A weighted U statistic for association analyses considering genetic heterogeneity.
A weighted U statistic for association analyses considering genetic heterogeneity.
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DOI：
10.1002/sim.6877
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发表时间：
2016-07-20
影响因子：
2
通讯作者：
Lu Q
中科院分区：
文献类型：
--
作者：
Wei C;Elston RC;Lu Q
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经转向揭示罕见的变异和结构变异易导致人类疾病，异质性在复杂疾病的遗传研究中的影响在很大程度上被忽视了。大多数现有的统计方法假设所调查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，其效力可能较低。本文提出了一种考虑遗传异质性的关联分析异质性加权U （HWU）方法。HWU可以应用于各种类型的表型（例如，二进制和连续），并且对高维遗传数据具有计算效率。通过模拟，我们展示了当一种疾病的潜在遗传病因是异质性时HWU的优势，以及HWU对不同模型假设（例如，表型分布）的鲁棒性。使用HWU，我们对来自成瘾研究：遗传与环境（SAGE）数据集的尼古丁依赖进行了全基因组分析。近100万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。
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 (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.