A fully integrated machine learning scan of selection in the chimpanzee genome

Abstract

After diverging, each chimpanzee subspecies has been the target of unique selective pressures. Here, we employ a machine learning approach to classify regions as under positive selection or neutrality genome-wide. The regions determined to be under selection reflect the unique demographic and adaptive history of each subspecies. The results indicate that effective population size is important for determining the proportion of the genome under positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and gene regulation. With these results, we have created a selection map for each population that can be displayed in a genome browser (www.hsb.upf.edu/chimp_browser). This study is the first to use a detailed demographic history and machine learning to map selection genome-wide in chimpanzee. The chimpanzee selection map will improve our understanding of the impact of selection on closely related subspecies and will empower future studies of chimpanzee.

Soft sweeps predominate recent positive selection in bonobos (Pan paniscus) and chimpanzees (Pan troglodytes)

“…Further, there were nearly twice the number of shared soft sweep windows shared between eastern and central chimpanzees. These results are similar to other recent findings (Nye et al 2020). It is impossible to discern whether or not the overlap in hard sweeps between central and Nigeria-Cameroon chimpanzees and the overlap in soft sweeps for eastern and central chimpanzees is the result of shared ancestry and/or similar environmental conditions because both pairs of lineages share a geographic boundary: the Ubangi river for eastern and central chimpanzees and Sanaga river for central and Nigeria-Cameroon chimpanzees.…”

“…Further, there were nearly twice the number of shared soft sweep windows shared between eastern and central chimpanzees. These results are similar to other recent findings (Nye et al 2020 As mentioned above, soft sweeps are not exclusively the result of selection on standing genetic variation (Pennings and Hermisson 2006a;Pennings and Hermisson 2006b). However, given the mutation rates estimated for bonobos and chimpanzees, it appears unlikely that recurrent de novo mutations explain the majority of these soft sweeps.…”

“…In this study, we apply a recently developed supervised machine-learning approach to population-level genomic data for bonobos ( Pan paniscus ) and chimpanzees ( Pan troglodytes ) to assess the extent of different completed sweep types in these species. While a few studies have examined recent positive selection in bonobos and chimpanzees (e.g., Cagan et al 2016; Han et al 2019; Schmidt et al 2019; Kovalaskas et al 2020; Nye et al 2020), the role of hard and soft sweeps in shaping their adaptations is currently unknown. We sought to categorize genomic regions as subject to recent hard or soft sweeps, as linked to recent hard or soft selective sweeps, or as evolving neutrally.…”