Back to Search
Start Over
The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle
- Source :
- PLoS ONE, Vol 11, Iss 5, p e0154118 (2016), PLoS ONE
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
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.
- Subjects :
- 0301 basic medicine
Male
Normal Distribution
lcsh:Medicine
Beef cattle
Bayes' theorem
Mathematical and Statistical Techniques
Animal Products
Statistics
Medicine and Health Sciences
lcsh:Science
Mathematics
Hyperparameter
Mammals
Multidisciplinary
Small number
Simulation and Modeling
Agriculture
04 agricultural and veterinary sciences
Genomics
Ruminants
Vertebrates
Physical Sciences
Female
Beef
Research Article
Veterinary Medicine
Livestock
Meat
Bayesian probability
Quantitative Trait Loci
Bayesian Method
Research and Analysis Methods
Genome Complexity
Polymorphism, Single Nucleotide
Normal distribution
03 medical and health sciences
Bovines
Prior probability
Genetics
Animals
Nutrition
Models, Statistical
business.industry
lcsh:R
0402 animal and dairy science
Organisms
Biology and Life Sciences
Computational Biology
Bayes Theorem
Heritability
Probability Theory
Probability Distribution
040201 dairy & animal science
Biotechnology
Diet
030104 developmental biology
Genetic Loci
Food
Amniotes
Cattle
Veterinary Science
Livestock Care
lcsh:Q
business
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....cafb29faf307337004177d54084a8d1b