Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models

By Éric Frichot, Sean D. Schoville, Guillaume Bouchard, Olivier Francois. Molecular biology and evolution, 2012

776 citations recorded by the source corpus.

Adaptation to local environments often occurs through natural selection acting on a large number of loci, each having a weak phenotypic effect. One way to detect these loci is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here, we propose new algorithms based on population genetics, ecological modeling, and statistical learning techniques to screen genomes for signatures of local adaptation. Implemented in the computer program “latent factor mixed model” (LFMM), these algorithms employ an approach in which population structure is introduced using unobserved variables. These fast and computationally efficient algorithms detect correlations between environmental and genetic variation while simultaneously inferring background levels of population structure. Comparing these new algorithms with related methods provides evidence that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations, and decrease the number of false-positive associations in genome scans. We then apply these models to plant and human genetic data, identifying several genes with functions related to development that exhibit strong correlations with climatic gradients.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/03976380df71d745b47e072eaca8bfee058c95e4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

A practical guide to environmental association analysis in landscape genomics (2015, methodology): “Latent factor mixed models (LFMMs).”
LFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies (2019, methodology): “When compared with PCA approaches, LFMMs gained power by removing the part of genetic variation that could not be explained by the primary variables (Frichot et al. 2013).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, positive): “Incorporating population structure with latent factors can improve the accuracy of both genetic-environment association and differentiation outlier tests (Frichot et al. 2013; Duforet-Frebourg et al. 2014; Lotterhos and Whitlock 2015).”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “2010; Günther and Coop 2013), LFMM (Frichot et al. 2013), or BayPass (Gautier Landscape Genomics: Understanding Relationships Between Environmental.”
A new FST‐based method to uncover local adaptation using environmental variables (2015, methodology): “When compared with the other methods (including Bayenv and LFMM), BayeScEnv typically yields a medium FDR for most scenarios and is less scenario sensitive than Bayenv and LFMM.”
Fast and Efficient Estimation of Individual Ancestry Coefficients (2014, background): “…in delineating genetic clusters, drawing inference about the history of a species, screening genomes for signatures of natural selection, and performing statistical corrections in genome-wide association studies (Pritchard et al. 2000b; Marchini et al. 2004; Price et al. 2006; Frichot et al. 2013).”
genodive version 3.0: Easy‐to‐use software for the analysis of genetic data of diploids and polyploids (2020, methodology): “…can be exported in the formats of aflp - surv (Vekemans, 2002), arlequin (Excoffier & Lischer, 2010), bayes - can (Foll & Gaggiotti, 2008), lfmm (Frichot, Schoville, Bouchard, & Francois, 2013) and tess 3 (Caye, Deist, Martins, Michel, & Francois, 2015). genodive also has its own file format…”
Robust Identification of Local Adaptation from Allele Frequencies (2012, methodology): “In addition, Frichot et al. (2013) have presented a Latent Factor Mixed Model that estimates the effect of population history and environmental correlations simultaneously.”