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

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 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.

Genomics, environment and balancing selection in behaviourally bimodal populations: The caribou case

“…Numerous analytical methods to detect association between SNPs and environmental variables have been developed in past years, all with strengths and limitations (i.e., also including Bayesian statistics approaches, Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ). Forester, Lasky, Wagner, and Urban () recently compared methods and promoted redundancy analysis (RDA). Nonetheless, the authors indicated important limitations of RDA too, in particular when used with structured populations, which should be the case in our sample including multiple populations some of which are known to be isolated (Weckworth et al, ).…”

Seascape genomics of eastern oyster (Crassostrea virginica) along the Atlantic coast of Canada

“…We also performed a RDA as a multilocus genotype–environment association method to detect loci putatively under selection based on correlations with environmental variables. This approach can detect (even weak) multilocus signatures of selection for multiple environmental predictors, especially compared to differentiation‐based outlier detection methods (Forester, Lasky, Wagner, & Urban, ; Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ). We used genotypes for all SNPs (one SNP per locus) and the same environmental variables as in the previous RDAs.…”

Recent advances in conservation and population genomics data analysis

“…The reasons for the popularity of GEA analyses are practical: They require no phenotypic data or prior genomic resources, do not require experimental approaches (such as reciprocal transplants) to demonstrate local adaptation, and are often more powerful than differentiation‐based outlier detection methods (De Mita et al., 2013; de Villemereuil, Frichot, Bazin, François, & Gaggiotti, 2014; Forester, Lasky, Wagner, & Urban, 2018; Lotterhos & Whitlock, 2015). In particular, participants considered how and why detection rates differed between univariate and multivariate GEAs, exploring the use of latent factor mixed models (Frichot, Schoville, Bouchard, & Francois, 2013) and redundancy analysis (Forester, Jones, Joost, Landguth, & Lasky, 2016; Lasky et al., 2012), respectively.…”

“…In particular, participants considered how and why detection rates differed between univariate and multivariate GEAs, exploring the use of latent factor mixed models (Frichot, Schoville, Bouchard, & Francois, 2013) and redundancy analysis (Forester, Jones, Joost, Landguth, & Lasky, 2016; Lasky et al., 2012), respectively. Recent work has shown that RDA is an effective means of detecting adaptive processes that result in weak, multilocus molecular signatures (Forester et al., 2018), providing a powerful tool for investigating the genetic basis of local adaptation and informing management actions to conserve evolutionary potential (Flanagan et al., 2017; Harrisson et al., 2014; Hoffmann et al., 2015). Finally, participants were encouraged to move beyond simply documenting candidate adaptive loci in their datasets, and instead focus on the ecological, evolutionary, and management‐relevant questions that can be addressed by more fully integrating a landscape genomic analytical framework.…”