title: Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.
abstract: Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.
authors.affiliation: Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. Electronic address: haschard@hsph.harvard.edu.
authors.forename: Hugues
authors.initials: H
authors.lastname: Aschard
authors.affiliation: Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA; Medical and Population Genetics Program, Broad Institute, Cambridge, MA 02142, USA.
authors.forename: Bjarni J
authors.initials: BJ
authors.lastname: Vilhjálmsson
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1166, 75005 Paris, France; INSERM, UMR_S 1166, Genomics and Physiopathology of Cardiovascular Diseases, 75013 Paris, France; Institute for Cardiometabolism and Nutrition (ICAN), 75013 Paris, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Greliche
authors.affiliation: Aix-Marseille Université, INSERM UMR_S 1062, 13385 Marseille, France.
authors.forename: Pierre-Emmanuel
authors.initials: PE
authors.lastname: Morange
authors.affiliation: Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1166, 75005 Paris, France; INSERM, UMR_S 1166, Genomics and Physiopathology of Cardiovascular Diseases, 75013 Paris, France; Institute for Cardiometabolism and Nutrition (ICAN), 75013 Paris, France.
authors.forename: David-Alexandre
authors.initials: DA
authors.lastname: Trégouët
authors.affiliation: Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Kraft
mesh_terms: D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis / Q000379:methods*; D019655:Quantitative Trait, Heritable; D020246:Venous Thrombosis / Q000235:genetics
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