Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations
Factors Affecting the Accuracy of Genomic Selection for Agricultural Economic Traits in Maize, Cattle, and Pig Populations
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影响玉米、牛和猪农业经济性状基因组选择准确性的因素
DOI：
10.3389/fgene.2019.00189
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发表时间：
2019-03
影响因子：
3.7
通讯作者：
Liu Xiaolei
中科院分区：
文献类型：
--
作者：
Zhang Haohao;Yin Lilin;Wang Meiyue;Yuan Xiaohui;Liu Xiaolei
基因组选择（GS）已被证明是一种在植物和家畜育种中估算遗传值的有力工具。新开发的测序技术极大地降低了基因分型成本，并显著扩大了基因分型的规模。（原文最后“the scale of g”表述似乎不完整，推测完整意思可能是基因分型规模）
Genomic Selection (GS) has been proved to be a powerful tool for estimating genetic values in plant and livestock breeding. Newly developed sequencing technologies have dramatically reduced the cost of genotyping and significantly increased the scale of genotype data that used for GS. Meanwhile, state-of-the-art statistical methods were developed to make the best use of high marker density genotype data. In this study, 14 traits from four data sets of three species (maize, cattle, and pig) and five influential factors that affect the prediction accuracy were evaluated, including marker density (from 1 to ~600 k), statistical method (GBLUP-A, GBLUP-AD, and BayesR), minor allele frequency (MAF), heritability, and genetic architecture. Results indicate that in the GBLUP method, higher marker density leads to a higher prediction accuracy. In contrast, BayesR method needs more Monte Carlo Markov Chain (MCMC) iterations to reach the convergence and get reliable prediction values. BayesR outperforms GBLUP in predicting high or medium heritability trait that affected by one or several genes with large effects, while GBLUP performs similarly or slightly better than BayesR in predicting low heritability trait that controlled by a large amount of genes with minor effects. Prediction accuracy of trait with complex genetic architecture can be improved by increasing the marker density. Interestingly, for simple traits that controlled by one or several genes with large effects, higher marker density can cause a lower prediction accuracy if the QTN is included, but leads to a higher prediction accuracy if the QTN is excluded. The quantity of genetic markers with low MAF would not significantly affect the prediction accuracy of GBLUP, but results in a bad prediction accuracy performance of BayesR method. Compared with GBLUP-A, GBLUP-AD didn't show any advantages in capturing the non-additive variance for the traits with high heritability. The factors that affected prediction accuracy are discussed in this study and indicate that a combination of either GBLUP or BayesR method with moderate marker density and favorable polymorphism single nucleotide polymorphisms (SNPs) (~25 k SNPs) would always produce a good and stable prediction accuracy with acceptable breeding and computational costs.
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影响因子：
4.5
作者：
Liu X;Huang M;Fan B;Buckler ES;Zhang Z
通讯作者：
Zhang Z
影响因子：
3.3
作者：
T. Meuwissen;B. Hayes;M. Goddard
通讯作者：
T. Meuwissen;B. Hayes;M. Goddard
影响因子：
3.7
作者：
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通讯作者：
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用于复杂表型预测的多级线性混合模型。
影响因子：
7
作者：
Weissbrod O;Geiger D;Rosset S
通讯作者：
Rosset S
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影响因子：
2.6
作者：
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通讯作者：
Li Jiaqi