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 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.
Highlights
Submitted Jan 31, 2018; Revised Apr 19, 2018; ante-hypeArc-cBepateydeJsuCn π2, w20e1r8e comparable with BayesCπ, Conclusion: Hyper-BayesCπ is recommended because it avoids pre-estimated total genetic avnatrei-aBnacyeesoBf, aantdraanittec-oBmaypesaArerdegwaridtihngBtahyeesCπ and shortens computing time compared with prediction accuracy and bias, except the situation in whichainanntoteeu--BBr aasyytueessdBBy.,pAlearlrftoghreomruergdehasiltghdneaiftiaacnawntetildtyhewphoeirngsdhe ednecnesmityodchelipinmBaayyebseCuπsdedidtnoovtaslhidoawtetihteaagdaivnanintatghees when using a few single nucleotide polymorphisms (SNP) and π = 0.95
Habier et al [3] firstly developed BayesCπ for genomic prediction to address the drawtion than their corresponding classical counterparts. bGaicvkens oafdvBaanyteagsAes aonfd BayesB with respect to influences of prior hyperparameters and the, we have developed hyper-BayesCπ, ante-BayesCπ, andprainotre-phryopbear-bBilaityyesπCtπhat a SNP has zero effect
In the scenario of using all SNPs, ante-hyper-BayesCπ performed as well as hyper-BayesCπ, which was 0.6% higher than BayesCπ and ante-BayesCπ, 0.5% for ante-BayesB, and 0.8% for ante-BayesA, respectively
Summary
BayesCπ over SBtaateyKeesyAL/aBbo,rawtoeryhoaf Bvieocdonetvroel,loScpheodol ohfyper-BayesaCdπv, aanttea-gBeasyeosfCBπa, yaendsCaπnteo-vheypreBr-aByaeyseAsC/Bπ, we have developed hyper-BayesCπ, ante-BayesCπ, to evaluate.
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