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

Selecting feed-efficient sheep could improve the sustainability of this livestock production. However, most sheep breeding companies cannot afford to record feed intake to select feed-efficient animals. Past studies underlined the potential of omics data, including microbiota metabarcoding data, as proxies for feed efficiency. The study involved 277 Romane lambs from two lines divergently selected for residual feed intake (RFI). There were two objectives: check the consequences of selecting for feed efficiency over the rumen microbiota, and assess the predictive ability of the rumen microbiota for host traits. The study assessed two contrasting diets (concentrate diet and mixed diet) and two microbial groups (prokaryotes and eukaryotes). Discriminant analyses did not highlight any significant effect of sheep selection for residual feed intake on the rumen microbiota composition. Indeed, prokaryotic and eukaryotic microbiota compositions poorly discriminated the RFI lines, with averaged balanced error rates ranging from 45% to 55%. Correlations between host traits (feed efficiency and production traits) and their predictions from microbiota data varied between -0.07 and 0.56, depending on the trait, diet and sequencing. Feed intake was the most accurately predicted trait. However, predictions from fixed effects and BW were more accurate than or as accurate as predictions from the microbiota. Environmental effects can greatly affect the variability of microbiota compositions. Considering batch and environmental effects should be paramount when the predictive ability of the microbiota is assessed. This study argues why metabarcoding the rumen microbiota is not the best way to predict meat sheep production traits: fixed effects and BW were more cost-effective proxies and they led to similar or better predictive accuracies than microbiota metabarcoding (16S and 18S sequencing).