The influence of a first-order antedependence model and hyperparameters in BayesC? for genomic prediction.

ABSTRACT:

Objective

The 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 BayesC? over BayesA/B, we have developed hyper-BayesC?, ante-BayesC?, and ante-hyper-BayesC? to evaluate influences of the antedependence model and hyperparameters for vg and sg2 on BayesC?.

Methods

Three 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 BayesC?, ante-BayesA and ante-BayesB.

Results

Through both simulation and real data analyses, we found that hyper-BayesC?, ante-BayesC? and ante-hyper-BayesC? were comparable with BayesC?, 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 ? = 0.95.

Conclusion

Hyper-BayesC? is recommended because it avoids pre-estimated total genetic variance of a trait compared with BayesC? and shortens computing time compared with ante-BayesB. Although the antedependence model in BayesC? 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.

SUBMITTER: Li X 

PROVIDER: S-EPMC6212739 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature