A Composite of Multiple Signals Distinguishes Causal Variants in Regions of Positive Selection
Author(s) -
Sharon R. Grossman,
Ilya Shylakhter,
Elinor K. Karlsson,
Elizabeth H. Byrne,
Shan J. Morales,
Gabriel Frieden,
Elizabeth Hostetter,
Elaine Angelino,
Manuel Garber,
Or Zuk,
Eric S. Lander,
S. F. Schaffner,
Pardis C. Sabeti
Publication year - 2010
Publication title -
science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.556
H-Index - 1186
eISSN - 1095-9203
pISSN - 0036-8075
DOI - 10.1126/science.1183863
Subject(s) - selection (genetic algorithm) , composite number , computational biology , genetics , biology , evolutionary biology , computer science , artificial intelligence , algorithm
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.
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