Abstract
IntroductionLarge-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci.MethodsWe apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. We also compare this multivariate test with a widely used univariate test that analyzes phenotypes separately.ResultsThe multivariate test identified 2 genetic variants that have been previously reported as associated with hypertension or coronary artery disease. In addition, our region-based analyses also show that the multivariate test tends to give smaller p values than the univariate test.ConclusionsHence, the multivariate test has potential to improve test power, especially when multiple phenotypes are correlated.
Highlights
Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants
In the Genetic Analysis Workshop 19 (GAW19) data [1], both systolic and diastolic blood pressures are available for each subject
Phenotypic and sequencing data In this study, we focused on the 1943 unrelated Mexican American samples provided by GAW19, and considered 2 phenotypes, systolic blood pressure (SBP) and diastolic blood pressure (DBP)
Summary
Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect diseaseassociated loci. It is possible that these related traits share some common genetic architecture either through pleiotropy—one genetic variant influencing multiple traits [2, 3]—or by contributions of different variants in the same gene [4]. Under such situations, multivariate methods may help in genetic association studies, as they may add ability to investigate the genetic architecture, and increase power to detect disease-associated loci [5]. Regionbased tests have become a standard alternative approach to summarize the genetic variability of a set of rare variants in a defined region [7,8,9]
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