A Modified Bayesian Optimization Approach for Determining a Training Set to Identify the Best Genotypes from a Candidate Population in Genomic Selection
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引用次数: 0
Abstract
Training set optimization is a crucial factor affecting the probability of success for plant breeding programs using genomic selection. Conventionally, the training set optimization is developed to maximize Pearson’s correlation between true breeding values and genomic estimated breeding values for a testing population, because it is an essential component of genetic gain in plant breeding. However, many practical breeding programs aim to identify the best genotypes for target traits in a breeding population. A modified Bayesian optimization approach is therefore developed in this study to construct training sets for tackling such an interesting problem. The proposed approach is based on Monte Carlo simulation and data cross-validation, which is shown to be competitive with the existing methods developed to achieve the maximal Pearson’s correlation. Four real genome datasets, including two rice, one wheat, and one soybean, are analyzed in this study. An R package is generated to facilitate the application of the proposed approach. Supplementary materials accompanying this paper appear online.
训练集优化是影响使用基因组选择的植物育种计划成功概率的关键因素。传统上,训练集优化的目的是使测试群体的真实育种值与基因组估计育种值之间的皮尔逊相关性最大化,因为它是植物育种遗传增益的重要组成部分。然而,许多实际的育种计划都旨在确定育种群体中目标性状的最佳基因型。因此,本研究开发了一种改进的贝叶斯优化方法,以构建训练集来解决这一有趣的问题。所提出的方法基于蒙特卡罗模拟和数据交叉验证,与为实现最大皮尔逊相关性而开发的现有方法相比,具有很强的竞争力。本研究分析了四个真实基因组数据集,包括两个水稻、一个小麦和一个大豆。为了便于应用所提出的方法,我们生成了一个 R 软件包。本文所附的补充材料可在线查阅。
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.