Improved genetic evaluation in Karan Fries cattle using multitrait single-step genomic best linear unbiased prediction method

The advancement in the methodology for genomic prediction, using Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP), has significantly improved the precision and efficiency of selecting desirable traits in livestock in recent times. Karan Fries (KF) cattle, developed since 1980s, are selected on the basis of pedigree and phenotypes so far. It is fairly reasonable that further enhancement in the performance can be achieved by incorporating genomic information for the estimation of genetic parameters and breeding values for higher accuracy. Therefore, the main aim of the study was to evaluate the accuracy of estimation for milk production performance traits in KF cattle using both pedigree and a limited genomic data. The 8 454 phenotypic records of KF cows, spanning from 1982 to 2023, and 355 genotyped animals were analysed to estimate variance-covariance components and genetic parameters using Restricted Maximum Likelihood (REML) and ssGREML methods. Heritability estimates for key traits, total milk yield (TMY), 305-day milk yield (305-DMY), lactation peak yield (PY), dry period (DP), and age at first calving (AFC), were 0.24 ± 0.03, 0.25 ± 0.03, 0.27 ± 0.03, 0.11 ± 0.04, and 0.21 ± 0.03, respectively. Genetic correlations among milk production traits (TMY, 305-DMY, and PY) were high and positive (> 0.9), while both AFC and DP showed negative genetic correlations with milk production traits (ranging from -0.85 to -0.04). Phenotypic correlations between milk production traits were all positive, ranging from 0.24 to 0.82. In terms of breeding value prediction, multitrait ssGBLUP yielded more accurate Genomic Estimated Breeding Values for AFC, with a 13% increase in accuracy compared to single-trait ssGBLUP. The accuracy of breeding value prediction for TMY, 305-DMY, PY, DP, and AFC was improved by 11.53, 11.53, 9.25, 26.47 and 33.33%, respectively, when using the multitrait ssGBLUP model. The present study highlighted the effectiveness of genomic data in enhancing the accuracy of genetic parameters for production and reproduction traits in KF cattle, even without their own phenotypes that will permit early selection for more effective breeding programmes using multitrait ssGBLUP in the tropics, having a global relevance.

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Journal
animal
Published
2025-10-25
DOI
https://doi.org/10.1016/j.animal.2025.101692
Primary Topic
Genetic and phenotypic traits in livestock
Type
article
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article

Improved genetic evaluation in Karan Fries cattle using multitrait single-step genomic best linear unbiased prediction method

Jayesh Vyas, Sukhwinder Singh, Indrajeet Kumar, G. R. Gowane et al.
animal
Genetic and phenotypic traits in livestock
article

Improved genetic evaluation in Karan Fries cattle using multitrait single-step genomic best linear unbiased prediction method

Jayesh Vyas, Sukhwinder Singh, Indrajeet Kumar, G. R. Gowane, Anupama Mukherjee, Sabyasachi Mukherjee, Anil Chitra, Lal Muansangi, Irusappan Ilayaraja
article en

Abstract

The advancement in the methodology for genomic prediction, using Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP), has significantly improved the precision and efficiency of selecting desirable traits in livestock in recent times. Karan Fries (KF) cattle, developed since 1980s, are selected on the basis of pedigree and phenotypes so far. It is fairly reasonable that further enhancement in the performance can be achieved by incorporating genomic information for the estimation of genetic parameters and breeding values for higher accuracy. Therefore, the main aim of the study was to evaluate the accuracy of estimation for milk production performance traits in KF cattle using both pedigree and a limited genomic data. The 8 454 phenotypic records of KF cows, spanning from 1982 to 2023, and 355 genotyped animals were analysed to estimate variance-covariance components and genetic parameters using Restricted Maximum Likelihood (REML) and ssGREML methods. Heritability estimates for key traits, total milk yield (TMY), 305-day milk yield (305-DMY), lactation peak yield (PY), dry period (DP), and age at first calving (AFC), were 0.24 ± 0.03, 0.25 ± 0.03, 0.27 ± 0.03, 0.11 ± 0.04, and 0.21 ± 0.03, respectively. Genetic correlations among milk production traits (TMY, 305-DMY, and PY) were high and positive (> 0.9), while both AFC and DP showed negative genetic correlations with milk production traits (ranging from -0.85 to -0.04). Phenotypic correlations between milk production traits were all positive, ranging from 0.24 to 0.82. In terms of breeding value prediction, multitrait ssGBLUP yielded more accurate Genomic Estimated Breeding Values for AFC, with a 13% increase in accuracy compared to single-trait ssGBLUP. The accuracy of breeding value prediction for TMY, 305-DMY, PY, DP, and AFC was improved by 11.53, 11.53, 9.25, 26.47 and 33.33%, respectively, when using the multitrait ssGBLUP model. The present study highlighted the effectiveness of genomic data in enhancing the accuracy of genetic parameters for production and reproduction traits in KF cattle, even without their own phenotypes that will permit early selection for more effective breeding programmes using multitrait ssGBLUP in the tropics, having a global relevance.

animalVol. 19(12)
National Dairy Research Institute (IN)
Openalex Percentile: Top 67%
Genetic and phenotypic traits in livestock
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