase ANCA-associated vasculitis are distinct autoimmune syndromes. (Funded by the British Heart Foundation and others.).

Control of Confounding of Genetic Associations in Stratified Populations

To control for hidden population stratification in genetic-association studies, statistical methods that use marker genotype data to infer population structure have been proposed as a possible alternative to family-based designs. In principle, it is possible to infer population structure from associations between marker loci and from associations of markers with the trait, even when no information about the demographic background of the population is available. In a model in which the total population is formed by admixture between two or more subpopulations, confounding can be estimated and controlled. Current implementations of this approach have limitations, the most serious of which is that they do not allow for uncertainty in estimations of individual admixture proportions or for lack of identifiability of subpopulations in the model. We describe methods that overcome these limitations by a combination of Bayesian and classical approaches, and we demonstrate the methods by using data from three admixed populations--African American, African Caribbean, and Hispanic American--in which there is extreme confounding of trait-genotype associations because the trait under study (skin pigmentation) varies with admixture proportions. In these data sets, as many as one-third of marker loci show crude associations with the trait. Control for confounding by population stratification eliminates these associations, except at loci that are linked to candidate genes for the trait. With only 32 markers informative for ancestry, the efficiency of the analysis is 70%. These methods can deal with both confounding and selection bias in genetic-association studies, making family-based designs unnecessary.