Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, United States of America.
National Cancer Institute, National Institutes of Health, Bethesda, MD, United States of America.
PLoS One. 2018 Aug 22;13(8):e0201140. doi: 10.1371/journal.pone.0201140. eCollection 2018.
Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer's disease.
病例对照全基因组关联研究（GWAS）为研究复杂疾病的遗传结构提供了丰富的资源。关键是要阐明遗传效应如何随环境而变化，传统上这被定义为基因-环境相互作用（GxE）。被忽视的复杂情况是，多种不同的病理生理机制可能导致相同的临床诊断，而这些机制通常具有不同的遗传基础。在本文中，我们首先表明，在感兴趣的病理诊断的频率相对于其他诊断取决于环境的情况下，使用临床诊断状态可能会导致 GxE 相互作用的严重偏差估计。然后，我们提出了一种拟似然解决方案来纠正偏差。最后，我们在广泛的模拟和阿尔茨海默病的 GWAS 中展示了我们的方法。
