title: Dynamic Bayesian testing of sets of variants in complex diseases.
abstract: Rare genetic variants have recently been studied for genome-wide associations with human complex diseases. Existing rare variant methods are based on the hypothesis-testing framework that predefined variant sets need to be tested separately. The power of those methods is contingent upon accurate selection of variants for testing, and frequently, common variants are left out for separate testing. In this article, we present a novel Bayesian method for simultaneous testing of all genome-wide variants across the whole frequency range. The method allows for much more flexible grouping of variants and dynamically combines them for joint testing. The method accounts for correlation among variant sets, such that only direct associations with the disease are reported, whereas indirect associations due to linkage disequilibrium are not. Consequently, the method can obtain much improved power and flexibility and simultaneously pinpoint multiple disease variants with high resolution. Additional covariates of categorical, discrete, and continuous values can also be added. We compared our method with seven existing categories of approaches for rare variant mapping. We demonstrate that our method achieves similar power to the best methods available to date when testing very rare variants in small SNP sets. When moderately rare or common variants are included, or when testing a large collection of variants, however, our method significantly outperforms all existing methods evaluated in this study. We further demonstrate the power and the usage of our method in a whole-genome resequencing study of type 1 diabetes.
authors.affiliation: Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania 16802 yzz2@psu.edu.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Division of Immuno-Inflammatory and Respiratory Genetics, GlaxoSmithKline, King of Prussia, Pennsylvania 19406.
authors.forename: Soumitra
authors.initials: S
authors.lastname: Ghosh
authors.affiliation: Department of Pediatrics, Division of Human Genetics and Molecular Biology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104.
authors.forename: Hakon
authors.initials: H
authors.lastname: Hakonarson
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D030541:Databases, Genetic; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D004194:Disease / Q000235:genetics*; D005787:Gene Frequency / Q000235:genetics; D056426:Genetic Loci; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D017422:Sequence Analysis, DNA
keywords: Bayesian graphical model; N brare variant; disease association mapping