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Nat Commun. 2018 Feb 19;9(1):703. doi: 10.1038/s41467-018-03100-7.

Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Author information

1
Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA. lauren_alpert@brown.edu.
2
Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA. lauren_alpert@brown.edu.
3
Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY, 11794, USA.
4
Division of Applied Mathematics, Brown University, Providence, RI, 02912, USA.
5
Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA.
6
Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, 02912, USA.
7
Center for Computational Molecular Biology, Brown University, Providence, RI, 02912, USA. sramachandran@brown.edu.
8
Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, 02912, USA. sramachandran@brown.edu.

Abstract

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios.

PMID:
29459739
PMCID:
PMC5818606
DOI:
10.1038/s41467-018-03100-7
[Indexed for MEDLINE]
Free PMC Article

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