Equivalence of Kernel Machine Regression and Kernel Distance Covariance for Multidimensional Phenotype Association Studies
Equivalence of Kernel Machine Regression and Kernel Distance Covariance for Multidimensional Phenotype Association Studies
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DOI：
10.1111/biom.12314
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发表时间：
2015-09-01
期刊：
影响因子：
1.9
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
--
作者：
Hua, Wen-Yu;Ghosh, Debashis
将遗传标记与多维表型相关联是一个重要但具有挑战性的问题。在这项工作中，我们建立了两种流行方法之间的等效性：核机器回归（KMR）和核距离协方差（KDC）。 KMR 是一种半参数回归框架，可参数化地对协变量效应和非参数化的遗传标记进行建模，而 KDC 代表一类包括距离协方差 (DC) 和希尔伯特-施密特独立性准则 (HSIC) 的方法，它们是独立性的非参数检验。我们证明，在某些条件下，KMR 得分测试和 KDC 统计量之间的等价性可以导致包含协变量的 KDC 测试的新颖概括。我们的贡献有三方面：(1) 建立 KMR 和 KDC 之间的等价性； (2)表明KMR的原则可以应用于KDC的解释； (3)开发更广泛的KDC统计类，其中类成员是对应于不同内核组合的统计数据。最后，我们进行模拟研究并对阿尔茨海默病神经影像倡议 (ADNI) 研究的真实数据进行分析。 ADNI 研究表明，FLJ16124 的 SNP 表现出成对相互作用效应，与大脑区域体积的变化密切相关。
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression framework that models covariate effects parametrically and genetic markers non-parametrically, while KDC represents a class of methods that include distance covariance (DC) and Hilbert-Schmidt independence criterion (HSIC), which are nonparametric tests of independence. We show that the equivalence between the score test of KMR and the KDC statistic under certain conditions can lead to a novel generalization of the KDC test that incorporates covariates. Our contributions are 3-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of KMR can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, where the class members are statistics corresponding to different kernel combinations. Finally, we perform simulation studies and an analysis of real data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The ADNI study suggest that SNPs of FLJ16124 exhibit pairwise interaction effects that are strongly correlated to the changes of brain region volumes.