ArticleMolecular biology and evolution2019
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies.
Article in Molecular biology and evolution, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 1 of them a synthesis that pooled it.
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176 citing papers in PubMed, 1 synthesis or guideline pooled it, 408 citations in OpenAlex.
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- Adverse and adaptive placental DNA methylation changes linking prenatal air pollution exposure to child lung function: findings from the SEPAGES cohort.EBioMedicine · 2026Article
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116 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.