Longitudinal association analysis of quantitative traits.
Longitudinal association analysis of quantitative traits.
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DOI：
10.1002/gepi.21673
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发表时间：
2012-12
影响因子：
2.1
通讯作者：
Xiong, Momiao
中科院分区：
文献类型：
--
作者：
Fan, Ruzong;Zhang, Yiwei;Albert, Paul S.;Liu, Aiyi;Wang, Yuanjia;Xiong, Momiao
纵向遗传学研究为探索影响复杂性状的关键遗传和环境因素提供了宝贵的资源。结合时间变化的纵向数据的遗传分析对于理解常见复杂疾病的遗传结构和生物变异是重要的。虽然它们很重要，但缺乏分析纵向人类遗传数据的统计方法。本文提出了一种时间关联映射的纵向方法来分析人口纵向数据。提出了参数模型和非参数模型。该模型可应用于多个双等位基因遗传标记，如单核苷酸多态性和多等位基因标记，如微卫星。通过解析公式表明，模型同时考虑了联动不均衡和时间趋势。基于随机过程理论，构建方差协方差结构，对个体的单次测量变异和多次测量相关进行建模。采用新的惩罚样条模型估计随时间变化的均值函数和回归系数。方法应用于分析遗传分析研讨会（GAW） 13和GAW 16的Framingham心脏研究数据。所提出的方法成功地检测了收缩压的时间趋势和遗传效应。仿真研究表明，非参数惩罚线性模型是拟合实际数据的最佳选择。该研究揭示了纵向遗传分析的重要领域，为今后的方法研究和实际应用奠定了基础。
Longitudinal genetic studies provide a valuable resource for exploring key genetic and environmental factors that affect complex traits over time. Genetic analysis of longitudinal data that incorporate temporal variations is important for understanding genetic architecture and biological variations of common complex diseases. Although they are important, there is a paucity of statistical methods to analyze longitudinal human genetic data. In this article, longitudinal methods are developed for temporal association mapping to analyze population longitudinal data. Both parametric and nonparametric models are proposed. The models can be applied to multiple diallelic genetic markers such as single-nucleotide polymorphisms and multiallelic markers such as microsatellites. By analytical formulae, we show that the models take both the linkage disequilibrium and temporal trends into account simultaneously. Variance-covariance structure is constructed to model the single measurement variation and multiple measurement correlations of an individual based on the theory of stochastic processes. Novel penalized spline models are used to estimate the time-dependent mean functions and regression coefficients. The methods were applied to analyze Framingham Heart Study data of Genetic Analysis Workshop (GAW) 13 and GAW 16. The temporal trends and genetic effects of the systolic blood pressure are successfully detected by the proposed approaches. Simulation studies were performed to find out that the nonparametric penalized linear model is the best choice in fitting real data. The research sheds light on the important area of longitudinal genetic analysis, and it provides a basis for future methodological investigations and practical applications.
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影响因子：
2.9
作者：
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通讯作者：
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