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A haplotype method detects diverse scenarios of local adaptation from genomic sequence variation
Author(s) -
Lange Jeremy D.,
Pool John E.
Publication year - 2016
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.13671
Subject(s) - biology , haplotype , population , statistic , genetics , evolutionary biology , selection (genetic algorithm) , locus (genetics) , genetic variation , allele frequency , selective sweep , allele , statistics , gene , computer science , machine learning , mathematics , demography , sociology
Identifying genomic targets of population‐specific positive selection is a major goal in several areas of basic and applied biology. However, it is unclear how often such selection should act on new mutations versus standing genetic variation or recurrent mutation, and furthermore, favoured alleles may either become fixed or remain variable in the population. Very few population genetic statistics are sensitive to all of these modes of selection. Here, we introduce and evaluate the Comparative Haplotype Identity statistic (χ MD ), which assesses whether pairwise haplotype sharing at a locus in one population is unusually large compared with another population, relative to genomewide trends. Using simulations that emulate human and Drosophila genetic variation, we find that χ MD is sensitive to a wide range of selection scenarios, and for some very challenging cases ( e.g . partial soft sweeps), it outperforms other two‐population statistics. We also find that, as with F ST , our haplotype approach has the ability to detect surprisingly ancient selective sweeps. Particularly for the scenarios resembling human variation, we find that χ MD outperforms other frequency‐ and haplotype‐based statistics for soft and/or partial selective sweeps. Applying χ MD and other between‐population statistics to published population genomic data from D. melanogaster , we find both shared and unique genes and functional categories identified by each statistic. The broad utility and computational simplicity of χ MD will make it an especially valuable tool in the search for genes targeted by local adaptation.