Learning natural selection from the site frequency spectrum.
Genetic adaptation to external stimuli occurs through the combined action of mutation and selection. A central problem in genetics is to identify loci responsive to specific selective constraints. Many tests have been proposed to identify the genomic signatures of natural selection by quantifying the skew in the site frequency spectrum (SFS) under selection relative to neutrality. We build upon recent work that connects many of these tests under a common framework, by describing how selective sweeps affect the scaled SFS. We show that the specific skew depends on many attributes of the sweep, including the selection coefficient and the time under selection. Using supervised learning on extensive simulated data, we characterize the features of the scaled SFS that best separate different types of selective sweeps from neutrality. We develop a test, SFselect, that consistently outperforms many existing tests over a wide range of selective sweeps. We apply SFselect to polymorphism data from a laboratory evolution experiment of Drosophila melanogaster adapted to hypoxia and identify loci that strengthen the role of the Notch pathway in hypoxia tolerance, but were missed by previous approaches. We further apply our test to human data and identify regions that are in agreement with earlier studies, as well as many novel regions.
['Ronen|Roy|R|', 'Udpa|Nitin|N|', 'Halperin|Eran|E|', 'Bafna|Vineet|V|']
D000222:Adaptation, Physiological D000818:Animals D029721:Drosophila Proteins D004331:Drosophila melanogaster D019143:Evolution, Molecular D005787:Gene Frequency D056426:Genetic Loci D006801:Humans D000860:Hypoxia D008957:Models, Genetic D011110:Polymorphism, Genetic D011153:Population D051880:Receptors, Notch D012641:Selection, Genetic
natural selection site frequency spectrum supervised learning