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

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dc.creatorShaohua Zhu
dc.creatorTingting Guo
dc.creatorChao Yuan
dc.creatorJianbin Liu
dc.creatorJianye Li
dc.creatorMei Han
dc.creatorHongchang Zhao
dc.creatorYi Wu
dc.creatorWeibo Sun
dc.creatorXijun Wang
dc.creatorTianxiang Wang
dc.creatorJigang Liu
dc.creatorTiambo, Christian K.
dc.creatorYaojing Yue
dc.creatorBohui Yang
dc.date2021-10-19
dc.date2021-08-15T07:35:02Z
dc.date2021-08-15T07:35:02Z
dc.date.accessioned2026-06-27T16:51:46Z
dc.descriptionThe 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, BayesCπ, 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.
dc.identifierhttps://hdl.handle.net/10568/114635
dc.identifier.urihttp://hdl.handle.net/123456789/135392
dc.languageen
dc.publisherOxford University Press
dc.rightsOpen Access
dc.sourceShaohua Zhu, Tingting Guo, Chao Yuan, Jianbin Liu, Jianye Li, Mei Han, Hongchang Zhao, Yi Wu, Weibo Sun, Xijun Wang, Tianxiang Wang, Jigang Liu, Tiambo, C.K., Yaojing Yue and Bohui Yang. 2021. Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep. G3: Genes, Genomes, Genetics 11(11): jkab206.
dc.subjectsheep
dc.subjectsmall ruminants
dc.subjectanimal breeding
dc.subjectgenetics
dc.titleEvaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino Sheep
dc.typeJournal Article

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Instituto Internacional de Investigaciones Pecuarias (ILRI)