CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates.
CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates.
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DOI：
10.3390/genes12111723
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发表时间：
2021-10-28
期刊：
影响因子：
3.5
通讯作者：
Zang Y
中科院分区：
文献类型：
--
作者：
Chen Z;Zang Y
在逻辑回归中实现的加性遗传模型已被广泛用于全基因组关联研究（GWAS）的二元结果。不幸的是，对于许多复杂的疾病，潜在的遗传模型通常是未知的，并且遗传模型的错误指定可能导致功率的实质性损失。为了解决这个问题，MAX3检验（三个独立的检验统计量的最大值）被提出作为一种稳健的检验，无论潜在的遗传模型如何，它都能可靠地执行。然而，MAX3的原始实现使用趋势检验，因此无法调整任何协变量，如年龄和性别。这一缺点极大地限制了MAX3在GWAS中的应用，因为协变量导致了这些疾病中相当大的变异性。本文对MAX3进行了扩展，提出了基于Logistic回归的协变量调整MAX3（covariateadjustedMAX3）。所提出的测试产生了与原始MAX3相似的稳健效率，同时基于似然框架轻松调整任何协变量。本文还开发了计算所提出的检验的p值的渐近公式。仿真结果表明，提出的测试进行理想的零假设和备择假设。出于说明的目的，我们应用提出的测试重新分析了酒精中毒遗传学合作研究（COGA）的病例对照GWAS数据集。本文还介绍了实现所提出的测试的R代码，可供免费下载。
The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the p-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download.
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影响因子：
5.3
作者：
Chen, Zhongxue;Wang, Kai
通讯作者：
Wang, Kai
影响因子：
0.8
作者：
Chen, Zhongxue
通讯作者：
Chen, Zhongxue
DOI：
10.1111/j.1530-0277.2010.01156.x
发表时间：
2010-05
期刊：
Alcoholism, clinical and experimental research
影响因子：
--
作者：
Edenberg HJ;Koller DL;Xuei X;Wetherill L;McClintick JN;Almasy L;Bierut LJ;Bucholz KK;Goate A;Aliev F;Dick D;Hesselbrock V;Hinrichs A;Kramer J;Kuperman S;Nurnberger JI Jr;Rice JP;Schuckit MA;Taylor R;Todd Webb B;Tischfield JA;Porjesz B;Foroud T
通讯作者：
Foroud T
DOI：
10.1515/sagmb-2016-0061
发表时间：
2017-08-01
影响因子：
0.9
作者：
Chen, Zhongxue;Han, Shizhong;Wang, Kai
通讯作者：
Wang, Kai
影响因子：
2.3
作者：
Chen, Zhongxue;Huang, Hanwen;Ng, Hon Keung Tony
通讯作者：
Ng, Hon Keung Tony