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

Publication ,  Journal Article
Forester, BR; Lasky, JR; Wagner, HH; Urban, DL
Published in: Molecular ecology
May 2018

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false-positive and high true-positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re-analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

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Published In

Molecular ecology

DOI

EISSN

1365-294X

ISSN

0962-1083

Publication Date

May 2018

Volume

27

Issue

9

Start / End Page

2215 / 2233

Related Subject Headings

  • Selection, Genetic
  • Multivariate Analysis
  • Genotype
  • Genomics
  • Evolutionary Biology
  • Computer Simulation
  • Adaptation, Biological
  • 31 Biological sciences
  • 06 Biological Sciences
 

Citation

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Forester, B. R., Lasky, J. R., Wagner, H. H., & Urban, D. L. (2018). Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Molecular Ecology, 27(9), 2215–2233. https://doi.org/10.1111/mec.14584
Forester, Brenna R., Jesse R. Lasky, Helene H. Wagner, and Dean L. Urban. “Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations.Molecular Ecology 27, no. 9 (May 2018): 2215–33. https://doi.org/10.1111/mec.14584.
Forester BR, Lasky JR, Wagner HH, Urban DL. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Molecular ecology. 2018 May;27(9):2215–33.
Forester, Brenna R., et al. “Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations.Molecular Ecology, vol. 27, no. 9, May 2018, pp. 2215–33. Epmc, doi:10.1111/mec.14584.
Forester BR, Lasky JR, Wagner HH, Urban DL. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Molecular ecology. 2018 May;27(9):2215–2233.
Journal cover image

Published In

Molecular ecology

DOI

EISSN

1365-294X

ISSN

0962-1083

Publication Date

May 2018

Volume

27

Issue

9

Start / End Page

2215 / 2233

Related Subject Headings

  • Selection, Genetic
  • Multivariate Analysis
  • Genotype
  • Genomics
  • Evolutionary Biology
  • Computer Simulation
  • Adaptation, Biological
  • 31 Biological sciences
  • 06 Biological Sciences