Predictable genome-wide sorting of standing genetic variation during parallel adaptation to basic versus acidic environments in stickleback fish

Abstract

Genomic studies of parallel (or convergent) evolution often compare multiple populations diverged into two ecologically different habitats to search for loci repeatedly involved in adaptation. Because the shared ancestor of these populations is generally unavailable, the source of the alleles at adaptation loci, and the direction in which their frequencies were shifted during evolution, remain elusive. To shed light on these issues, we here use multiple populations of threespine stickleback fish adapted to two different types of derived freshwater habitats—basic and acidic lakes on the island of North Uist, Outer Hebrides, Scotland—and the present‐day proxy of their marine ancestor. In a first step, we combine genome‐wide pooled sequencing and targeted individual‐level sequencing to demonstrate that ecological and phenotypic parallelism in basic‐acidic divergence is reflected by genomic parallelism in dozens of genome regions. Exploiting data from the ancestor, we next show that the acidic populations, residing in ecologically more extreme derived habitats, have adapted by accumulating alleles rare in the ancestor, whereas the basic populations have retained alleles common in the ancestor. Genomic responses to selection are thus predictable from the ecological difference of each derived habitat type from the ancestral one. This asymmetric sorting of standing genetic variation at loci important to basic‐acidic divergence has further resulted in more numerous selective sweeps in the acidic populations. Finally, our data suggest that the maintenance in marine fish of standing variation important to adaptive basic‐acidic differentiation does not require extensive hybridization between the marine and freshwater populations. Overall, our study reveals striking genome‐wide determinism in both the loci involved in parallel divergence, and in the direction in which alleles at these loci have been selected.

Allele Frequency Difference AFD–An Intuitive Alternative to FST for Quantifying Genetic Population Differentiation

“…The focus of this paper, however, lies on standard bi-allelic SNPs, given that this type of polymorphism has become the predominant genetic marker. A worked example of AFD calculation for both a bi-allelic SNP and a multi-allelic microsatellite is provided as Analysis S1 in the Supplementary Materials (for applications of AFD in recent genomic investigations see References [23,24,25,26,27]).…”

“…With AFD as a differentiation metric, this option is not available. A straightforward ad hoc solution, however, is to simply average multiple AFD values for SNPs or genome windows across the multiple population contrasts of interest [27].…”

Genomic basis of parallel adaptation varies with divergence inArabidopsisand its relatives

“…1). Moreover, we found that allele reuse (repeated selection of the same haplotype, shared either via gene flow or from standing genetic variation) frequently underlies parallel adaptation between closely related lineages [29][30][31][32] , while parallelism from de-novo mutations dominates between distantly related taxa 13 . This suggests that the degree of allele reuse may be the primary mechanism behind the hypothesized divergence-dependency of parallel genome evolution, possibly reflecting either genetic (weak hybridization barriers, widespread ancestral polymorphism between closely related lineages 33 ) or ecological reasons (lower niche differentiation and geographical proximity 34,35 ).…”

Shared ancestral polymorphisms and chromosomal rearrangements as potential drivers of local adaptation in a marine fish

“…Classic population genetics theory states that de novo mutations are the raw source of genetic adaptation (Barton, 1998; Kaplan, Hudson, & Langley, 1989; Smith & Haigh, 1974) for which empirical evidence has been previously reported (e.g., Linnen, Kingsley, Jensen, & Hoekstra, 2009). Nevertheless, increasing evidence suggests that standing genetic variation plays a central role in adaptation (Bitter, Kapsenberg, Gattuso, & Pfister, 2019; Haenel, Roesti, Moser, MacColl, & Berner, 2019; Jones et al., 2012; Lai et al, 2019) and might allow faster adaptation to a new environment than de novo mutation (Barrett & Schluter, 2008). Finally, adaptive introgression may also contribute to local adaptation when new mutants or variants from standing genetic variation with beneficial effects on fitness are introduced in the population through interbreeding with related taxa (Hedrick, 2013; Oziolor et al., 2019; Racimo, Sankararaman, Nielsen, & Huerta‐Sánchez, 2015).…”