A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants
A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants
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DOI：
10.1016/j.ajhg.2016.01.017
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发表时间：
2016-03-03
影响因子：
9.8
通讯作者：
Epstein, Michael P.
中科院分区：
文献类型：
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作者：
Broadaway, K. Alaine;Cutler, David J.;Epstein, Michael P.
越来越多的经验证据表明，许多遗传变异影响多种不同的表型。当存在交叉表型效应时，考虑多效性的多变量关联方法通常比单独模拟每种表型的单变量方法更有效。虽然有几种统计方法可以检测常见变异的交叉表型效应，但对于罕见变异的基于基因的分析，缺乏类似的测试。为了填补这一重要的空白，我们引入了一种统计方法，使用非参数距离协方差方法对罕见变异进行交叉表型分析，该方法将多变量表型的相似性与跨基因的罕见变异基因型的相似性进行比较。该方法可以适应二元和连续表型，并进一步调整协变量。我们的方法产生了一种封闭形式的测试，其重要性可以分析评估，从而提高了计算效率，并允许在全基因组范围内应用。我们使用模拟数据来证明我们的方法，我们称之为基因与多性状关联（GAMuT）测试，提供了比竞争方法更强大的力量。我们还使用来自动脉病变遗传流行病学网络的外显子组芯片数据说明了我们的方法。
Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.