Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis

文献类型： 外文期刊

第一作者： Guo, Peng

作者： Guo, Peng;Zhu, Bo;Niu, Hong;Wang, Zezhao;Liang, Yonghu;Chen, Yan;Zhang, Lupei;Gao, Xue;Gao, Huijiang;Xu, Lingyang;Li, Junya;Guo, Peng;Ni, Hemin;Guo, Yong;Hay, El Hamidi A.;Wu, Xiaolin;Wu, Xiaolin

作者机构：

关键词： Bayesian models;Convergence diagnosis;Genomic prediction;High-performance computing;Tunable burn-in

期刊名称：BMC BIOINFORMATICS （ 影响因子：3.169； 五年影响因子：3.629 ）

ISSN： 1471-2105

年卷期： 2018 年 19 卷

页码：

收录情况： SCI

摘要： Background: Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium. Results: Automatic tuning of the burn-in length for running multiple-chain MCMC was proposed in the context of genomic predictions using BayesA and BayesC pi models. The performance of parallel computing versus sequential computing and tunable burn-in MCMC versus fixed burn-in MCMC was assessed using simulation data sets as well by applying these methods to genomic predictions of a Chinese Simmental beef cattle population. The results showed that tunable burn-in parallel MCMC had greater speedups than fixed burn-in parallel MCMC, and both had greater speedups relative to sequential (single-chain) MCMC. Nevertheless, genomic estimated breeding values (GEBVs) and genomic prediction accuracies were highly comparable between the various computing approaches. When applied to the genomic predictions of four quantitative traits in a Chinese Simmental population of 1217 beef cattle genotyped by an Illumina Bovine 770 K SNP BeadChip, tunable burn-in multiple-chain BayesCp (TBM-BayesC pi) outperformed tunable burn-in multiple-chain BayesCp (TBM-BayesA) and Genomic Best Linear Unbiased Prediction (GBLUP) in terms of the prediction accuracy, although the differences were not necessarily caused by computational factors and could have been intrinsic to the statistical models per se. Conclusions: Automatically tunable burn-in multiple-chain MCMC provides an accurate and cost-effective tool for high-performance computing of Bayesian genomic prediction models, and this algorithm is generally applicable to high-performance computing of any complex Bayesian statistical model.

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