In recent years, improved genotyping and sequencing technologies have enabled the discovery of new loci associated with various diseases or traits. For instance, by testing the association with each single-nucleotide variant (SNV) separately, genomewide association studies (GWAS) have achieved tremendous success in identifying SNVs associated with specific traits. However, little is known about the common genetic basis of multiple traits owing to lack of efficient methods. With the use of extended quasi-likelihood, a Wald test has been proposed to perform a bivariate analysis of a continuous and a binary trait in unrelated samples. However, owing to its low computational efficiency, it has not been implemented in real applications to large-scale genetic studies. In this paper, we propose an efficient bivariate robust score test for two traits, one continuous and one binary, based on extended generalized estimating equations. Our approach is applicable to both family-based and unrelated study designs and can be extended to test the association of multiple traits. Our simulation studies demonstrate the type-I error rate of our approach is well controlled in all minor allele frequency (MAF) scenarios, with MAF ranging from 1 to 30%, and the method is more powerful in certain MAF scenarios than univariate testing with correction for multiple testing. Because of the computational advantage of score tests, our approach is readily applicable to GWAS or sequencing studies. Finally, we present a real application to uncover genetic variants associated with body mass index and type-2 diabetes in the Framingham Heart Study. European Journal of Human Genetics (2017) 25, 130-136; doi:10.1038/ejhg.2016; published online 26 October 2016
INTRODUCTIONIn recent years, univariate association test has been implemented as the predominant statistical method in genetic epidemiology and has yielded fruitful results in many applications. For example, univariate association tests have led to tremendous success in the discovery of disease susceptibility loci when applied to genome-wide association studies (GWAS) for various diseases. However, for the genetic association testing of multiple and often correlated traits, univariate association testing combined with multiple testing correction has usually been implemented owing to the ease of computation. Other variations include MultiPhen 1 and Yang's combination of univariate association tests. 2 However, none of these approaches are as powerful or efficient as a joint multivariate test with each trait treated as a dependent variable in discovering genetic loci associated with all traits under study. 1,3,4 For example, in the case of two continuous traits assumed to be normally distributed, a joint test can be derived as a simple extension of a univariate normal test. However, if one of the two traits is a discrete trait, for example, a binary trait, deriving such a test becomes challenging, and it further complicates in family samples. One reason is that there is no exact closed form of the...