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Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations.

Brenna R Forester1, Jesse R Lasky2, Helene H Wagner3

  • 1Nicholas School of the Environment, Duke University, Durham, North Carolina.

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|April 11, 2018
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Summary
This summary is machine-generated.

Identifying adaptive loci helps understand local adaptation. Redundancy analysis (RDA) effectively detects genetic adaptation, even with weak, multilocus selection signatures, outperforming other genotype-environment association methods.

Keywords:
constrained ordinationlandscape genomicsnatural selectionrandom forestredundancy analysissimulations

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Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Identifying adaptive loci is crucial for understanding local adaptation mechanisms.
  • Genotype-environment association (GEA) methods correlate genetic and environmental data to find these loci.
  • Multivariate GEA methods, analyzing multiple loci simultaneously, are potentially better suited for high-dimensional genetic and environmental data than univariate methods.

Purpose of the Study:

  • To evaluate the performance of various multivariate, univariate, and differentiation-based GEA methods.
  • To identify the most effective methods for detecting local adaptation, especially weak, multilocus selection signatures.
  • To assess the robustness of these methods across different demographic histories and sampling designs.

Main Methods:

  • Comparative analysis of four multivariate and five univariate/differentiation-based GEA methods.
  • Utilized published simulation data of multilocus selection.
  • Applied redundancy analysis (RDA) to genomic data from grey wolves.

Main Results:

  • Random Forest showed poor performance in GEA.
  • Univariate GEA methods had limited detection rates for loci under weak selection.
  • Constrained ordinations, particularly RDA, demonstrated superior performance with low false-positive and high true-positive rates for detecting adaptation across selection levels.

Conclusions:

  • Redundancy analysis (RDA) is a highly effective method for detecting local adaptation, including weak, multilocus selection.
  • RDA offers a powerful tool for investigating the genetic basis of local adaptation by identifying covarying adaptive loci.
  • The study highlights RDA's utility, supported by robust performance across various simulation parameters and a real-world case study.