Genome-wise complex trait analysis (GCTA) was developed and applied to heritability analyses on complex traits and more recently extended to mental disorders. However, besides the intensive computation, previous literature also limits the scope to univariate phenotype, which ignores mutually informative but partially independent pieces of information provided in other phenotypes. Our goal is to use such auxiliary information to improve power. We show that the proposed method leads to a large power increase, while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method over several state-of-the-art methods. We illustration our methods on dataset from a schizophrenia study.