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

@article{citeulike:779318, abstract = {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.}, author = {Price, Alkes L. and Patterson, Nick J. and Plenge, Robert M. and Weinblatt, Michael E. and Shadick, Nancy A. and Reich, David}, citeulike-article-id = {779318}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/ng1847}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/ng1847}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/16862161}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=16862161}, day = {23}, doi = {10.1038/ng1847}, issn = {1061-4036}, journal = {Nature genetics}, keywords = {gwas, methods, pca, statistics}, month = {August}, number = {8}, pages = {904--909}, posted-at = {2009-09-23 13:01:47}, priority = {2}, publisher = {Nature Publishing Group}, title = {Principal components analysis corrects for stratification in genome-wide association studies.}, url = {http://dx.doi.org/10.1038/ng1847}, volume = {38}, year = {2006} }

TY - JOUR ID - citeulike:779318 L3 - citeulike-article-id:779318 N2 - 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. IS - 8 JF - Nature genetics SN - 1061-4036 EP - 909 TI - Principal components analysis corrects for stratification in genome-wide association studies. PB - Nature Publishing Group VL - 38 SP - 904 KW - gwas KW - methods KW - pca KW - statistics AU - Price, Alkes AU - Patterson, Nick AU - Plenge, Robert AU - Weinblatt, Michael AU - Shadick, Nancy AU - Reich, David PY - 2006/08/23/ UR - http://dx.doi.org/10.1038/ng1847 ER -