A General Framework for Association Tests with Multivariate Traits in Large-Scale Genomics Studies

• Activity Number: 127
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Chad He*+ and Christy L. Avery and Danyu Lin
• Companies: Fred Hutchinson Cancer Research Center and The University of North Carolina at Chapel Hill and Univ of North Carolina
• Keywords: Binary traits ; Genomic studies ; Meta-analysis ; Multivariate tests ; Pleiotropy ; Quantitative traits

Abstract

Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants which have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows the mixture of binary and continuous traits. Our statistics can be combined efficiently across multiple studies with different designs and arbitrary patterns of missing data. We compare the power of the test statistics both analytically and empirically. We also provide a strategy to determine genomewide significance that properly accounts for the linkage disequilibrium (LD) of genetic variants. We illustrate the usefulness of the new methods with applications to real genomic studies.