Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data.
Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data.
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DOI：
10.1002/sim.5535
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发表时间：
2013-02-10
影响因子：
2
通讯作者：
Wu, Rongling
中科院分区：
文献类型：
--
作者：
Das, Kiranmoy;Li, Jiahan;Fu, Guifang;Wang, Zhong;Li, Runze;Wu, Rongling
许多对生物学和生物医学至关重要的现象都是以动态曲线的形式出现的，例如器官生长和HIV动力学。这些性状的遗传作图受到在不规则和可能特定于受试者的时间点测量的纵向变量的挑战，在这种情况下，需要保证估计的协方差矩阵的非负确定性。我们提出了一种半参数方法，通过联合建模的平均值和协方差结构的不规则纵向数据的混合模型设置遗传作图。采用惩罚样条模型对QTL基因型的均值函数作为潜在变量进行建模，采用扩展广义线性模型对协方差矩阵进行近似。采用Gibbs采样器和Metropolis Hastings算法，采用MCMC方法估计了平均协方差的建模参数。我们推导了均值和协方差参数的完整条件分布，并计算贝叶斯因子来检验关于显著qtl存在的假设。该模型用于筛选具有稀疏纵向数据集的体重指数年龄特异性变化的特异性qtl的存在性。该模型为拓展遗传作图的应用，揭示动态性状的遗传控制提供了有力的手段。
Many phenomena of fundamental importance to biology and biomedicine arise as a dynamic curve, such as organ growth and HIV dynamics. The genetic mapping of these traits is challenged by longitudinal variables measured at irregular and possibly subject-specific time points, in which case nonnegative definiteness of the estimated covariance matrix needs to be guaranteed. We present a semiparametric approach for genetic mapping within the mixture-model setting by jointly modeling mean and covariance structures for irregular longitudinal data. Penalized spline is used to model the mean functions of individual QTL genotypes as latent variables while an extended generalized linear model is used to approximate the covariance matrix. The parameters for modeling the mean-covariances are estimated by MCMC, using Gibbs sampler and Metropolis Hastings algorithm. We derive the full conditional distributions for the mean and covariance parameters and compute Bayes factors to test the hypothesis about the existence of significant QTLs. The model was used to screen the existence of specific QTLs for age-specific change of body mass index with a sparse longitudinal dataset. The new model provides powerful means for broadening the application of genetic mapping to reveal the genetic control of dynamic traits.
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影响因子：
1.9
作者：
Daniels, MJ;Kass, RE
通讯作者：
Kass, RE
影响因子：
1.9
作者：
Chen, ZH
通讯作者：
Chen, ZH
影响因子：
1.9
作者：
Wu, RL;Ma, CX;Casella, G
通讯作者：
Casella, G
DOI：
10.1017/s0016672303006153
发表时间：
2003-04-01
期刊：
GENETICAL RESEARCH
影响因子：
--
作者：
Jannink, JL;Wu, XL
通讯作者：
Wu, XL
影响因子：
2.7
作者：
Daniels, MJ;Pourahmadi, M
通讯作者：
Pourahmadi, M