Variant-set association test for generalized linear mixed model.
Variant-set association test for generalized linear mixed model.
复制标题
广义线性混合模型的变集关联检验。
DOI：
10.1002/gepi.22378
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发表时间：
2021-06
影响因子：
2.1
通讯作者：
Chen J
中科院分区：
文献类型：
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作者：
Zhan X;Banerjee K;Chen J
高通量生物技术的进步导致了广泛的组学（如基因组学、表观基因组学、转录组学、代谢组学和宏基因组学）研究，这些研究中越来越多的证据表明，复杂性状的生物学结构涉及大量的组学变体，每个变体的影响较小，但共同解释了完整的表型变异。因此，在许多“ome-wide”关联分析中的主要挑战是实现足够的统计功效来识别小效应量的多个变体，这对于样本量相对较小的研究来说是非常困难的。提出了结合在核机器回归框架中的小样本调整，以解决在各种设置下的关联研究（Chen et al.，Genetic Epidemiology，40，5-19; Zhan等人，Genetic Epidemiology，41，210-220; Zhan等人，Genetic Epidemiology，42，772-782）。然而，这种调整的广义线性混合模型（GLMM）的框架，占样本相关性和非高斯的结果，尚未尝试。在这项工作中，我们填补了这一空白，扩展小样本调整内核机器关联测试的GLMM。我们提出了一个新的变量集关联测试（VSAT），一个强大而有效的分析工具，在GLMM，检查一组学变异和相关表型之间的关联。使用数值模拟研究和应用程序，从多个关联研究收集的数据表明VSAT的有用性。用于在R中实现所提出的方法的软件可在https://github.com/jchen1981/SSKAT获得。
Advances in high-throughput biotechnologies have culminated in a wide range of omics (such as genomics, epigenomics, transcriptomics, metabolomics and metagenomics) studies, and increasing evidence in these studies indicates that the biological architecture of complex traits involves a large number of omics variants each with minor effects but collectively accounting for the full phenotypic variability. Thus, a major challenge in many “ome-wide” association analyses is to achieve adequate statistical power to identify multiple variants of small effect sizes, which is notoriously difficult for studies with relatively small sample sizes. A small-sample adjustment incorporated in the kernel machine regression framework was proposed to solve this for association studies under various settings (Chen et al., Genetic Epidemiology, 40, 5–19; Zhan et al., Genetic Epidemiology, 41, 210–220; Zhan et al., Genetic Epidemiology, 42, 772–782). However, such an adjustment in the generalized linear mixed model (GLMM) framework, which accounts for both sample relatedness and non-Gaussian outcomes, has not yet been attempted. In this work, we fill this gap by extending small-sample adjustment in kernel machine association test to GLMM. We propose a new Variant-Set Association Test (VSAT), a powerful and efficient analysis tool in GLMM, to examine the association between a set of omics variants and correlated phenotypes. The usefulness of VSAT is demonstrated using both numerical simulation studies and applications to data collected from multiple association studies. The software for implementing the proposed method in R is available at https://github.com/jchen1981/SSKAT.
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影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
影响因子：
9.8
作者：
Chen, Han;Huffman, Jennifer E.;Lin, Xihong
通讯作者：
Lin, Xihong
影响因子：
3.3
作者：
Pan, Wei;Kim, Junghi;Wei, Peng
通讯作者：
Wei, Peng
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
DOI：
10.1038/nrg3000
发表时间：
2011-07-12
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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