Multi-population GWA mapping via multi-task regularized regression.

ABSTRACT:

Motivation

Population heterogeneity through admixing of different founder populations can produce spurious associations in genome-wide association studies that are linked to the population structure rather than the phenotype. Since samples from the same population generally co-evolve, different populations may or may not share the same genetic underpinnings for the seemingly common phenotype. Our goal is to develop a unified framework for detecting causal genetic markers through a joint association analysis of multiple populations.

Results

Based on a multi-task regression principle, we present a multi-population group lasso algorithm using L(1)/L(2)-regularized regression for joint association analysis of multiple populations that are stratified either via population survey

SUBMITTER: Puniyani K 

PROVIDER: S-EPMC2881376 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature