An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies.

ABSTRACT: Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding t

SUBMITTER: Thompson WK 

PROVIDER: S-EPMC5456456 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature