Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

ABSTRACT: Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thous

SUBMITTER: Moser G 

PROVIDER: S-EPMC4388571 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature