Journal home
Advance online publication
Current issue
Archive
Press releases
Free Association (blog)
Supplements
Focuses
Guide to authors
Online submissionOnline submission
For referees
Free online issue
Contact the journal
Subscribe
Advertising
work@npg
Reprints and permissions
About this site
For librarians
 
NPG Resources
Nature
Nature Biotechnology
Nature Cell Biology
Nature Medicine
Nature Methods
Nature Reviews Cancer
Nature Reviews Genetics
Nature Reviews Molecular Cell Biology
news@nature.com
Nature Conferences
RNAi Gateway
NPG Subject areas
Biotechnology
Cancer
Chemistry
Clinical Medicine
Dentistry
Development
Drug Discovery
Earth Sciences
Evolution & Ecology
Genetics
Immunology
Materials Science
Medical Research
Microbiology
Molecular Cell Biology
Neuroscience
Pharmacology
Physics
Browse all publications
Article
Nature Genetics 38, 904 - 909 (2006)
Published online: 23 July 2006; | doi:10.1038/ng1847

Principal components analysis corrects for stratification in genome-wide association studies

Alkes L Price1, 2, Nick J Patterson2, Robert M Plenge2, 3, Michael E Weinblatt3, Nancy A Shadick3 & David Reich1, 2

1  Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.

2  Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.

3  Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.

Correspondence should be addressed to Alkes L Price aprice@broad.mit.edu

Population stratification—allele frequency differences between cases and controls due to systematic ancestry differences—can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.

 Top
Abstract
Previous | Next
Table of contents
Full textFull text
Download PDFDownload PDF
Send to a friendSend to a friend
rights and permissionsRights and permissions
CrossRef lists 3361 articles citing this articleCrossRef lists 3361 articles citing this article
Save this linkSave this link
Competing financial interests
Figures & Tables
Supplementary info
Export citation

natureevents

natureproducts

Search buyers guide:

 
Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
Journal home | Advance online publication | Current issue | Archive | Press releases | Supplements | Focuses | For authors | Online submission | Permissions | For referees | Free online issue | About the journal | Contact the journal | Subscribe | Advertising | work@npg | naturereprints | About this site | For librarians
Nature Publishing Group, publisher of Nature, and other science journals and reference works©2006 Nature Publishing Group | Privacy policy