Improving the power of GWAS and avoiding confounding from population stratification with PC-Select

George Tucker; Alkes L Price; Bonnie Berger

doi:10.1534/genetics.114.164285

Improving the power of GWAS and avoiding confounding from population stratification with PC-Select

Genetics. 2014 Jul;197(3):1045-9. doi: 10.1534/genetics.114.164285. Epub 2014 Apr 29.

Authors

George Tucker¹, Alkes L Price², Bonnie Berger³

Affiliations

¹ *Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.
² †Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
³ *Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 [email protected].

Abstract

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models.

Keywords: GWAS; mixed models; population stratification.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Confounding Factors, Epidemiologic
Genetics, Population
Genome-Wide Association Study*
Humans
Linear Models
Models, Genetic
Multiple Sclerosis / genetics
Polymorphism, Single Nucleotide / genetics
Principal Component Analysis*

Abstract

Publication types

MeSH terms

Grants and funding