DOI QR코드

DOI QR Code

The influence of a first-order antedependence model and hyperparameters in BayesCπ for genomic prediction

  • Li, Xiujin (State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University) ;
  • Liu, Xiaohong (State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University) ;
  • Chen, Yaosheng (State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University)
  • Received : 2018.01.31
  • Accepted : 2018.06.02
  • Published : 2018.12.01

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 $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.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 $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, 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 ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ 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.

Keywords

$BayesC{\pi}$;Antedependence Model;Hyperparameter

Acknowledgement

Supported by : National Natural Science Foundation of China, China Postdoctoral Science Foundation, China Agriculture Research System

References

  1. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157:1819-29.
  2. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23. https://doi.org/10.3168/jds.2007-0980
  3. Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 2011;12:186. https://doi.org/10.1186/1471-2105-12-186
  4. Yang W, Tempelman RJ. A Bayesian antedependence model for whole genome prediction. Genetics 2012;190:1491-501. https://doi.org/10.1534/genetics.111.131540
  5. Zhu B, Zhu M, Jiang J, et al. The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese Simmental beef cattle. PLoS One 2016;11: e0154118. https://doi.org/10.1371/journal.pone.0154118
  6. Jiang J, Zhang Q, Ma L, et al. Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model. Heredity (Edinb) 2015;115:29-36. https://doi.org/10.1038/hdy.2015.9
  7. Yang W, Chen C, Tempelman RJ. Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models. Genet Sel Evol 2015;47:13. https://doi.org/10.1186/s12711-015-0092-x
  8. Yi N, Xu S. Bayesian LASSO for quantitative trait loci mapping. Genetics 2008;179:1045-55. https://doi.org/10.1534/genetics.107.085589
  9. Gelman A. Prior distributions fro variance parameters in hierachical models. Bayesian Anal 2006;1:515-34. https://doi.org/10.1214/06-BA117A
  10. Gao N, Martini JWR, Zhang Z, et al. Incorporating gene annotation into genomic prediction of complex phenotypes. Genetics 2017;207:489-501.
  11. Elsen J-M, Tesseydre S, Filangi O, Le Roy P, Demeure O. XVth QTLMAS: simulated dataset. BMC Proc 2012;6(Suppl 2):S1.
  12. Valdar W, Solberg LC, Gauguier D, et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 2006;38:879-87. https://doi.org/10.1038/ng1840
  13. Legarra A, Robert-Granié C, Manfredi E, Elsen JM. Performance of genomic selection in mice. Genetics 2008;180:611-8. https://doi.org/10.1534/genetics.108.088575
  14. Valdar W, Solberg LC, Gauguier D, et al. Genetic and environmental effects on complex traits in mice. Genetics 2006;174:959-84. https://doi.org/10.1534/genetics.106.060004
  15. Gianola D, De Los Campos G, Hill WG, Manfredi E, Fernando R. Additive genetic variability and the Bayesian alphabet. Genetics 2009;183:347-63. https://doi.org/10.1534/genetics.109.103952
  16. Wang L, Edwards D, Janss L. Evaluation of antedependence model performance and genomic prediction for growth in Danish pigs. 10th World Congress on Genetics Applied to Livestock Production (WCGALP), Vancouver, Canada; 2014. p. 1-3.
  17. Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics 2010;185:1021-31. https://doi.org/10.1534/genetics.110.116855
  18. Li X, Lund MS, Janss L, et al. The patterns of genomic variances and covariances across genome for milk production traits between Chinese and Nordic Holstein populations. BMC Genet 2017;18:26.
  19. Ding X, Zhang Z, Li X, et al. Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. J Dairy Sci 2013;96:5315-23. https://doi.org/10.3168/jds.2012-6194