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The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle

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  • Bo Zhu
  • Miao Zhu
  • Jicai Jiang
  • Hong Niu
  • Yanhui Wang
  • Yang Wu
  • Lingyang Xu
  • Yan Chen
  • Lupei Zhang
  • Xue Gao
  • Huijiang Gao
  • Jianfeng Liu
  • Junya Li

Abstract

Three conventional Bayesian approaches (BayesA, BayesB and BayesCπ) have been demonstrated to be powerful in predicting genomic merit for complex traits in livestock. A priori, these Bayesian models assume that the non-zero SNP effects (marginally) follow a t-distribution depending on two fixed hyperparameters, degrees of freedom and scale parameters. In this study, we performed genomic prediction in Chinese Simmental beef cattle and treated degrees of freedom and scale parameters as unknown with inappropriate priors. Furthermore, we compared the modified methods (BayesFA, BayesFB and BayesFCπ) with their corresponding counterparts using simulation datasets. We found that the modified methods with distribution assumed to the two hyperparameters were beneficial for improving the predictive accuracy. Our results showed that the predictive accuracies of the modified methods were slightly higher than those of their counterparts especially for traits with low heritability and a small number of QTLs. Moreover, cross-validation analysis for three traits, namely carcass weight, live weight and tenderloin weight, in 1136 Simmental beef cattle suggested that predictive accuracy of BayesFCπ noticeably outperformed BayesCπ with the highest increase (3.8%) for live weight using the cohort masking cross-validation.

Suggested Citation

  • Bo Zhu & Miao Zhu & Jicai Jiang & Hong Niu & Yanhui Wang & Yang Wu & Lingyang Xu & Yan Chen & Lupei Zhang & Xue Gao & Huijiang Gao & Jianfeng Liu & Junya Li, 2016. "The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-16, May.
    • Handle: RePEc:plo:pone00:0154118
    DOI: 10.1371/journal.pone.0154118
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    References listed on IDEAS

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    1. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    2. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
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    1. Peng Guo & Bo Zhu & Lingyang Xu & Hong Niu & Zezhao Wang & Long Guan & Yonghu Liang & Hemin Ni & Yong Guo & Yan Chen & Lupei Zhang & Xue Gao & Huijiang Gao & Junya Li, 2017. "Genomic prediction with parallel computing for slaughter traits in Chinese Simmental beef cattle using high-density genotypes," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.

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