Microbiability and microbiome-wide association analyses of feed efficiency and performance traits in pigs

Abstract

Background The objective of the present study was to investigate how variation in the faecal microbial composition is associated with variation in average daily gain (ADG), backfat thickness (BFT), daily feed intake (DFI), feed conversion ratio (FCR), and residual feed intake (RFI), using data from two experimental pig lines that were divergent for feed efficiency. Estimates of microbiability were obtained by a Bayesian approach using animal mixed models. Microbiome-wide association analyses (MWAS) were conducted by single-operational taxonomic units (OTU) regression and by back-solving solutions of best linear unbiased prediction using a microbiome covariance matrix. In addition, accuracy of microbiome predictions of phenotypes using the microbiome covariance matrix was evaluated. Results Estimates of heritability ranged from 0.31 ± 0.13 for FCR to 0.51 ± 0.10 for BFT. Estimates of microbiability were lower than those of heritability for all traits and were 0.11 ± 0.09 for RFI, 0.20 ± 0.11 for FCR, 0.04 ± 0.03 for DFI, 0.03 ± 0.03 for ADG, and 0.02 ± 0.03 for BFT. Bivariate analyses showed a high microbial correlation of 0.70 ± 0.34 between RFI and FCR. The two approaches used for MWAS showed similar results. Overall, eight OTU with significant or suggestive effects on the five traits were identified. They belonged to the genera and families that are mainly involved in producing short-chain fatty acids and digestive enzymes. Prediction accuracy of phenotypes using a full model including the genetic and microbiota components ranged from 0.60 ± 0.19 to 0.78 ± 0.05. Similar accuracies of predictions of the microbial component were observed using models that did or did not include an additive animal effect, suggesting no interaction with the genetic effect. Conclusions Our results showed substantial associations of the faecal microbiome with feed efficiency related traits but negligible effects with growth traits. Microbiome data incorporated as a covariance matrix can be used to predict phenotypes of animals that do not (yet) have phenotypic information. Connecting breeding environment between training sets and predicted populations could be necessary to obtain reliable microbiome predictions.

Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet

“…Thus, our results confirm that the phenotypic variance of FE and growth traits is better explained by the host additive genetic effects than by the microbiota effects, as already reported in the literature [ 12 , 33 ]. In contrast, the phenotypic variance of the DE traits was better explained by the microbiota effects than the host genetics effects, especially for pigs fed the HF diet.…”

“…0.11 ± 0.09 for RFI and 0.20 ± 0.11 for FCR. Conversely, in the previously cited study [ 33 ], estimates were lower and close to 0 for DFI (0.04 ± 0.03) and ADG (0.03 ± 0.03). Using data from Camarinha-Silva et al [ 12 ], Weishaar et al [ 14 ] estimated a higher for RFI (0.45 ± 0.15) than in our study.…”

“…Similarly, the rank correlations of GEBV between the Gen and Micro+Gen models were close to 1 for the FE traits. To our knowledge, only the study of Aliakbari et al [ 33 ] found non-significantly different and estimates between models that fit the effects jointly and separately. Our study did not suggest microbiota-host confounding for FE traits in pigs.…”

“…Camarinha-Silva et al [ 12 ] and Lu et al [ 13 ] reported estimates of ~ 0.20 for FCR, DFI and growth traits (ADG). Aliakbari et al [ 33 ] found estimates of the same order of magnitude for FE traits in divergent lines of Large White pigs selected on RFI, i.e. 0.11 ± 0.09 for RFI and 0.20 ± 0.11 for FCR.…”

Metagenomic Predictions: A Review 10 years on

“…Subsequently this finding was validated in a much larger cohort of animals ( N = 1702) by Hess et al (2021) . Recently in pigs ( Aliakbari et al, 2022 ) the accuracy of prediction for a number of traits including residual feed intake and back fat depth were shown to increase when both genetic and microbiome information was used in the prediction model.…”