Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data.

ABSTRACT: Genome-wide association studies (GWAS) have associated many single variants with complex disease, yet the better part of heritable complex disease risk remains unexplained. Analytical tools designed to work under specific population genetic models are needed. Rare variants are increasingly shown to be important in human complex disease, but most existing GWAS data do not cover rare variants. Explicit population genetic models predict that genes contributing to complex traits and experiencing recurrent, unconditionally deleterious, mutation will harbor multiple rare, causative mutations of subtle effect. It is difficult to identify genes harboring rare variants of large effect that contribute to complex disease risk via the single marker association tests typically used in GWAS. Gene/region

SUBMITTER: Sanjak JS 

PROVIDER: S-EPMC4825638 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature