Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models

摘要

MotivationGenome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci is associated with multiple traits—a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide single nucleic polymorphisms (SNPs) together.