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The WZA: A window-based method for characterizing genotype-environment associations.

Tom R Booker1,2,3, Sam Yeaman1, James R Whiting1

  • 1Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.

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|February 14, 2023
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Summary
This summary is machine-generated.

We developed Weighted-Z Analysis (WZA), a new method for genotype-environment association (GEA) studies. WZA improves the identification of genetic adaptations by analyzing linked sites, outperforming single-SNP methods, especially in small populations.

Keywords:
GEAlandscape genomicslocal adaptationpopulation genetics

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Genomics

Background:

  • Genotype-environment association (GEA) studies identify genetic adaptations to local environments by correlating allele frequencies with environmental factors.
  • Genetic markers showing strong associations are presumed to indicate genes involved in local adaptation.
  • Existing GEA methods often analyze single nucleotide polymorphisms (SNPs) independently.

Purpose of the Study:

  • To introduce a novel GEA method, Weighted-Z Analysis (WZA), designed to enhance the detection of local adaptation.
  • To compare the performance of WZA against existing single-SNP GEA methods using simulations.
  • To assess the efficacy of WZA in identifying adaptive loci in real-world population data.

Main Methods:

  • WZA combines information from linked genetic sites into analysis windows, inspired by FST calculation methods.
  • Simulations of local adaptation in heterogeneous environments were used to test WZA against single-SNP approaches.
  • The WZA was applied to existing genotype and environmental data from lodgepole pine populations.

Main Results:

  • WZA generally outperformed or performed comparably to single-SNP methods in simulations.
  • WZA showed particular advantages when analyzing small sample sizes (individuals or demes).
  • Several GEA methods demonstrated high false positive rates, highlighting the need for robust approaches like WZA.

Conclusions:

  • WZA is an effective method for GEA studies, improving the detection of local adaptation.
  • The window-based approach of WZA leverages linkage disequilibrium around adaptive loci.
  • WZA identified known and novel candidate adaptive loci in lodgepole pine, demonstrating its practical utility.