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• W3181160463 abstract "This research aimed to compare single-step random regression models with third-order Legendre polynomials (RRLP) and splines with four (RRSP4) and five (RRSP5) knots for milk yield (MY) and fat percentage (FP) genomic-polygenic evaluations in the Thai multibreed dairy population. Models were compared using estimates of variance components, genetic parameters, goodness of fit, genomic-polygenic EBV (GPEBV) accuracies, and animal rankings. The dataset included pedigree and monthly test-day records (69,029 for MY; 29,878 for FP) of 7,206 first-lactation cows from 761 farms, and genotypic records (74,144 actual and imputed SNP) from 2,661 animals. Variance components and genetic parameters for MY and FP were estimated using REML procedures. Models contained contemporary group (herd-year-season), calving age, heterozygosity, and population lactation curve regression coefficients as fixed effects. Random effects were animal additive genetic, permanent environment random regression coefficients, and residual. The population lactation curve, additive genetic, and permanent environment effects were fitted using regression coefficients of third-order Legendre polynomials for RRLP, splines of four knots for RRSP4, and five knots for RRSP5. The estimates of 305-day additive genetic variances (σ^a2) and heritabilities (h^2) were higher for RRLP (MY: σ^a2 = 279,893.2 kg2, h^2 = 0.27; FP: σ^a2 = 0.10%2, h^2 = 0.16) than for RRSP4 (MY: σ^a2 = 260,178.1 kg2, h^2 = 0.19; FP: σ^a2 = 0.08%2, h^2 = 0.11), and for RRSP5 (MY: σ^a2 = 266,198.0 kg2, h^2 = 0.20; FP: σ^a2 = 0.08%2, h^2 = 0.12). Similarly, RRLP yielded better goodness of fit and higher GPEBV accuracies than RRSP4 and RRSP5. The goodness of fit values for RRLP were 293,813 for -2 log-likelihood (-2logL), 293,855 for the Akaike's information criterion (AIC), and 293,915 for the Bayesian information criterion (BIC). The corresponding values for RRSP4 were 362,738 for -2logL, 362,888 for AIC, and 363,101 for BIC, and those for RRSP5 were 354,473 for -2logL, 354,699 for AIC, and 355,020 for BIC. Lastly, the GPEBV accuracies for RRLP were 49.3% for MY and 38.6% for FP, those for RRSP4 were 47.2% for MY and 37.8% for FP, and the ones for RRSP5 were 47.4% for MY, 37.5% for FP. Rank correlations between animal GPEBV from these three models were high ranging from 0.91 to 0.98 for MY and 0.88 to 0.98 for FY. Results indicated that GPEBV from RRLP should be preferred to GPEBV from RRSP4 and RRSP5 to increase selection responses for MY and FP in the Thai multibreed dairy population." @default.
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• W3181160463 date "2021-09-01" @default.
• W3181160463 modified "2023-09-23" @default.
• W3181160463 title "Genomic-polygenic evaluations using random regression models with Legendre polynomials and linear splines for milk yield and fat percentage in the Thai multibreed dairy cattle population" @default.
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• W3181160463 doi "https://doi.org/10.1016/j.livsci.2021.104619" @default.
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