Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ω constants for genomic relationships.
Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (τ: 1), several weights (ωxx, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC).
Results: The genetic variances and total heritability estimates (T2) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ω other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ω in both breeds, indicating the better accuracy of ω0.1 models. Therefore, the optimal values of ω to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS.
Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819686 | PMC |
http://dx.doi.org/10.5713/ajas.19.0182 | DOI Listing |
Curr Issues Mol Biol
December 2024
The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China.
The development of multi-omics has increased the likelihood of further improving genomic prediction (GP) of complex traits. Gene expression data can directly reflect the genotype effect, and thus, they are widely used for GP. Generally, the gene expression data are integrated into multiple random effect models as independent data layers or used to replace genotype data for genomic prediction.
View Article and Find Full Text PDFAnim Genet
February 2025
College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, Henan, China.
Body mass index (BMI) can serve as a reasonable indicator of overall body fat content in pigs. This study aimed to identify underlying variants and candidate genes associated with BMI in Yunong-black pigs. A single-step genome-wide association analysis (GWAS) was performed on 1405 BMI records and 924 Yunong-black pigs genotyped using a 50 K SNP Chip.
View Article and Find Full Text PDFBMC Genomics
December 2024
São Paulo State University, School of Agriculture and Veterinary Science, Jaboticabal, SP, 14884-900, Brazil.
Background: Reproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle.
View Article and Find Full Text PDFJ Dairy Sci
January 2025
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602; Council on Dairy Cattle Breeding, Bowie, MD 20716.
The US dairy cattle genetic evaluation is currently a multistep process, including multibreed traditional BLUP estimations followed by single-breed SNP effects estimation. Single-step GBLUP (ssGBLUP) combines pedigree and genomic data for all breeds in one analysis. Unknown parent groups (UPG) or metafounders (MF) can be used to address missing pedigree information.
View Article and Find Full Text PDFJDS Commun
November 2024
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602.
Random-regression models (RRM) are used in national genetic evaluations for longitudinal traits. The outputs of RRM are an index based on random-regression coefficients and its reliability. The reliabilities are obtained from the inverse of the coefficient matrix of mixed model equations (MME).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!