Sequencing-based studies are emerging as a major tool for genetic association studies of complex diseases. These studies pose great challenges to the traditional statistical methods because of the high-dimensionality of data and the low frequency of genetic variants. Moreover, there is a great interest in biology and epidemiology to identify genetic risk factors contributed to multiple disease phenotypes. The multiple phenotypes can often follow different distributions, which bring an additional challenge to the current statistical framework. In this talk, I will introduce a generalized similarity U test, referred to as GSU. GSU is a similarity-based test that can handle high-dimensional genotypes and phenotypes. We studied the properties of GSU, and provided the efficient p-value calculation for association test. Through simulation, we found that GSU had advantages over existing methods in terms of power and robustness to phenotype distributions.