LEA: An R package for landscape and ecological association studies

Summary Based on population genomic and environmental data, genomewide ecological association studies aim at detecting allele frequencies that exhibit significant statistical association with ecological gradients. Ecological association studies can provide lists of genetic polymorphisms that are pot...

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Vydáno v:Methods in ecology and evolution Ročník 6; číslo 8; s. 925 - 929
Hlavní autoři: Frichot, Eric, François, Olivier, O'Meara, Brian
Médium: Journal Article
Jazyk:angličtina
Vydáno: London John Wiley & Sons, Inc 01.08.2015
Wiley
Témata:
Alleles
control of false discoveries
Datasets
Ecological association
ecological association studies
Ecological effects
Ecological studies
Environmental conditions
Gene frequency
Genetics
genome scans for signature of local adaptation
Genomes
Genotype & phenotype
inference of population structure
Life Sciences
Natural selection
Polymorphism
Population genetics
Population structure
Population studies
Populations and Evolution
Statistical analysis
ISSN:2041-210X, 2041-210X
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Shrnutí:Summary Based on population genomic and environmental data, genomewide ecological association studies aim at detecting allele frequencies that exhibit significant statistical association with ecological gradients. Ecological association studies can provide lists of genetic polymorphisms that are potentially involved in local adaptation to environmental conditions through natural selection. Here, we present the R package LEA that enables users to run ecological association studies from the R command line. The package can perform analyses of population structure and genome scans for adaptive alleles from large genomic data sets. It derives advantages from R programming functionalities to adjust significance values for multiple testing issues and to visualize results. This note also illustrates the main steps of ecological association studies and the typical use of LEA for analysing data sets based on R commands.
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ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.12382