  In genome-wide association studies (GWASs), there is an increasing need for
detecting the associations between a genetic variant and multiple traits. In
studies of complex diseases, it is common to measure several potentially
correlated traits in a single GWAS. Despite the multivariate nature of the
studies, single-trait-based methods remain the most widely-adopted analysis
procedure, owing to their simplicity for studies with multiple traits as their
outcome. However, the association between a genetic variant and a single trait
sometimes can be weak, and ignoring the actual correlation among traits may
lose power. On the contrary, multiple-trait analysis, a method analyzes a group
of traits simultaneously, has been proven to be more powerful by incorporating
information from the correlated traits. Although existing methods have been
developed for multiple traits, several drawbacks limit their wide application
in GWASs. In this paper, we propose a multiple-trait adaptive Fisher's (MTAF)
method to test associations between a genetic variant and multiple traits at
once, by adaptively aggregating evidence from each trait. The proposed method
can accommodate both continuous and binary traits and it has reliable
performance under various scenarios. Using a simulation study, we compared our
proposed method with several existing methods and demonstrated its
competitiveness in terms of type I error control and statistical power. By
applying the method to the Study of Addiction: Genetics and Environment (SAGE)
dataset, we successfully identified several genes associated with substance
dependence.
