Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

文献类型： 外文期刊

第一作者： Zhu, Shaohua

作者： Zhu, Shaohua;Guo, Tingting;Yuan, Chao;Liu, Jianbin;Li, Jianye;Han, Mei;Zhao, Hongchang;Wu, Yi;Sun, Weibo;Yang, Bohui;Zhu, Shaohua;Guo, Tingting;Yuan, Chao;Liu, Jianbin;Li, Jianye;Han, Mei;Zhao, Hongchang;Wu, Yi;Sun, Weibo;Yue, Yaojing;Wang, Xijun;Wang, Tianxiang;Liu, Jigang;Tiambo, Christian Keambou

作者机构：

关键词： genomic prediction; Alpine Merino sheep; wool traits; GBLUP; Bayesian alphabet; marker density; GenPred; shared data resource

期刊名称：G3-GENES GENOMES GENETICS （ 影响因子：2.6； 五年影响因子：3.0 ）

ISSN： 2160-1836

年卷期： 2021 年 11 卷 11 期

页码：

收录情况： SCI

摘要： The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesC pi, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

分类号：

  • 相关文献

[1]Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep. Zhu, Shaohua,Guo, Tingting,Yuan, Chao,Liu, Jianbin,Li, Jianye,Han, Mei,Zhao, Hongchang,Wu, Yi,Sun, Weibo,Yang, Bohui,Zhu, Shaohua,Guo, Tingting,Yuan, Chao,Liu, Jianbin,Li, Jianye,Han, Mei,Zhao, Hongchang,Wu, Yi,Sun, Weibo,Yue, Yaojing,Wang, Xijun,Wang, Tianxiang,Liu, Jigang,Tiambo, Christian Keambou. 2021

作者其他论文 更多>>

微信小程序