A composite of multiple signals distinguishes causal variants in regions of positive selection.

Bibliographic Collection:

MOCA Reference, APE

Publication Type: Journal Article

Authors: Grossman, Sharon R; Shlyakhter, Ilya; Shylakhter, Ilya; Karlsson, Elinor K; Byrne, Elizabeth H; Morales, Shannon; Frieden, Gabriel; Hostetter, Elizabeth; Angelino, Elaine; Garber, Manuel; Zuk, Or; Lander, Eric S; Schaffner, Stephen F; Sabeti, Pardis C

Year of Publication: 2010

Journal: Science

Volume: 327

Issue: 5967

Pagination: 883-6

Date Published: 2010 Feb 12

Publication Language: eng

ISSN: 1095-9203

Keywords: Computational Biology, DNA, Intergenic, Evolution, Molecular, Genetic Loci, Genetic Variation, Genome, Human, Haplotypes, Humans, Polymorphism, Genetic, Population Groups, Regulatory Sequences, Nucleic Acid, Selection, Genetic, Software

Abstract:

The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.

DOI: 10.1126/science.1183863

Custom 1:

http://www.ncbi.nlm.nih.gov/pubmed/20056855?dopt=Abstract

Alternate Journal: Science

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