The maintenance of standing genetic variation: gene flow versus selective neutrality in Atlantic stickleback fish

Abstract

Adaptation to derived habitats often occurs from standing genetic variation (SGV). The maintenance within ancestral populations of genetic variants favorable in derived habitats is commonly ascribed to long-term antagonism between purifying selection and gene flow resulting from hybridization across habitats. A largely unexplored alternative idea based on quantitative genetic models of polygenic adaptation is that variants favored in derived habitats are neutral in ancestral populations when their frequency is relatively low. To explore the latter, we first identify genetic variants important to the adaptation of threespine stickleback fish to a rare derived habitat – nutrient-depleted acidic lakes – based on whole-genome sequence data. Sequencing marine stickleback from six locations across the Atlantic ocean then allows us to infer that the frequency of these derived variants in the ancestral habitat is unrelated to the likely opportunity for gene flow of these variants from acidic-adapted populations. This result is consistent with the selective neutrality of derived variants within the ancestor. Our study thus supports an underappreciated explanation for the maintenance of SGV, and calls for a better understanding of the fitness consequences of adaptive genetic variation across habitats and genomic backgrounds.

The maintenance of standing genetic variation: Gene flow vs. selective neutrality in Atlantic stickleback fish

“…Raw sequences reads (Haenel et al, 2019b , 2021 ) were parsed by library (pool or individual) and aligned to the third‐generation stickleback reference genome assembly (Glazer et al, 2015 ) by using novoalign (version 4.0, http://www.novocraft.com/products/novoalign/ ; alignment settings provided in the Supplementary Codes). From the alignments, we derived nucleotide counts (pileups) for all genome‐wide positions by using the pileup function from the Rsamtools r package (Morgan et al, 2017 ; unless specified otherwise, all analyses were implemented with the r language, version 3.6.0; r Development Core Team, 2019 ).…”