TY - JOUR T1 - Using genotype-environment associations to identify multilocus local adaptation JF - bioRxiv DO - 10.1101/129460 SP - 129460 AU - Brenna R. Forester AU - Jesse R. Lasky AU - Helene H. Wagner AU - Dean L. Urban Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/04/21/129460.abstract N2 - Identifying loci under selection can provide insight into the mechanisms underlying local adaptation and inform management decisions for agricultural, natural resources, and conservation applications. Genotype-environment association (GEA) methods, which identify adaptive loci based on associations between genetic data and environmental variables, are particularly promising for distinguishing these loci. Univariate statistical methods have dominated GEA, despite the high dimensional nature of genomic data sets. Multivariate and machine learning methods, which can analyze many loci simultaneously, may be better suited to these large data sets since they consider how groups of markers covary in response to environmental predictors. These methods may also be more effective at detecting important adaptive processes, such as selection on standing genetic variation, that result in weak, multilocus signatures. Here we evaluate four of these methods, as well as a popular univariate approach, using published simulations of multilocus selection. We found that the machine learning method, Random Forest, performed poorly as a GEA. The univariate approach 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 demographic history, sampling designs, and sample sizes. Although further testing is needed on more complex genetic architectures, this study indicates that constrained ordinations are an effective means of detecting adaptive processes that result in weak, multilocus molecular signatures, providing a powerful tool for investigating the genetic basis of local adaptation and improving management actions. ER -