Wei Changshuai, Elston Robert C, Lu Qing
Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, U.S.A.
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, U.S.A.
Stat Med. 2016 Jul 20;35(16):2802-14. doi: 10.1002/sim.6877. Epub 2016 Feb 1.
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 efficient 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 dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd.
越来越多的证据表明，具有相同或相似临床表现的常见复杂疾病可能具有不同的潜在遗传病因。虽然目前的研究兴趣已转向揭示导致人类疾病的罕见变异和结构变异，但复杂疾病遗传研究中的异质性影响在很大程度上被忽视了。现有的大多数统计方法都假定所研究的疾病具有同质的遗传效应，因此，如果疾病经历异质的病理生理和病因过程，这些方法的效能可能较低。在本文中，我们提出了一种考虑遗传异质性的异质性加权U（HWU）关联分析方法。HWU可应用于各种类型的表型（如二元和连续型），并且对于高维遗传数据计算效率高。通过模拟，我们展示了HWU在疾病潜在遗传病因异质时的优势，以及HWU对不同模型假设（如表型分布）的稳健性。使用HWU，我们对成瘾：遗传学与环境研究数据集进行了尼古丁依赖的全基因组分析。对近一百万个遗传标记的全基因组分析耗时7小时，确定了两个新基因（即CYP3A5和IKBKB）对尼古丁依赖的异质效应。版权所有©2016约翰威立父子有限公司。
