Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

文献类型： 外文期刊

第一作者： Liu, Tianfei

作者： Liu, Tianfei;Luo, Chenglong;Ma, Jie;Wang, Yan;Shu, Dingming;Qu, Hao;Liu, Tianfei;Luo, Chenglong;Ma, Jie;Wang, Yan;Shu, Dingming;Qu, Hao;Liu, Tianfei;Su, Guosheng

作者机构：

期刊名称：HEREDITY （ 影响因子：3.832； 五年影响因子：4.412 ）

ISSN： 0018-067X

年卷期： 2022 年 128 卷 3 期

页码：

收录情况： SCI

摘要： 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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