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

View ORCID ProfileBrenna R. Forester, Jesse R. Lasky, Helene H. Wagner, Dean L. Urban
doi: https://doi.org/10.1101/129460
Brenna R. Forester
1Duke University, Nicholas School of the Environment, Durham, NC 27708, USA.
2Current location: Colorado State University, Department of Biology, Fort Collins, CO 80523, USA.
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Jesse R. Lasky
3Pennsylvania State University, Department of Biology, University Park, PA 16802, USA.
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Helene H. Wagner
4University of Toronto Mississauga, Department of Biology, Mississauga, ON, Canada
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Dean L. Urban
1Duke University, Nicholas School of the Environment, Durham, NC 27708, USA.
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Abstract

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 analyze many loci simultaneously, may be better suited to these data since 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 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 levels of population structure tested. The value of combining detections from different methods was variable, and depended on study goals and knowledge of the drivers of selection. Reanalysis of genomic data from gray wolves highlighted the unique, covarying sets of adaptive loci that could be identified using redundancy analysis, a constrained ordination. Although additional testing is needed, this study indicates that constrained ordinations are 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 17, 2017.
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Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations
Brenna R. Forester, Jesse R. Lasky, Helene H. Wagner, Dean L. Urban
bioRxiv 129460; doi: https://doi.org/10.1101/129460
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Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations
Brenna R. Forester, Jesse R. Lasky, Helene H. Wagner, Dean L. Urban
bioRxiv 129460; doi: https://doi.org/10.1101/129460

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