NDLI: Detecting natural selection by empirical comparison to random regions of the genome

Content Provider	PubMed Central
Author	Yu, Fuli Keinan, Alon Chen, Hua Ferland, Russell J. Hill, Robert S. Mignault, Andre A. Walsh, Christopher A. Reich, David
Abstract	Historical episodes of natural selection can skew the frequencies of genetic variants, leaving a signature that can persist for many tens or even hundreds of thousands of years. However, formal tests for selection based on allele frequency skew require strong assumptions about demographic history and mutation, which are rarely well understood. Here, we develop an empirical approach to test for signals of selection that compares patterns of genetic variation at a candidate locus with matched random regions of the genome collected in the same way. We apply this approach to four genes that have been implicated in syndromes of impaired neurological development, comparing the pattern of variation in our re-sequencing data with a large-scale, genomic data set that provides an empirical null distribution. We confirm a previously reported signal at FOXP2, and find a novel signal of selection centered at AHI1, a gene that is involved in motor and behavior abnormalities. The locus is marked by many high frequency derived alleles in non-Africans that are of low frequency in Africans, suggesting that selection at this or a closely neighboring gene occurred in the ancestral population of non-Africans. Our study also provides a prototype for how empirical scans for ancient selection can be carried out once many genomes are sequenced.
Related Links	http://dx.doi.org/10.1093/hmg/ddp457
Ending Page	4867
Page Count	15
Starting Page	4853
File Format	PDF
ISSN	09646906
e-ISSN	14602083
Journal	Human Molecular Genetics
Issue Number	24
Volume Number	18
Language	English
Publisher	Oxford University Press
Publisher Date	2009-12-15
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Genetics(clinical) Genetics Molecular Biology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics Molecular Biology Genetics (clinical)

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