Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities.
Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities.
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DOI：
10.1038/s41437-022-00504-6
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发表时间：
2022-03
期刊：
影响因子：
3.8
通讯作者：
Su G
中科院分区：
文献类型：
--
作者：
Liu T;Luo C;Ma J;Wang Y;Shu D;Qu H;Su G
显性效应被认为是影响复杂性状的关键因素。然而，之前的研究表明，模型的改进，包括显性效应在内，通常小于1%。本研究提出了一种称为 CADM 的新型基因组预测方法，该方法通过杂合基因型上的位点特异性权重将加性遗传效应和显性遗传效应结合起来。据我们所知，这是第一项针对基因组预测的加权显性效应的研究。该方法应用于鸡（511 只鸟）和猪（3534 只动物）数据集的分析。使用5倍交叉验证方法来评估基因组预测能力。将CADM模型与考虑加性和显性遗传效应（ADM）的典型模型以及仅考虑加性遗传效应（AM）的模型进行了比较。基于鸡数据，使用CADM模型，对所有三个性状（第12周体重、内脏百分比和胸肌百分比）的基因组预测能力都有所提高，与AM模型相比，预测精度平均提高27.1%，而ADM模型并不优于AM模型。基于猪数据，CADM模型提高了猪所有三个性状（性状名称被屏蔽，这里指定为T1、T2和T3）的基因组预测能力，平均提高了26.3%，而ADM模型与AM模型相比没有提高，甚至略有下降。结果表明，显性遗传变异是表型变异的重要来源之一，新型预测模型显着提高了基因组预测的准确性。
The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.
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DOI：
10.1186/s12711-016-0186-0
发表时间：
2016-02-01
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
Aliloo H;Pryce JE;González-Recio O;Cocks BG;Hayes BJ
通讯作者：
Hayes BJ
影响因子：
3.3
作者：
Vitezica, Zulma G.;Legarra, Andres;Varona, Luis
通讯作者：
Varona, Luis
使用基因组最佳线性无偏预测预测水稻的杂交性能
DOI：
10.1073/pnas.1413750111
发表时间：
2014-08-26
影响因子：
11.1
作者：
Xu, Shizhong;Zhu, Dan;Zhang, Qifa
通讯作者：
Zhang, Qifa
DOI：
10.1534/g3.111.001453
发表时间：
2012-04
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Cleveland MA;Hickey JM;Forni S
通讯作者：
Forni S
影响因子：
2.9
作者：
Liu T;Qu H;Luo C;Shu D;Wang J;Lund MS;Su G
通讯作者：
Su G