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Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

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    Genomic predictions in Angus cattle using single-step genomic best linear unbiased prediction (ssGBLUP) improved accuracy for traits like birth weight. The reference population size significantly impacts predictive gains, making ssGBLUP feasible for widespread genomic evaluation.

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    Area of Science:

    • Animal Genetics and Breeding
    • Quantitative Genetics
    • Genomic Prediction

    Background:

    • Traditional breeding values (EBV) rely on pedigree information and can be limited in accuracy.
    • Genomic selection offers a powerful tool to enhance accuracy by utilizing single nucleotide polymorphism (SNP) data.
    • Single-step genomic best linear unbiased prediction (ssGBLUP) integrates pedigree and genomic information for more precise EBV.

    Purpose of the Study:

    • To investigate the predictive ability of genomic EBV derived from ssGBLUP in Angus cattle.
    • To compare ssGBLUP with traditional BLUP methods across various economically important traits.
    • To evaluate the impact of reference population size and composition on prediction accuracy.

    Main Methods:

    • Utilized a large dataset with millions of records for birth weight (BiW), weaning weight (WW), postweaning gain (PWG), and calving ease (CE).
    • Employed ssGBLUP and indirect prediction methods using SNP effects derived from different reference populations (ref_2k, ref_8k, ref_33k).
    • Implemented cross-validation to assess predictive ability on a validation population and used an algorithm for proven and young animals (APY).

    Main Results:

    • ssGBLUP significantly improved predictivity for growth traits (BiW, WW, PWG) compared to BLUP, with gains increasing with larger reference populations (e.g., ref_33k).
    • Predictivity for calving ease (CE) remained lower, attributed to its low incidence rate.
    • Indirect predictions using SNP effects were as accurate as full ssGBLUP, and efficient algorithms (APY) provided substantial gains with reduced computational load.

    Conclusions:

    • Genomic evaluation using ssGBLUP is feasible in beef cattle, maintaining existing model complexities.
    • Predictive gains are strongly influenced by the reference population's composition and size.
    • Indirect genomic predictions enable accurate evaluations for young animals, especially with large reference populations, making ssGBLUP applicable to extensive genotyped populations.