This webpage provides the results of the genome-wide positive selection scan performed in this article:
A fully integrated machine learning scan of selection in the Chimpanzee genome
Jessica Nye, Mayukh Mondal, Jaume Bertranpetit*, and Hafid Laayouni*
* Corresponding authors. Email: firstname.lastname@example.org or email@example.comABSTRACT
After the divergence of the four subspecies of chimpanzees, which began half a million years ago, each subspecies has been targeted by unique selective pressures. Here, we train a machine learning algorithm with 15 statistics that detect positive selection based on sequence variation, linkage disequilibrium decay, and population differentiation calculated from extensive simulations of selective scenarios occurring between present time and 60 thousand years ago. We are able to robustly categorize regions of the genome as under selection in each of the four subspecies. Our results reflect the unique demographic and adaptive history of each population and we find more shared signals in subspecies that diverged more recently. We observe that the effective population of these subspecies is important in determining the number of signals of positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and muscle function phenotypes. However, we find the majority of signals to be subspecies specific targeting genes associated with male reproduction and DNA repair. We identify and discuss likely functional variants in genes under selection. With these results, we have created a genome browser that can be used as a community resource. This study is the first to use a detailed demographic history in order to incorporate simulations and several tests of selection genome-wide in chimpanzee.