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The genetic relationship between feed efficiency and host resistance to parasites: insights from experimental infections in ewe lambs from divergent lines.

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Predicting feed efficiency traits in growing lambs from their ruminal microbiota.

Q Le Graverand1, C Marie-Etancelin1, A Meynadier1

  • 1GenPhySE, Université de Toulouse, INRAE, ENVT, 24 Chemin de Borde-Rouge-Auzeville CS 52627, F-31326 Castanet-Tolosan, France.

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Rumen microbiota analysis is not the best method for predicting feed efficiency in sheep. Standard measurements like body weight (BW) offer more cost-effective and accurate predictions for livestock production traits.

Keywords:
Meat sheepMicrobiomeMultivariate analysesResidual feed intakeRumen

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Area of Science:

  • Animal Science
  • Microbiology
  • Genetics

Background:

  • Feed efficiency in sheep is crucial for sustainable livestock production but difficult to measure directly.
  • Omics data, particularly rumen microbiota metabarcoding, has been proposed as a proxy for feed efficiency.
  • Understanding the impact of selection for feed efficiency on rumen microbiota is essential.

Purpose of the Study:

  • To investigate the consequences of divergent selection for residual feed intake (RFI) on rumen prokaryotic and eukaryotic microbiota composition in Romane lambs.
  • To evaluate the predictive ability of rumen microbiota for host feed efficiency and production traits under different dietary conditions.

Main Methods:

  • Analysis of rumen microbiota composition using metabarcoding (16S and 18S rRNA gene sequencing) in 277 Romane lambs from RFI-selected lines.
  • Assessment of discriminant ability of microbiota for RFI lines and correlation with host traits (feed efficiency, production traits).
  • Comparison of predictive accuracy of microbiota data versus traditional measures like fixed effects and body weight (BW).

Main Results:

  • Rumen microbiota composition did not significantly differ between sheep lines selected for RFI; discriminant analyses showed poor discrimination (45-55% error rate).
  • Predictive accuracy of host traits from microbiota data varied (-0.07 to 0.56), with feed intake being the most predictable.
  • Predictions using fixed effects and BW were as accurate or more accurate than those derived from microbiota data.

Conclusions:

  • Metabarcoding the rumen microbiota is not the optimal method for predicting meat sheep production traits.
  • Traditional metrics like fixed effects and BW are more cost-effective and provide comparable or superior predictive accuracy.
  • Environmental and batch effects significantly influence microbiota variability, complicating its use as a predictive tool.