| In studies of complex disorders, researchers assess multiple variables related to theprimary disorder of interest. We refer to those variables as multiple traits. The multiple traitsmay or may not have a common causal genetic variant. Although the analysis of multipletraits is generally more complicated than the analysis of a single trait, it is more powerful.However all current methods do not consider structure information among multiple traits. Inthis paper, we conduct simulation studies to explore the performance of considering thestructure information among multiple traits in genetic association analysis. The advantage ofconsidering the structure information is maximized when all of the traits are influenced by thegene. |
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