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

@inproceedings{Zhu2016TheIO,
  title={The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle},
  author={Bo Zhu and Miao Zhu and Jicai Jiang and Hong Niu and Yanhui Wang and Yang Wu and Lingyang Xu and Yan Chen and Lupei Zhang and Xue Wen Gao and Huijiang Gao and Jianfeng Liu and Junya Li},
  booktitle={PloS one},
  year={2016}
}
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… CONTINUE READING
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