ArticlePloS one2016
The Impact of Variable Degrees of Freedom and Scale Parameters in Bayesian Methods for Genomic Prediction in Chinese Simmental Beef Cattle.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A fast method for breeding by design via G × E interactions detected in large-scale climatic, phenomic and genomic data.National science review · 2026Article
- Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Crop phenotype prediction using SNP context and whole-genome feature embedding based on DNABERT-2.Plant methods · 2026Article
- Fast3VmrMLM: A fast algorithm that integrates genome-wide scanning with machine learning to accelerate gene mining and breeding by design for polygenic traits in large-scale GWAS datasets.Plant communications · 2025Article
- Genomic selection in pig breeding: comparative analysis of machine learning algorithms.Genetics, selection, evolution : GSE · 2025Article
- The integration of quantile regression with 3VmrMLM identifies more QTNs and QTN-by-environment interactions using SNP- and haplotype-based markers.Plant communications · 2025Article
- IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.Computational and structural biotechnology journal · 2024Article
- BLUPmrMLM: A Fast mrMLM Algorithm in Genome-wide Association Studies.Genomics, proteomics & bioinformatics · 2024Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study.Briefings in bioinformatics · 2024Article
- Genomic prediction of carcass traits using different haplotype block partitioning methods in beef cattle.Evolutionary applications · 2022Article
- Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel.Animals : an open access journal from MDPI · 2021Article
- Genomic Prediction Using Bayesian Regression Models With Global-Local Prior.Frontiers in genetics · 2021Article
- Genomic Prediction Using LD-Based Haplotypes Inferred From High-Density Chip and Imputed Sequence Variants in Chinese Simmental Beef Cattle.Frontiers in genetics · 2021Article
- mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies.Genomics, proteomics & bioinformatics · 2020Article
- Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.Frontiers in genetics · 2020Article
- Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle.Animal genetics · 2019Article
- Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle.PloS one · 2019Article
- The influence of a first-order antedependence model and hyperparameters in BayesCπ for genomic prediction.Asian-Australasian journal of animal sciences · 2018Article
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13 authors.
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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.
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