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Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study.

ABSTRACT: A recent association scan using a genome-wide set of nonsynonymous coding single-nucleotide polymorphisms (nsSNPs) conducted in four diseases including Graves' disease (GD), identified nine novel possible regions of association with GD. We used a case-control approach in an attempt to replicate association of these nine regions in an independent collection of 1578 British GD patients and 1946 matched Caucasian controls. Although none of these loci showed evidence of association with GD in the independent data set, when combined with the original Wellcome Trust Case-Control Consortium study group, minor differences in allele frequencies (P>or=10(-3)) remained in the combined collection of 5924 subjects for four of the nsSNPs, present within HDLBP, TEKT1, JSRP1 and UTX. An additional 29 Tag SNPs were screened within these four gene regions to determine if further associations could be detected. Similarly, minor differences only (P=0.042-0.002) were detected in two HDLBP and two TEKT1 Tag SNPs in the combined UK GD collection. In conclusion, it is unlikely that the SNPs selected in this replication study have a significant effect on the risk of GD in the United Kingdom. Our study confirms the need for large data sets and stringent analysis criteria when searching for susceptibility loci in common diseases.

SUBMITTER: Newby PR

PROVIDER: S-EPMC2987410 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study.

Newby Paul R PR Pickles Oliver J OJ Mazumdar Samaresh S Brand Oliver J OJ Carr-Smith Jaqueline D JD Pearce Simon H S SH Franklyn Jayne A JA Evans David M DM Simmonds Matthew J MJ Gough Stephen C L SC

European journal of human genetics : EJHG 20100505 9

A recent association scan using a genome-wide set of nonsynonymous coding single-nucleotide polymorphisms (nsSNPs) conducted in four diseases including Graves' disease (GD), identified nine novel possible regions of association with GD. We used a case-control approach in an attempt to replicate association of these nine regions in an independent collection of 1578 British GD patients and 1946 matched Caucasian controls. Although none of these loci showed evidence of association with GD in the in ...[more]

PMID: 20442750

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Project description:BackgroundGenetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan.ResultsWe developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations.ConclusionsWe show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Dataset Information

Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study.

Publications

Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study.

Similar Datasets

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