The method of generalized estimating equations (GEE) [16] was developed for analyzing correlated multivariate outcomes primarily from longitudinal studies. It can be applied to test the association between a candidate SNP and multivariate phenotypes. The GEE only requires specification of the link function and the working correlation matrix. The former depends on the measurement scale of the outcomes (e.g. the identify link for continuous outcomes). The latter assumes the correlation structure among multivariate outcomes. The estimation of GEE is usually robust against this assumption. GEE was widely used in GWAS. For example, GEE was proposed as one of the multivariate approaches in Solovieff et al. [4]. For another example, Liu et al. [17] proposed to use GEE for bivariate association analyses for the mixture of continuous and binary traits. However, to the best of our knowledge, none of the existing studies have conducted simulations to investigate whether GEE can control the type I error rate when multivariate traits are involved in GWAS. To fill in this knowledge gap, we conduct a simulation study to examine the statistical properties of this
