Improving the power of GWAS and avoiding confounding from population stratification with PC-Select.
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.
*Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.
George
G
Tucker
†Department of Epidemiology and Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Alkes L
AL
Price
*Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 bab@mit.edu.
Bonnie
B
Berger
D015986:Confounding Factors, Epidemiologic; D005828:Genetics, Population; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D009103:Multiple Sclerosis / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D025341:Principal Component Analysis
GWAS; mixed models; population stratification