Biology Faculty Articles

Title

Genome-Wide Scans for Footprints of Natural Selection

Document Type

Article

Publication Date

1-12-2010

Publication Title

Philosophical Transactions B: Biological Sciences

Keywords

Genomes, Genome-wide selection scans, Whole genome sequences, Candidate genes, Human populations, Vertebrate species

ISSN

0962-8436

Volume

365

Issue/No.

1537

First Page

185

Last Page

205

Abstract

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 (FST), 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.

Comments

©2010 The Royal Society

Additional Comments

National Cancer Institute contract #: N01-CO-12400

ORCID ID

0000-0001-7353-8301

ResearcherID

N-1726-2015

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Peer Reviewed

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