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Fifteen years of genomewide scans for selection: trends, lessons and unaddressed genetic sources of complication

期刊

MOLECULAR ECOLOGY
卷 25, 期 1, 页码 5-23

出版社

WILEY
DOI: 10.1111/mec.13339

关键词

genetic architecture; genomewide scans for selection; mutation; natural selection; recombination

资金

  1. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM007133, R01GM100426] Funding Source: NIH RePORTER
  2. NIGMS NIH HHS [T32 GM007133, R01 GM100426] Funding Source: Medline

向作者/读者索取更多资源

Genomewide scans for natural selection (GWSS) have become increasingly common over the last 15years due to increased availability of genome-scale genetic data. Here, we report a representative survey of GWSS from 1999 to present and find that (i) between 1999 and 2009, 35 of 49 (71%) GWSS focused on human, while from 2010 to present, only 38 of 83 (46%) of GWSS focused on human, indicating increased focus on nonmodel organisms; (ii) the large majority of GWSS incorporate interpopulation or interspecific comparisons using, for example F-ST, cross-population extended haplotype homozygosity or the ratio of nonsynonymous to synonymous substitutions; (iii) most GWSS focus on detection of directional selection rather than other modes such as balancing selection; and (iv) in human GWSS, there is a clear shift after 2004 from microsatellite markers to dense SNP data. A survey of GWSS meant to identify loci positively selected in response to severe hypoxic conditions support an approach to GWSS in which a list of a priori candidate genes based on potential selective pressures are used to filter the list of significant hits a posteriori. We also discuss four frequently ignored determinants of genomic heterogeneity that complicate GWSS: mutation, recombination, selection and the genetic architecture of adaptive traits. We recommend that GWSS methodology should better incorporate aspects of genomewide heterogeneity using empirical estimates of relevant parameters and/or realistic, whole-chromosome simulations to improve interpretation of GWSS results. Finally, we argue that knowledge of potential selective agents improves interpretation of GWSS results and that new methods focused on correlations between environmental variables and genetic variation can help automate this approach.

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