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PMID: 17668374 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Combining evidence of natural selection with association analysis increases power to detect malaria-resistance variants.

American journal of human genetics ·Vol. 81 ·No. 2 ·2007-08-00 ·Pages 234-42

Ayodo G, Price AL, Keinan A, Ajwang A, Otieno MF, Orago AS, Patterson N, Reich D

Abstract

Statistical power to detect disease variants can be increased by weighting candidates by their evidence of natural selection. To demonstrate that this theoretical idea works in practice, we performed an association study of 10 putative resistance variants in 471 severe malaria cases and 474 controls from the Luo in Kenya. We replicated associations at HBB (P=.0008) and CD36 (P=.03) but also showed that the same variants are unusually differentiated in frequency between the Luo and Yoruba (who historically have been exposed to malaria) and the Masai and Kikuyu (who have not been exposed). This empirically demonstrates that combining association analysis with evidence of natural selection can increase power to detect risk variants by orders of magnitude--up to P=.000018 for HBB and P=.00043 for CD36.

MeSH Terms
Adolescent Adult Case-Control Studies Child Child, Preschool Female Gene Frequency Genetic Variation Humans Immunity, Innate/genetics Infant Kenya Linkage Disequilibrium Malaria/genetics Male Selection, Genetic Statistics as Topic
Authors & Affiliations
8 authors, click to expand affiliations / ORCID
Ayodo George
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Price Alkes L
Keinan Alon
Ajwang Arthur
Otieno Michael F
Orago Alloys S S
Patterson Nick
Reich David
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Article Info
Journal
American journal of human genetics
Abbr.
Am J Hum Genet
ISSN
0002-9297
DOI
10.1086/519221
Published
2007-08-00
Epub
2007-00-15
Pages
234-42
Language
English
Region
United States
NLM ID
0370475
PMCID
PMC1950820
Subset
IM
Grants
NIAID NIH HHS · R21 AI064519 · United States
NIAID NIH HHS · R21-AI64519 · United States
External Links
PubMed source DOI: 10.1086/519221 PMC Full Text View journal details
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