Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models
| Autor: | Eric Frichot, Sean D. Schoville, Guillaume Bouchard, Olivier François |
|---|---|
| Přispěvatelé: | Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), Xerox Research Centre Europe [Meylan], Xerox Company, Biologie Computationnelle et Mathématique (TIMC-IMAG-BCM), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011) |
| Rok vydání: | 2013 |
| Předmět: |
FOS: Computer and information sciences
environmental correlations 0106 biological sciences Mixed model Population Population genetics Computational biology Environment Biology Statistics - Computation 010603 evolutionary biology 01 natural sciences 03 medical and health sciences latent factor models Genetic variation Methods Genetics Humans Selection Genetic Gene–environment interaction Quantitative Biology - Populations and Evolution education Molecular Biology Computation (stat.CO) Ecology Evolution Behavior and Systematics 030304 developmental biology Local adaptation 0303 health sciences education.field_of_study [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE] Polymorphism Genetic genome scans Natural selection Ecology Populations and Evolution (q-bio.PE) Genetic Variation population structure Models Theoretical Random effects model Adaptation Physiological Genetics Population FOS: Biological sciences Gene-Environment Interaction Algorithms local adaptation |
| Zdroj: | Molecular Biology and Evolution Molecular Biology and Evolution, Oxford University Press (OUP), 2013, 30 (7), pp.1687-99. ⟨10.1093/molbev/mst063⟩ |
| ISSN: | 1537-1719 0737-4038 |
| DOI: | 10.1093/molbev/mst063 |
| Popis: | 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. Comment: 29 pages with 8 pages of Supplementary Material (V2 revised presentation and results part) |
| Databáze: | OpenAIRE |
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