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Genome Res. 2006 Jun;16(6):702-12. Epub 2006 May 10.

How reliable are empirical genomic scans for selective sweeps?

Author information

  • 1Department of Human Genetics, University of Chicago, Chicago, Illinois 60637, USA. kteshima@uchicago.edu

Abstract

The beneficial substitution of an allele shapes patterns of genetic variation at linked sites. Thus, in principle, adaptations can be mapped by looking for the signature of directional selection in polymorphism data. In practice, such efforts are hampered by the need for an accurate characterization of the demographic history of the species and of the effects of positive selection. In an attempt to circumvent these difficulties, researchers are increasingly taking a purely empirical approach, in which a large number of genomic regions are ordered by summaries of the polymorphism data, and loci with extreme values are considered to be likely targets of positive selection. We evaluated the reliability of the "empirical" approach, focusing on applications to human data and to maize. To do so, we considered a coalescent model of directional selection in a sensible demographic setting, allowing for selection on standing variation as well as on a new mutation. Our simulations suggest that while empirical approaches will identify several interesting candidates, they will also miss many--in some cases, most--loci of interest. The extent of the trade-off depends on the mode of positive selection and the demographic history of the population. Specifically, the false-discovery rate is higher when directional selection involves a recessive rather than a co-dominant allele, when it acts on a previously neutral rather than a new allele, and when the population has experienced a population bottleneck rather than maintained a constant size. One implication of these results is that, insofar as attributes of the beneficial mutation (e.g., the dominance coefficient) affect the power to detect targets of selection, genomic scans will yield an unrepresentative subset of loci that contribute to adaptations.

PMID:
16687733
[PubMed - indexed for MEDLINE]
PMCID:
PMC1473181
Free PMC Article

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