19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Chinese Simmental beef cattle are the most economically important cattle breed in China. Estimated breeding values for growth, carcass, and meat quality traits are commonly used as selection criteria in animal breeding. The objective of this study was to evaluate the accuracy of alternative statistical methods for the estimation of genomic breeding values. Analyses of the accuracy of genomic best linear unbiased prediction (GBLUP), BayesB, and elastic net (EN) were performed with an Illumina BovineHD BeadChip on 1,217 animals by applying 5-fold cross-validation. Overall, the accuracies ranged from 0.17 to 0.296 for ten traits, and the heritability estimates ranged from 0.36 to 0.63. The EN (alpha = 0.001) model provided the most accurate prediction, which was also slightly higher (0.2–2%) than that of GBLUP for most traits, such as average daily weight gain (ADG) and carcass weight (CW). BayesB was less accurate for each trait than were EN (alpha = 0.001) and GBLUP. These findings indicate the importance of using an appropriate variable selection method for the genomic selection of traits and suggest the influence of the genetic architecture of the traits we analyzed.

          Related collections

          Most cited references19

          • Record: found
          • Abstract: found
          • Article: not found

          Mapping genes for complex traits in domestic animals and their use in breeding programmes.

          Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes

            Background The theory of genomic selection is based on the prediction of the effects of genetic markers in linkage disequilibrium with quantitative trait loci. However, genomic selection also relies on relationships between individuals to accurately predict genetic value. This study aimed to examine the importance of information on relatives versus that of unrelated or more distantly related individuals on the estimation of genomic breeding values. Methods Simulated and real data were used to examine the effects of various degrees of relationship on the accuracy of genomic selection. Genomic Best Linear Unbiased Prediction (gBLUP) was compared to two pedigree based BLUP methods, one with a shallow one generation pedigree and the other with a deep ten generation pedigree. The accuracy of estimated breeding values for different groups of selection candidates that had varying degrees of relationships to a reference data set of 1750 animals was investigated. Results The gBLUP method predicted breeding values more accurately than BLUP. The most accurate breeding values were estimated using gBLUP for closely related animals. Similarly, the pedigree based BLUP methods were also accurate for closely related animals, however when the pedigree based BLUP methods were used to predict unrelated animals, the accuracy was close to zero. In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy. Conclusions An animal's relationship to the reference data set is an important factor for the accuracy of genomic predictions. Animals that share a close relationship to the reference data set had the highest accuracy from genomic predictions. However a baseline accuracy that is driven by the reference data set size and the overall population effective population size enables gBLUP to estimate a breeding value for unrelated animals within a population (breed), using information previously ignored by pedigree based BLUP methods.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation

              Background Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction. Methods Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip. These bulls were clustered into five groups using K-means clustering on pedigree estimates of additive genetic relationships between animals, with the aim of increasing within-group and decreasing between-group relationships. All five combinations of four groups were used for model training, with cross-validation performed in the group not used in training. Bivariate animal models were used for each trait to estimate the genetic correlation between deregressed estimated breeding values and direct genomic values. Results Accuracies of direct genomic values ranged from 0.22 to 0.69 for the studied traits, with an average of 0.44. Predictions were more accurate when animals within the validation group were more closely related to animals in the training set. When training and validation sets were formed by random allocation, the accuracies of direct genomic values ranged from 0.38 to 0.85, with an average of 0.65, reflecting the greater relationship between animals in training and validation. The accuracies of direct genomic values obtained from training on older animals and validating in younger animals were intermediate to the accuracies obtained from K-means clustering and random clustering for most traits. The genetic correlation between deregressed estimated breeding values and direct genomic values ranged from 0.15 to 0.80 for the traits studied. Conclusions These results suggest that genomic estimates of genetic merit can be produced in beef cattle at a young age but the recurrent inclusion of genotyped sires in retraining analyses will be necessary to routinely produce for the industry the direct genomic values with the highest accuracy.
                Bookmark

                Author and article information

                Contributors
                Role: Data curationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – original draft
                Role: ResourcesRole: Validation
                Role: MethodologyRole: Resources
                Role: SoftwareRole: Supervision
                Role: Data curationRole: ValidationRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: Methodology
                Role: SupervisionRole: Visualization
                Role: SupervisionRole: Visualization
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administration
                Role: Funding acquisitionRole: InvestigationRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 February 2019
                2019
                : 14
                : 2
                : e0210442
                Affiliations
                [1 ] Laboratory of Molecular Biology and Bovine Breeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
                [2 ] Veterinary Bureau of Wulagai Precinct in Xilin Gol League, Wulagai, China
                The University of Sydney, AUSTRALIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5502-2528
                Article
                PONE-D-18-13832
                10.1371/journal.pone.0210442
                6394919
                30817758
                497b78b4-4246-412e-b980-3ad95e98efcf
                © 2019 Wang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 May 2018
                : 21 December 2018
                Page count
                Figures: 1, Tables: 6, Pages: 14
                Funding
                Funded by: National Natural Science Foundations of China
                Award ID: 31372294
                Award Recipient :
                Funded by: National Natural Science Foundations of China
                Award ID: 31472079
                Award Recipient :
                Funded by: National Natural Science Foundations of China
                Award ID: 31702084
                Award Recipient :
                Funded by: Cattle Breeding Innovative Research Team
                Award ID: cxgc-ias-03
                Award Recipient :
                This work was supported by funds from the National Natural Science Foundation of China 31872975 for Huijiang Gao, National Natural Science Foundation of China 31672384 for Lupei Zhang, National Natural Science Foundation of China 31572376 for Xue Gao, and the Chinese Academy of Agricultural Sciences of Technology Innovation Project (CAAS-XTCX2016010, CAAS-ZDXT2018006 and ASTIP-IAS03) for Junya Li.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Bovines
                Cattle
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Cattle
                Biology and Life Sciences
                Agriculture
                Animal Products
                Meat
                Beef
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Meat
                Beef
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Meat
                Beef
                Biology and Life Sciences
                Genetics
                Heredity
                Biology and Life Sciences
                Genetics
                Phenotypes
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Weight Gain
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Weight Gain
                Biology and Life Sciences
                Agriculture
                Animal Products
                Meat
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Meat
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Meat
                Custom metadata
                We confirm that all data underlying our findings are publicly available without restriction. Data is available from the Dryad Digital Repository: doi: 10.5061/dryad.4qc06.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article