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Open Access Methodology article

Functional annotation signatures of disease susceptibility loci improve SNP association analysis

Edwin S Iversen1*, Gary Lipton1, Merlise A Clyde1 and Alvaro NA Monteiro2

Author Affiliations

1 Department of Statistical Science, Duke University, Box 90251, 27708–0251 Durham, NC, USA

2 Cancer Epidemiology Program, H. Lee Moffitt Cancer Center & Research Institute, 12902 Magnolia Drive, 33612 Tampa, FL, USA

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BMC Genomics 2014, 15:398  doi:10.1186/1471-2164-15-398

Published: 24 May 2014

Abstract

Background

Genetic 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.

Results

We 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.

Conclusions

We 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.

Keywords:
Association study; GWAS; SNPs; Functional annotations; Bayesian analysis; ENCODE project