Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine

Abstract

Background: The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. 'Full' included information from all genetic markers and all operational taxonomic units (OTU), while 'informatively reduced' and 'randomly reduced' represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction. Results: Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-bymicrobiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly. Conclusions: This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.

Feed and Host Genetics Drive Microbiome Diversity with Resultant Consequences for Production Traits in Mass-Reared Black Soldier Fly (Hermetia illucens) Larvae

“…Further research in humans has established that body mass index and blood lipid levels may be substantially influenced by microbiome compositionality, irrespective of the confounding effects of age, sex, and individual genotype [102]. These findings are recapitulated in swine models, where microbiota profiles have strong predictive potential for various mass-related traits, including feeding efficiency and final carcass weight [84,103]. Given that the animal GIT microbiome itself behaves as a quantitative phenotype, Camarinha-Silva et al [84] postulated that there is a precedent for accelerated improvement of valuable but complex livestock production traits via tandem, selective optimisation of host genotype, and microbiota.…”

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

“…Overall, these studies highlight the relevance of variability in the GIT microbiome composition associated with variability in performance traits, which suggests the possibility of predicting future phenotypes based on predicted microbial values ( ) [ 7 ]. However, in livestock, only a few studies have evaluated the accuracy of phenotype predictions by including microbiota effects in linear mixed models [ 7 , 8 ]. In addition, similar to genome-wide association studies, microbiome components can be considered as potential markers of the selected complex traits, and their associations can be identified through microbiome-wide association studies (MWAS) [ 10 ].…”

Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs

“…As such, partial non-redundant results of the present research on TE have been published earlier. We have focused our previous studies on the inclusion of microbial information in predictive models for selection purposes through microbial covariance matrices [ 48 ]. Here, we significantly extend these results by providing a comprehensive ecological comparison of nucleus versus terminal systems, meanwhile essentially doubling the sample size of the analysis.…”

“…The phenotypes used in the association analysis for TE were the same of Khanal et al [ 47 , 48 ]. Briefly, carcass quality traits included measures of body growth and tissue deposition taken at harvest (TP3), such as carcass average daily gain ( cADG ) as the eviscerated body weight accumulated from birth to harvest; loin depth ( cLD ) as the depth of the loin muscle; back-fat depth ( cBF ) as the depth of the fat layer in correspondence of the 10 th thoracic vertebra; ham yield ( cHAM ), loin yield ( cLOI ), belly yield ( cYEL ) as the proportion of the ham, loin and belly cuts on carcass weight, respectively.…”