ArticleAsian-Australasian journal of animal sciences2018
The influence of a first-order antedependence model and hyperparameters in BayesCπ for genomic prediction.
Article in Asian-Australasian journal of animal sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Genomic Prediction Using Bayesian Regression Models With Global-Local Prior.Frontiers in genetics · 2021Article
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Abstract
objectiveThe Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of BayesCπ over BayesA/B, we have developed hyper-BayesCπ, ante-BayesCπ, and ante-hyper-BayesCπ to evaluate influences of the antedependence model and hyperparameters for vg and sg2 on BayesCπ.
methodsThree public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional BayesCπ, ante-BayesA and ante-BayesB.
resultsThrough both simulation and real data analyses, we found that hyper-BayesCπ, ante-BayesCπ and ante-hyper-BayesCπ were comparable with BayesCπ, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and π = 0.95.
conclusionHyper-BayesCπ is recommended because it avoids pre-estimated total genetic variance of a trait compared with BayesCπ and shortens computing time compared with ante-BayesB. Although the antedependence model in BayesCπ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.
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