Publication: "LFMM 2: Fast and accurate inference of gene-environment associations in genome-wide studies"

on the February 5, 2019

Kevin Caye, Basile Jumentier, Johanna Lepeule and Olivier François publish "LFMM 2: Fast and accurate inference of gene-environment associations in genome-wide studies" in Molecular Biology and Evolution in the framework of the Data Institute’s work package 2 “Data Science for Life Sciences”.

Association studies have been extensively used to identify genes or molecular markers associated with disease states, exposure levels or phenotypic traits. Given a large number of molecular markers, the objective of those studies is to test whether any of the markers exhibits significant correlation with a primary variable of interest. Among association methods, gene-environment association (GEA) studies are essential to understand the past and ongoing adaptations of organisms to their environment.

Although they bring useful information on the molecular targets of selection, GEA studies are complicated by the problem of confounding. This problem arises when there exist unobserved variables that correlate both with the primary variables and genomic data. Several model-based approaches have been introduced to evaluate GEA while correcting for unobserved demographic processes and population structure, but the proposed approaches do not scale with the high-dimensionality of genomic data.

This study presents a fast and accurate estimation method implemented in an upgrated version of the program LFMM. 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 approaches.

We illustrate the program use with analyses of the 1,000 Genomes Project data set, leading to new findings on adaptation of humans to their environment, and with analyses of DNA methylation profiles providing insights on how tobacco consumption could affect DNA methylation in patients with rheumatoid arthritis.



Link for the publication

Published on February 5, 2019

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