The search for loci under selection: trends, biases and progress

Abstract

Detecting genetic variants under selection using F outlier analysis (OA) and environmental association analyses (EAAs) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field-based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analysed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographical coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP-based selection studies and help to progress landscape and evolutionary genomics.

Signatures of local adaptation along environmental gradients in a range‐expanding damselfly (Ischnura elegans)

“…Evolutionary and landscape genomics approaches have recently enabled the characterization of the role of environmental variables in explaining signatures of local adaptation at the molecular level (Ahrens et al., ; Hoban et al., ; Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ). Searching for loci underpinning local adaptation is a formidable challenge that has become increasingly accessible via new analytical tools that identify loci with higher than expected genetic divergence among populations ( F ST outlier tests, e.g., Foll & Gaggiotti, ; Whitlock & Lotterhos, ) or exhibit high correlation with spatially explicit environmental variables (environmental association analysis; EAA; Rellstab et al., ), while accounting for neutral genetic structure.…”

“…Analytical tools to translate genomic information into signatures of local adaptation have only recently been developed and few empirical applications have been presented (Creech et al., ; Fitzpatrick & Keller, ; Landguth, Bearlin, Day, & Dunham, ). This may be partially due to a lack of data sets with appropriate sampling designs at both the genomic and the spatial scales that are needed to test for selection processes along environmental gradients (Ahrens et al., ; Hoban et al., ; Rellstab et al., ). However, characterizing variation in selection and local adaptation across environmental gradients is a necessary next step in evolutionary and landscape genomics, which will inform conservation management of biodiversity (Hoffmann & Sgro, ; Hoffmann et al., ).…”

Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species

“…Although local adaptation is not ubiquitous (Havens et al, ; Hereford, ; Leimu & Fischer, ), there is supporting evidence from ecological transplants (Leimu & Fischer, ), experimental manipulations (Simon et al, ), genetic surveys (Rellstab et al, ; Sork et al, ) and genomic studies (Lasky et al, ). Recently developed environmental association analytical methods (Ahrens et al, ; Hoban et al, ; Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ) can assist in identifying patterns of local adaptation, highlighting genetic variants associated with environmental factors. It is important to note that these associations between genetic variants and climate do not explicitly indicate the presence of local adaptation because fitness cannot be directly linked to climate (Sork, ).…”

Detection of environmental and morphological adaptation despite high landscape genetic connectivity in a pest grasshopper (Phaulacridium vittatum)

“…Landscape genomics approaches allow for complementary assessment of how environmental and landscape features influence genetic connectivity (Manel & Holderegger, ), and environmental selection processes (Ahrens et al, ; Balkenhol et al, ). Recent advancements in statistical approaches such as environmental association analyses (EAAs) allow for the correlation between allele frequency and environmental variables to be tested (Coop, Witonsky, Di Rienzo, & Pritchard, ; Frichot, Schoville, Bouchard, & Francois, ; Gunther & Coop, ), and may identify selection on many genes of small effect (Rellstab, Gugerli, Eckert, Hancock, & Holderegger, ).…”