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Bayesian Inference for Mixed Model-Based Genome-Wide Analysis of Expression Quantitative Trait Loci by Gibbs Sampling

机译：贝叶斯推理的基于吉布斯采样的混合模型基于基因组范围的表达定量性状基因座分析

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摘要

The importance of expression quantitative trait locus (eQTL) has been emphasized in understanding the genetic basis of cellular activities and complex phenotypes. Mixed models can be employed to effectively identify eQTLs by explaining polygenic effects. In these mixed models, the polygenic effects are considered as random variables, and their variability is explained by the polygenic variance component. The polygenic and residual variance components are first estimated, and then eQTL effects are estimated depending on the variance component estimates within the frequentist mixed model framework. The Bayesian approach to the mixed model-based genome-wide eQTL analysis can also be applied to estimate the parameters that exhibit various benefits. Bayesian inferences on unknown parameters are based on their marginal posterior distributions, and the marginalization of the joint posterior distribution is a challenging task. This problem can be solved by employing a numerical algorithm of integrals called Gibbs sampling as a Markov chain Monte Carlo. This article reviews the mixed model-based Bayesian eQTL analysis by Gibbs sampling. Theoretical and practical issues of Bayesian inference are discussed using a concise description of Bayesian modeling and the corresponding Gibbs sampling. The strengths of Bayesian inference are also discussed. Posterior probability distribution in the Bayesian inference reflects uncertainty in unknown parameters. This factor is useful in the context of eQTL analysis where a sample size is too small to apply the frequentist approach. Bayesian inference based on the posterior that reflects prior knowledge, will be increasingly preferred with the accumulation of eQTL data. Extensive use of the mixed model-based Bayesian eQTL analysis will accelerate understanding of eQTLs exhibiting various regulatory functions.
机译：在理解细胞活动和复杂表型的遗传基础时，已强调了表达数量性状基因座（eQTL）的重要性。通过解释多基因效应，可以采用混合模型来有效识别eQTL。在这些混合模型中，多基因效应被视为随机变量，其可变性由多基因方差分量解释。首先估计多基因和残余方差分量，然后根据频繁混合模型框架内的方差分量估计值来估计eQTL效果。基于混合模型的全基因组全eQTL分析的贝叶斯方法也可以用于估计具有各种优势的参数。贝叶斯对未知参数的推论基于其边缘后验分布，联合后验分布的边缘化是一项艰巨的任务。这个问题可以通过采用称为Gibbs采样的积分数值算法作为马尔可夫链蒙特卡洛来解决。本文通过Gibbs采样回顾了基于混合模型的贝叶斯eQTL分析。使用贝叶斯建模的简要描述和相应的吉布斯采样，讨论了贝叶斯推理的理论和实践问题。还讨论了贝叶斯推理的优势。贝叶斯推断中的后验概率分布反映了未知参数的不确定性。在eQTL分析的情况下，此因素很有用，因为eQTL分析的样本量太小而无法应用频繁方法。随着eQTL数据的积累，基于反映先验知识的后验的贝叶斯推理将越来越受到青睐。广泛使用基于混合模型的贝叶斯eQTL分析将加速对具有各种监管功能的eQTL的理解。

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