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Genomic analysis of feed efficiency traits in beef cattle using random regression models.

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Summary

Genomic evaluations using random regression models with B-splines improve feed efficiency in beef cattle. This allows for reduced testing periods, enhancing selection strategies for traits like dry matter intake and weight gain.

Keywords:
B‐splinesLegendre polynomialsfeed intakegenomic selectionlongitudinal datamodel comparison

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Area of Science:

  • Animal Genetics and Breeding
  • Quantitative Genetics
  • Beef Cattle Production

Background:

  • Feed efficiency is crucial for beef cattle profitability and sustainability, impacting input demand and methane emissions.
  • Traditional feed efficiency calculations use average daily intake and weight gain.
  • Longitudinal analysis via random regression models (RRMs) accounts for genetic and environmental effects over time.

Purpose of the Study:

  • To propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI), and residual weight gain (RWG) using RRMs.
  • To compare the goodness-of-fit of RRMs using Legendre polynomials (LP) and B-spline functions.
  • To evaluate genetic parameters and inform new selection strategies for feed efficiency traits.

Main Methods:

  • Genomic breeding values (GEBVs) were estimated using RRMs under ssGBLUP for Nellore cattle.
  • Orthogonal LPs and B-spline functions were employed, with model comparison based on the deviance information criterion (DIC).
  • Rankings of weekly and overall GEBVs were compared using Spearman correlation and percentage of common individuals.

Main Results:

  • Linear B-spline functions with heterogeneous residual variance showed the best goodness-of-fit.
  • Heritability estimates across the 84-day test ranged from 0.03 to 0.30 for DMI, BWG, RFI, and RWG.
  • High genetic correlations were observed for DMI and RFI, while BWG and RWG showed negative correlations early in the test.
  • GEBV rankings for DMI and RFI at week 8 closely matched overall rankings (Spearman correlation 0.95-1.00).
  • BWG and RWG rankings at week 11 showed high correlation with overall rankings (Spearman correlation 0.94-0.98).

Conclusions:

  • Random regression models using linear B-splines are a viable method for genomic evaluation of feed efficiency.
  • Sufficient additive genetic variance exists for DMI, RFI, BWG, and RWG, supporting moderate selection response.
  • Performance testing can be shortened to 56 days for DMI/RFI selection and 77 days for BWG/RWG selection.