Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.
Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits.
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DOI：
10.1002/gepi.21901
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发表时间：
2015-07
影响因子：
2.1
通讯作者：
Epstein, Michael P.
中科院分区：
文献类型：
--
作者：
Broadaway, K. Alaine;Duncan, Richard;Conneely, Karen N.;Almli, Lynn M.;Bradley, Bekh;Ressler, Kerry J.;Epstein, Michael P.
复杂性状的病因学可能涉及遗传和环境因素的影响，沿着它们之间复杂的交互作用。因此，人们一直有兴趣应用复杂性状的遗传关联测试，解释在环境因素存在下遗传效应的潜在改变。人们可以使用基因和基因-环境相互作用的联合测试来进行这样的分析。一个最佳的联合测试将是一个保持强大的各种模型下，从那些强大的基因-环境相互作用的影响，很少或没有基因-环境相互作用的影响。为了满足这一需求，我们扩展了基于核机器的方法，考虑基因和基因-环境相互作用的联合测试的多个SNP的关联映射。基于核的方法联合测试是有前途的，因为它结合了连锁不平衡信息，同时从多个SNP的分析，并允许灵活的相互作用的影响建模。使用模拟数据，我们表明，我们的核机器方法通常优于传统的联合测试下强基因-环境相互作用模型，并进一步优于传统的主效应关联测试下弱或无基因-环境相互作用的影响。我们使用来自Grady创伤项目的全基因组关联数据来说明我们的测试，Grady创伤项目是一组高度创伤的高危个体，以前曾研究过相互作用效应。
The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel-machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, since it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel-machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.
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影响因子：
1.9
作者：
Bůžková P;Lumley T;Rice K
通讯作者：
Rice K
影响因子：
2.8
作者：
Barrero, FJ;Ampuero, I;Yébenes, JG
通讯作者：
Yébenes, JG
影响因子：
120.7
作者：
Binder, Elisabeth B.;Bradley, Rebekah G.;Ressler, Kerry J.
通讯作者：
Ressler, Kerry J.
影响因子：
56.9
作者：
Caspi, A;McClay, J;Poulton, R
通讯作者：
Poulton, R
影响因子：
3.4
作者：
Beck, AT;Steer, RA;Ranieri, WF
通讯作者：
Ranieri, WF