Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.
Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.
复制标题
用于检测基因与环境与罕见变异的相互作用的基于单倍型的测试的比较。
DOI：
10.1093/bib/bbz031
复制
发表时间：
2020
影响因子：
9.5
通讯作者：
Biswas,Swati
中科院分区：
文献类型：
--
作者：
Papachristou,Charalampos;Biswas,Swati
剖析复杂疾病背后的遗传机制取决于发现基因-环境相互作用（GXE）。然而，检测GXE是一个具有挑战性的问题，特别是当所研究的遗传变异是罕见的。基于单倍型的测试有几个优势，在最近的文献中强调，在检测罕见的变异所谓的崩溃测试。因此，比较用于检测GXE的基于单倍型的测试，包括最近专门针对罕见单倍型开发的测试，具有实际意义。我们比较了以下方法：haplo.glm，hapasmodal，HapReg，贝叶斯分层广义线性模型（BhGLM）和logistic贝叶斯LASSO（LBL）。我们模拟不同类型的关联场景和基因环境依赖水平下的数据。我们发现，当I型错误率被控制为所有方法相同时，LBL是检测GXE的最强大的方法。我们将这些方法应用于肺癌数据集，特别是在区域15q25.1中，因为文献中已经表明它与吸烟相互作用以影响肺癌易感性，并且它与吸烟行为相关。LBL和BhGLM能够检测到一种罕见的单倍型吸烟在这个地区的相互作用。我们还分析了来自达拉斯心脏研究（一项基于人群的多种族研究）的序列数据。具体来说，我们考虑了基因ANGPTL 4中的单倍型块与血清甘油三酯性状的关联，并使用种族作为协变量。只有LBL发现了单倍型与种族（西班牙裔）的相互作用。因此，在一般情况下，LBL似乎是最好的方法检测GXE之间，我们在这里研究。然而，它需要最多的计算时间。
Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene–environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature. Thus, it is of practical interest to compare haplotype-based tests for detecting GXE including the recent ones developed specifically for rare haplotypes. We compare the following methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL). We simulate data under different types of association scenarios and levels of gene–environment dependence. We find that when the type I error rates are controlled to be the same for all methods, LBL is the most powerful method for detecting GXE. We applied the methods to a lung cancer data set, in particular, in region 15q25.1 as it has been suggested in the literature that it interacts with smoking to affect the lung cancer susceptibility and that it is associated with smoking behavior. LBL and BhGLM were able to detect a rare haplotype–smoking interaction in this region. We also analyzed the sequence data from the Dallas Heart Study, a population-based multi-ethnic study. Specifically, we considered haplotype blocks in the gene ANGPTL4 for association with trait serum triglyceride and used ethnicity as a covariate. Only LBL found interactions of haplotypes with race (Hispanic). Thus, in general, LBL seems to be the best method for detecting GXE among the ones we studied here. Nonetheless, it requires the most computation time.