Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes
Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes
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DOI：
10.1534/g3.119.400154
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发表时间：
2018-10
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影响因子：
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通讯作者：
Haipeng Yu;Malachy T. Campbell;Qi Zhang;H. Walia;G. Morota
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文献类型：
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作者：
Haipeng Yu;Malachy T. Campbell;Qi Zhang;H. Walia;G. Morota
随着高通量表型分析平台的出现，植物育种者有了一种方法来评估大育种群体的许多性状。然而，以统计学稳健的方式理解高维性状之间的遗传相互依赖性仍然是一个重大挑战。由于多种表型可能具有相互关系，因此阐明经济上重要的性状之间的相互依赖性可以更好地为育种决策提供信息，并加速植物的遗传改良。本研究旨在利用验证性因子分析和图解模型来阐明水稻不同农艺性状间的遗传相关性。我们使用贝叶斯网络来描述表型之间的条件依赖关系，这不能通过标准的多性状分析获得。我们利用贝叶斯验证性因子分析，假设48个观察到的表型导致6个潜在变量，包括粮食形态，形态，开花时间，生理，产量，和形态盐的反应。随后，使用单核苷酸多态性研究每个潜在变量（也称为因子）的遗传学。通过拟合四种算法（即，爬山、禁忌、最大-最小爬山和一般2阶段限制最大化算法）。生理成分影响开花时间和籽粒形态，形态和籽粒形态影响产量。总之，我们表明贝叶斯网络与因子分析相结合，可以提供一种有效的方法来了解表型之间的相互依赖模式，并预测在相互关联的复杂性状系统中与目标性状相关的外部干预或选择的潜在影响。
With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms (i.e., Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.