Discussion: Why do we test multiple traits in genetic association studies

Zhu and Zhang [Zhu, W., &; Zhang, H. (2009). Why do we test multiple traits in genetic association studies. Journal of the Korean Statistical Society, 38(1), 1–10] publish a paper “Why Do We Test Multiple Traits in Genetic Association Studies?” in this issue. The authors used linear structural equations and acyclic graph as tools to explore the performance of testing multiple traits simultaneously by large-scale simulations for various genetic models. The methods, conclusions and results are of great interest in quantitative genetics. Diseases are caused by dynamic interaction among many genes and many environmental exposures through regulation and metabolism. In the past several decades, researchers have primarily focused on (1) the role of individual genetic variation in determining the diseases and (2) one single trait at a time. Little attention has been paid to determining how the genetic variations and environmental perturbation are integrated into networks which act together to dynamically alter regulations and metabolism leading to the emergence of complex phenotypes and diseases. Pending conceptual and statistical challenges are (1) how to identify networks involved in molecular phenotypes and endpoint clinical phenotypes under perturbation of environments and (2) how to connect DNA variation to disease outcomes through gene regulations and cellular intermediate traits. Structural equations and graphical models of multiple quantitative traits provide a general framework for developing novel analytic strategies for identifying the path from genomic information coupled with the environmental exposures, through gene expressions and other intermediate traits, to the clinical endpoints of complex diseases, to meet the above conceptual and statistical challenges. In this discussion, we use structural equations to analyze multiple intermediate traits of ankylosing spondylitis (AS) as a real example to further demonstrate the importance of network approach to genetic studies of complex traits.