Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps

Abstract

Numerous studies of emerging species have identified genomic “islands” of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward “speciation genes” that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last ∼50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be soft sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed light on the complex manner in which natural selection shapes genome sequences during speciation.

SIA: Selection Inference Using the Ancestral Recombination Graph

“…Figure S2 reports the ROC curves for the task of distinguishing partial soft sweeps from neutral regions. Despite soft sweeps being harder to detect, the classifier achieved good performance in the moderate-to-strong selection regimes (s = 0.005 and s = 0.0075) where the accuracy ranged between 82% and 96%, a substantial improvement over the previous accuracy of 56% [31]. SIA performed particularly well in identifying partial soft sweeps when the site under selection was at a high segregating frequency.…”

“…In our previous work on southern capuchino seedeaters [31] (see Methods), we applied newly developed statistical methods for ancestral recombination graph inference and machine-learning for the prediction of selective sweeps. We found evidence suggesting that a substantial fraction of soft sweeps are partial but had limited power to identify them (i.e.…”

“…In addition, we validated the ability of SIA to classify genomic regions with additional test sets simulated under a demographic model for southern capuchinos, a group of songbirds in which we previously identified and characterized many examples of sweeps [31], finding a predominance of "soft" rather than "hard" sweeps (meaning that they tend to be based on standing genetic variation rather than new mutations; see Methods). Figure S2 reports the ROC curves for the task of distinguishing partial soft sweeps from neutral regions.…”

“…Localizing sweeps in southern capuchino seedeaters. We recently applied a combination of ARG inference and machine-learning methods for identifying selective sweeps to study previously identified "islands of differentiation" in southern capuchino seedeaters and distinguish among possible evolutionary scenarios leading to their formation [31]. Taking advantage of its improved power and genomic resolution, we applied SIA to sequence data for the species for which we have the most samples, Sporophila hypoxantha.…”

“…Applying SIA to data from the 1000 Genomes Northern and Western European (CEU) population, we identified new and known loci under positive selection that are associated with a variety of phenotypes and estimated selection coefficients at these loci. In addition, using SIA, we built on our previous work [31] on a bird species-complex in the genus Sporophila by elucidating the strength and targets of selection at specific loci tied to a collection of rapid speciation events. Overall, SIA is the first method that couples ARG-based features with a machine-learning approach for population genetic inference.…”