Multivariate phenotype association analysis by marker-set kernel machine regression.
Multivariate phenotype association analysis by marker-set kernel machine regression.
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DOI：
10.1002/gepi.21663
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发表时间：
2012-11
影响因子：
2.1
通讯作者：
Tzeng, Jung-Ying
中科院分区：
文献类型：
--
作者：
Maity, Arnab;Sullivan, Patrick E.;Tzeng, Jung-Ying
复杂疾病的遗传学研究经常收集与疾病相关的多种表型。由于这些表型可以相互关联并共享共同的遗传机制，因此联合分析这些性状可能会带来更多的力量来检测影响个体或多个表型的基因。鉴于多元表型方法和多标记核机器回归方法的发展，我们构建了一个基于核机器的多元回归模型，以便于多标记对多表型的联合评价。核机器作为一个强大的降维工具，捕捉标记之间的复杂影响。多变量框架结合了潜在相关的多维表型信息，并适应每个性状的共同或不同的环境协变量。我们推导了基于类得分统计量的多变量核机器测试，并进行了模拟，以评估该方法的有效性和有效性。我们还研究了多表型的核机器分析的常用适应策略的性能，包括多个单变量核机器测试与原始表型或其主成分。我们的研究结果表明，这些方法都没有统一的最佳功率，最佳的测试取决于表型相关性和效果模式的大小。然而，当多个表型没有相关性或具有轻度相关性时，多变量检验仍然是一种合理的方法，并且一旦相关性变得更强或当存在影响多个表型的基因时，多变量检验给出最佳功效。我们通过CATIE抗体研究说明了多变量核机器方法的实用性。
Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multi-dimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the CATIE antibody study.
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影响因子：
1.9
作者：
Maity A;Lin X
通讯作者：
Lin X
影响因子：
64.8
作者：
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通讯作者：
Collier, David A.
影响因子：
9.8
作者：
Wu, Michael C.;Kraft, Peter;Lin, Xihong
通讯作者：
Lin, Xihong
影响因子：
2.1
作者：
Klei, Lambertus;Luca, Diana;Roeder, Kathryn
通讯作者：
Roeder, Kathryn
影响因子：
11
作者：
Sullivan, P. F.;Lin, D.;Close, S. L.
通讯作者：
Close, S. L.