Multiple correlated traits are often collected in genetic studies. The joint analysis of multiple traits could have increased power by aggregating multiple weak effects and offer additional insights into the aetiology of complex human diseases by revealing pleiotropic variants. We propose to study multivariate test statistics to detect single nucleotide polymorphism (SNP) association with multiple correlated traits. Most existing methods have been based on the generalized estimating equation (GEE) approach without explicitly modelling the trait correlations. In this article, we explore an alternative likelihood-based framework to test the multiple trait associations. It is based on the familiar multinomial logistic regression modelling of genotypes, which can be readily implemented using widely available software, and offers very competitive performance. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study.
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