LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies.

ABSTRACT: Gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment, but those studies are complicated by confounding due to unobserved demographic factors. Although the confounding problem has recently received considerable attention, the proposed approaches do not scale with the high-dimensionality of genomic data. Here, we present a new estimation method for latent factor mixed models (LFMMs) implemented in an upgraded version of the corresponding computer program. We developed a least-squares estimation approach for confounder estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several order faster than existing GEA approac

SUBMITTER: Caye K 

PROVIDER: S-EPMC6659841 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature