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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

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 hype...

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Autores principales: Zhu, Bo, Zhu, Miao, Jiang, Jicai, Niu, Hong, Wang, Yanhui, Wu, Yang, Xu, Lingyang, Chen, Yan, Zhang, Lupei, Gao, Xue, Gao, Huijiang, Liu, Jianfeng, Li, Junya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854473/
https://www.ncbi.nlm.nih.gov/pubmed/27139889
http://dx.doi.org/10.1371/journal.pone.0154118
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author Zhu, Bo
Zhu, Miao
Jiang, Jicai
Niu, Hong
Wang, Yanhui
Wu, Yang
Xu, Lingyang
Chen, Yan
Zhang, Lupei
Gao, Xue
Gao, Huijiang
Liu, Jianfeng
Li, Junya
author_facet Zhu, Bo
Zhu, Miao
Jiang, Jicai
Niu, Hong
Wang, Yanhui
Wu, Yang
Xu, Lingyang
Chen, Yan
Zhang, Lupei
Gao, Xue
Gao, Huijiang
Liu, Jianfeng
Li, Junya
author_sort Zhu, Bo
collection PubMed
description 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.
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spelling pubmed-48544732016-05-07 The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle Zhu, Bo Zhu, Miao Jiang, Jicai Niu, Hong Wang, Yanhui Wu, Yang Xu, Lingyang Chen, Yan Zhang, Lupei Gao, Xue Gao, Huijiang Liu, Jianfeng Li, Junya PLoS One Research Article 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. Public Library of Science 2016-05-03 /pmc/articles/PMC4854473/ /pubmed/27139889 http://dx.doi.org/10.1371/journal.pone.0154118 Text en © 2016 Zhu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhu, Bo
Zhu, Miao
Jiang, Jicai
Niu, Hong
Wang, Yanhui
Wu, Yang
Xu, Lingyang
Chen, Yan
Zhang, Lupei
Gao, Xue
Gao, Huijiang
Liu, Jianfeng
Li, Junya
The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title_full The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title_fullStr The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title_full_unstemmed The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title_short The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
title_sort impact of variable degrees of freedom and scale parameters in bayesian methods for genomic prediction in chinese simmental beef cattle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854473/
https://www.ncbi.nlm.nih.gov/pubmed/27139889
http://dx.doi.org/10.1371/journal.pone.0154118
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