Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

文献类型： 外文期刊

第一作者： Xu, Ling

作者： Xu, Ling;Wang, Zezhao;Xu, Lei;Liu, Ying;Chen, Yan;Xu, Lingyang;Gao, Xue;Zhang, Lupei;Gao, Huijiang;Zhu, Bo;Li, Junya;Gao, Ning;Gao, Huijiang;Zhu, Bo;Li, Junya

作者机构：

关键词： genomic prediction; genome annotation; haplotype; Chinese Simmental beef cattle; prediction accuracy

期刊名称：FRONTIERS IN GENETICS （ 影响因子：4.599； 五年影响因子：4.888 ）

ISSN：

年卷期： 2020 年 11 卷

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收录情况： SCI

摘要： Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3 '/5 ' untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4-9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.

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