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Genome-wide scans for footprints of natural selection
Author(s) -
Tarás K. Oleksyk,
Michael W. Smith,
Stephen J. O’Brien
Publication year - 2009
Publication title -
philosophical transactions of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.753
H-Index - 272
eISSN - 1471-2970
pISSN - 0962-8436
DOI - 10.1098/rstb.2009.0219
Subject(s) - genome , biology , evolutionary biology , linkage disequilibrium , natural selection , population , human genome , computational biology , genetic variation , 1000 genomes project , genetics , selection (genetic algorithm) , gene , haplotype , single nucleotide polymorphism , allele , genotype , computer science , machine learning , demography , sociology
Detecting recent selected 'genomic footprints' applies directly to the discovery of disease genes and in the imputation of the formative events that molded modern population genetic structure. The imprints of historic selection/adaptation episodes left in human and animal genomes allow one to interpret modern and ancestral gene origins and modifications. Current approaches to reveal selected regions applied in genome-wide selection scans (GWSSs) fall into eight principal categories: (I) phylogenetic footprinting, (II) detecting increased rates of functional mutations, (III) evaluating divergence versus polymorphism, (IV) detecting extended segments of linkage disequilibrium, (V) evaluating local reduction in genetic variation, (VI) detecting changes in the shape of the frequency distribution (spectrum) of genetic variation, (VII) assessing differentiating between populations (F(ST)), and (VIII) detecting excess or decrease in admixture contribution from one population. Here, we review and compare these approaches using available human genome-wide datasets to provide independent verification (or not) of regions found by different methods and using different populations. The lessons learned from GWSSs will be applied to identify genome signatures of historic selective pressures on genes and gene regions in other species with emerging genome sequences. This would offer considerable potential for genome annotation in functional, developmental and evolutionary contexts.

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