Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees
Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees
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DOI：
10.1534/g3.119.201004
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发表时间：
2019-08-01
影响因子：
2.6
通讯作者：
Ribeiro de Resende Junior, Marcio Fernando
中科院分区：
文献类型：
--
作者：
de Almeida Filho, Janeo Eustaquio;Rodrigues Guimaraes, Joao Filipi;Ribeiro de Resende Junior, Marcio Fernando
利用具有密集标记和系谱信息的模型可以估计个体的遗传价值。早期的基因组模型只考虑了加性效应。然而，非加性效应的预测对于不同的森林育种系统是重要的，因为整个基因型值可以通过克隆繁殖获得。在本研究中，我们在包括或忽略非加性效应的模型中评估了标记数据与系谱信息的整合。我们用加性框架和加性优势框架测试了核希尔伯特空间（RKHS）和BayesA模型。以71个全同胞家庭923个松个体为研究对象，对树高、胸径和抗锈蚀性进行了模型性能评价。我们还模拟了具有相似遗传特性的种群，并评估了具有不同遗传结构的六个模拟性状的模型的性能。评估了不同的交叉验证策略，使用家族交叉验证获得了最高的准确性。在基因组预测模型中包含系谱信息并没有产生更高的准确性。不同的RKHS模型产生了相似的预测精度，RKHS和BayesA产生了比纯谱系模型更好的预测。在锈病发生率和模拟的加性寡生性状上，加性bayesa比RKHS具有更高的准确性。对于胸径、HT和加性显性多基因性状，RKHS模型的准确率略高于BayesA。结果表明，BayesA模型对主要效应基因较少的性状的预测效果最好，而基于RKHS模型对复杂性状克隆选择的基因型效应预测效果最好。
The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits.