A unified sparse representation for sequence variant identification for complex traits.

ABSTRACT: Joint adjustment of cryptic relatedness and population structure is necessary to reduce bias in DNA sequence analysis; however, existent sparse regression methods model these two confounders separately. Incorporating prior biological information has great potential to enhance statistical power but such information is often overlooked in many existent sparse regression models. We developed a unified sparse regression (USR) to incorporate prior information and jointly adjust for cryptic relatedness, population structure, and other environmental covariates. Our USR models cryptic relatedness as a random effect and population structure as fixed effect, and utilize the weighted penalties to incorporate prior knowledge. As demonstrated by extensive simulations, our USR algorithm can discover mor

SUBMITTER: Cao S 

PROVIDER: S-EPMC4236284 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature