A weighted U statistic for association analysis considering genetic heterogeneity

TL;DR

This paper introduces a heterogeneity weighted U (HWU) statistical method for association analysis that effectively detects genetic effects in complex diseases with heterogeneous etiologies, outperforming traditional methods especially in diverse genetic scenarios.

Contribution

The paper presents HWU, a novel, computationally efficient method for association analysis that accounts for genetic heterogeneity across various phenotype types.

Findings

HWU outperforms traditional methods in heterogeneous genetic scenarios.

HWU successfully identified new genes associated with nicotine dependence.

The genome-wide analysis identified CYP3A5 and IKBKB as novel genes related to nicotine dependence.

Abstract

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.