Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here we propose a new fast adaptive Markov Chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared to the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the...针对数量性状背后的遗传参数，采用带加性与显性效应的线性混合模型进行精准且高效的估计，在自然种群与育种种群中均具备重要的研究与应用价值。为此，我们提出一种全新的快速自适应马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）采样算法，用于估计含多个随机效应的线性混合模型中的遗传参数。在算法的学习阶段，我们借助混合吉布斯采样器（hybrid Gibbs sampler）学习方差组分的协方差结构。在算法的第二阶段，我们利用该协方差结构为梅特罗波利斯-黑斯廷斯（Metropolis-Hastings）算法构建高效的提议分布，该算法采用了已将随机效应积分掉的似然函数。相较于混合吉布斯采样器，新算法展现出更优异的混合特性，且运行速度约提升一倍。我们提出的新算法能够检测后验分布中的不同模态，此外，……