Article,
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning
PLOS Genetics, 12 (3): 1-31 (March 2016)
DOI: 10.1371/journal.pgen.1005928
Abstract
Author Summary The genetic basis of recent adaptation can be uncovered from genomic patterns of variation, which are perturbed in predictable ways when a beneficial mutation “sweeps” through a population. However, the detection of such “selective sweeps” is complicated by demographic events, such as population expansion, which can produce similar skews in genetic diversity. Here, we present a method for detecting selective sweeps that is remarkably powerful and robust to potentially confounding demographic histories. This method, called S/HIC, operates using a machine learning paradigm to combine many different features of population genetic variation, and examine their values across a large genomic region in order to infer whether a selective sweep has recently occurred near its center. S/HIC is also able to accurately distinguish between selection acting on de novo beneficial mutations (“hard sweeps”) and selection on previously standing variants (“soft sweeps”). We demonstrate S/HIC’s power on a variety of simulated datasets as well as human population data wherein we recover several previously discovered targets of recent adaptation as well as a novel selective sweep.
