  Converging evidence suggests that common complex diseases with the same or
similar clinical manifestations could have different underlying genetic
etiologies. While current research interests have shifted toward uncovering
rare variants and structural variations predisposing to human diseases, the
impact of heterogeneity in genetic studies of complex diseases has been largely
overlooked. Most of the existing statistical methods assume the disease under
investigation has a homogeneous genetic effect and could, therefore, have low
power if the disease undergoes heterogeneous pathophysiological and etiological
processes. In this paper, we propose a heterogeneity weighted U (HWU) method
for association analyses considering genetic heterogeneity. HWU can be applied
to various types of phenotypes (e.g., binary and continuous) and is
computationally effcient for high- dimensional genetic data. Through
simulations, we showed the advantage of HWU when the underlying genetic
etiology of a disease was heterogeneous, as well as the robustness of HWU
against different model assumptions (e.g., phenotype distributions). Using HWU,
we conducted a genome-wide analysis of nicotine dependence from the Study of
Addiction: Genetics and Environments (SAGE) dataset. The genome-wide analysis
of nearly one million genetic markers took 7 hours, identifying heterogeneous
effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence.
