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

Kevin Caye 1 Basile Jumentier 1 Johanna Lepeule 2, 3 Olivier François 4
1 TIMC-IMAG-BCM - Biologie Computationnelle et Mathématique
TIMC-IMAG - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525
4 TIMC-BCM - Biologie Computationnelle et Modélisation
TIMC - Translational Innovation in Medicine and Complexity / Recherche Translationnelle et Innovation en Médecine et Complexité - UMR 5525
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 approaches and then our previous version of the LFMM program. In addition, the new method outperforms other fast approaches based on principal component or surrogate variable analysis. We illustrate the program use with analyses of the 1000 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. Software availability: Software is available in the R package lfmm at https://bcm-uga.github.io/lfmm/.
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https://www.hal.inserm.fr/inserm-03179375
Contributor : Maïlys Barbagallo Connect in order to contact the contributor
Submitted on : Wednesday, March 24, 2021 - 11:47:59 AM
Last modification on : Tuesday, October 19, 2021 - 11:27:50 AM

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Kevin Caye, Basile Jumentier, Johanna Lepeule, Olivier François. LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies. Molecular Biology and Evolution, Oxford University Press (OUP), 2019, 36 (4), pp.852-860. ⟨10.1093/molbev/msz008⟩. ⟨inserm-03179375⟩

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