Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.
Multivariate dimensionality reduction approaches to identify gene-gene and gene-environment interactions underlying multiple complex traits.
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多变量降维方法来识别多种复杂性状背后的基因-基因和基因-环境相互作用
DOI：
10.1371/journal.pone.0108103
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lou XY
中科院分区：
文献类型：
--
作者：
Xu HM;Sun XW;Qi T;Lin WY;Liu N;Lou XY
难以捉摸，但无处不在的多因素相互作用代表了一个绊脚石，迫切需要在寻找参与人类复杂疾病的决定因素被删除。降维方法是一个很有前途的工具，这项任务。许多复杂疾病表现出需要在一组具有不同相关性的临床特征中进行测量的复合综合征和/或本质上是纵向的（随着时间的推移而变化并在多个时间点动态测量）。因此，非常需要用于检测相互作用的多变量方法，其目的是处理多方面的表型和纵向数据，以及通过两阶段测试程序提高多个显著性测试的统计功效，所述两阶段测试程序涉及对分组表型的多变量分析，然后对显著组中的表型进行单变量分析。本文基于多元广义线性模型、多元拟似然模型和广义估计方程模型，提出了广义多因子降维（GMDR）的多元扩展。通过对成瘾研究：遗传学与环境的队列数据的模拟和真实的数据分析，与单变量方法进行了比较，研究了所提方法的性质和性能。结果表明，提出的多元GMDR大大提高了统计能力。
The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
4.5
作者：
Evans, David M.;Marchini, Jonathan;Morris, Andrew P.;Cardon, Lon R.
通讯作者：
Cardon, Lon R.
影响因子：
2.6
作者：
Evans, DM;Duffy, DL
通讯作者：
Duffy, DL
影响因子：
10.6
作者：
Li, Ming D.;Lou, Xiang-Yang;Elston, Robert C.
通讯作者：
Elston, Robert C.
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH