Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.
Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.
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DOI：
10.1093/genetics/iyab002
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发表时间：
2021-03-31
期刊：
影响因子：
3.3
通讯作者：
Riebler A
中科院分区：
文献类型：
--
作者：
Hem IG;Selle ML;Gorjanc G;Fuglstad GA;Riebler A
我们提出了一种新的贝叶斯方法，通过利用先前分布的专家知识(EK)来增强基因组建模的健壮性。其核心部分是将表型变异分解为加性和非加性遗传变异，从而得到直观的模型参数化，可以可视化为一棵树。树的边表示方差的比率，例如广义遗传率，其是EK自然存在的量。惩罚复杂性先验是在自下而上的过程中为树的所有边定义的，该过程尊重模型结构并将EK结合到所有级别。我们研究了具有不同变异源的模型，并比较了在植物育种中实施不同量EK的不同先验的表现。一项模拟研究表明，所提出的先验知识改进了基因组建模的稳健性，并在育种计划中选择了遗传最好的个体。无论是在遗传值上的品种选择还是在加性值上的亲本选择上，我们都观察到了这一改进，品种选择受益最大。在一个真实的案例研究中，对于标准最大似然方法没有找到方差分量的最佳估计的情况，EK提高了表型预测的准确性。最后，我们讨论了EK先验在基因组建模和育种中的重要性，并指出了基因组建模中易于使用和简约的先验的未来研究领域。
We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.
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影响因子：
3.3
作者：
Gianola, Daniel;de los Campos, Gustavo;Fernando, Rohan
通讯作者：
Fernando, Rohan
影响因子：
3.8
作者：
Bouvet, J-M;Makouanzi, G.;Vigneron, Ph
通讯作者：
Vigneron, Ph
影响因子：
5.4
作者：
Gianola, Daniel;Hospital, Frederic;Verrier, Etienne
通讯作者：
Verrier, Etienne
影响因子：
3.3
作者：
de Los Campos G;Hickey JM;Pong-Wong R;Daetwyler HD;Calus MP
通讯作者：
Calus MP
影响因子：
2.6
作者：
de Almeida Filho, Janeo Eustaquio;Rodrigues Guimaraes, Joao Filipi;Ribeiro de Resende Junior, Marcio Fernando
通讯作者：
Ribeiro de Resende Junior, Marcio Fernando