The WZA: A window‐based method for characterizing genotype–environment associations

Genotype–environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extrem...

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Bibliographic Details
Published in:Molecular ecology resources 2024-02, Vol.24 (2), p.e13768-n/a
Main Authors: Booker, Tom R., Yeaman, Sam, Whiting, James R., Whitlock, Michael C.
Format: Article
Language:English
Subjects:
Adaptation
Adaptation, Physiological - genetics
Alleles
GEA
Gene Frequency
Gene polymorphism
Genetic markers
Genetics, Population
Genotype
Humans
landscape genomics
Linkage disequilibrium
local adaptation
Natural populations
Nucleotides
Pine trees
Polymorphism
Polymorphism, Single Nucleotide
Population genetics
Single-nucleotide polymorphism
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Summary:Genotype–environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the Weighted‐Z Analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST. Performing GEA methods in analysis windows has the advantage that it takes advantage of the increased linkage disequilibrium expected surrounding sites subject to local adaptation. We analyse simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single‐SNP (single nucleotide polymorphism)‐based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes were sampled. Particularly troubling, we found that some GEA methods exhibit very high false positive rates. We applied the WZA to previously published data from lodgepole pine and identified candidate loci that were identified in the original study alongside numerous loci that were not found in the original study. see also the Perspective by Kathrin A. Otte
ISSN:1755-098X
1755-0998
DOI:10.1111/1755-0998.13768