Asymmetric independence modeling identifies novel gene-environment interactions.
Asymmetric independence modeling identifies novel gene-environment interactions.
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不对称独立模型识别新的基因-环境相互作用。
DOI：
10.1038/s41598-019-38983-z
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发表时间：
2019
影响因子：
4.6
通讯作者：
Wang,Yue
中科院分区：
文献类型：
--
作者：
Yu,Guoqiang;Miller,DavidJ;Wu,Chiung-Ting;Hoffman,EricP;Liu,Chunyu;Herrington,DavidM;Wang,Yue
大多数遗传或环境因素共同决定复杂的疾病风险。检测基因与环境的相互作用可能使我们能够阐明环境暴露如何改变遗传效应的新颖和有针对性的分子机制。不幸的是，标准Logistic回归(LR)为零假设提供了一种方便的数学结构，但这会导致较差的检测能力和第一类错误，而且还容易受到缺失因素、不完全替代和疾病异质性混杂的影响。在这里，我们描述了病例对照研究中的一个新的基线框架--非对称独立性模型(AIM)，并提供了数学证明和模拟研究来验证其在各种条件下的有效性。我们表明，与LR不同，AIM在数学上保留了维持健康与获得疾病的不对称性质，因此在检测协同作用方面更强大和更稳健。我们提供了四个临床离散领域的例子，在这些领域中，AIM识别出以前不一致或较不确定的交互作用。
Most genetic or environmental factors work together in determining complex disease risk. Detecting gene-environment interactions may allow us to elucidate novel and targetable molecular mechanisms on how environmental exposures modify genetic effects. Unfortunately, standard logistic regression (LR) assumes a convenient mathematical structure for the null hypothesis that however results in both poor detection power and type 1 error, and is also susceptible to missing factor, imperfect surrogate, and disease heterogeneity confounding effects. Here we describe a new baseline framework, the asymmetric independence model (AIM) in case-control studies, and provide mathematical proofs and simulation studies verifying its validity across a wide range of conditions. We show that AIM mathematically preserves the asymmetric nature of maintaining health versus acquiring a disease, unlike LR, and thus is more powerful and robust to detect synergistic interactions. We present examples from four clinically discrete domains where AIM identified interactions that were previously either inconsistent or recognized with less statistical certainty.