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.
%0 Journal Article
%1 schrider2016robust
%A Schrider, Daniel R.
%A Kern, Andrew D.
%D 2016
%I Public Library of Science
%J PLOS Genetics
%K machine_learning methods scan_for_selection
%N 3
%P 1-31
%R 10.1371/journal.pgen.1005928
%T S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning
%U http://dx.doi.org/10.1371%2Fjournal.pgen.1005928
%V 12
%X 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.
@article{schrider2016robust,
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.},
added-at = {2017-01-07T00:55:05.000+0100},
author = {Schrider, Daniel R. and Kern, Andrew D.},
biburl = {https://www.bibsonomy.org/bibtex/28c0d74cc2b9394a44e6d4aee44e0c6b4/peter.ralph},
doi = {10.1371/journal.pgen.1005928},
interhash = {095fa68c2b6c710d0af3ab6b373e18a9},
intrahash = {8c0d74cc2b9394a44e6d4aee44e0c6b4},
journal = {PLOS Genetics},
keywords = {machine_learning methods scan_for_selection},
month = {03},
number = 3,
pages = {1-31},
publisher = {Public Library of Science},
timestamp = {2017-01-07T00:55:05.000+0100},
title = {{S/HIC}: Robust Identification of Soft and Hard Sweeps Using Machine Learning},
url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1005928},
volume = 12,
year = 2016
}