Quantitative Issues in Cancer Research Working Seminar

Andy Shi
Doctoral Student, Department of Biostatistics, Harvard University

“Multiple Phenotype—Multiple Genotype Testing with Principal Components”

ABSTRACT: The increasing popularity of large-scale genetic compendiums has driven a recent interest in (1) testing sets of genotypes against a single phenotype and (2) testing sets of phenotypes against a single genotype. Incorporating the information from these correlated sets of variants and outcomes can offer more power to detect novel associations, reduce the multiple testing burden in such massive datasets, and produce more interpretable conclusions about the genetic etiology of complex diseases by incorporating prior biological knowledge into set definitions. However, less work has focused on the testing problem when sets are formed for both genotypes and phenotypes. Thus, in this paper we propose and study the performance of principal components-based methods that can jointly test for the association between multiple phenotypes and multiple genotypes. We demonstrate analytically how the operating characteristics of each test are determined by the correlation structures of the genotypes and phenotypes as well as the direction of effects linking the two sets. We also propose an omnibus test that is robust to the direction of the effects. Simulations demonstrate that our method controls Type-I error in practical sample sizes, and we apply our method to analyze correlated biomarkers in a study of cardiovascular outcomes.