Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms

Abstract

In this paper, we evaluated the power of microbiome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of microbiome information taken at weeks 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting microbiome information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of the microbiome on feed efficiency as well as carcass and meat quality should be investigated.

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

“…In this study, predictive ability increased and MSE decreased for most of the models that included microbiome information. These results agree with those of Maltecca et al [18] and Lu et al [20], which suggest that the microbiome can be used as a biomarker to predict phenotype for growth and carcass traits in swine. Our results also show that the predictive ability is generally greater when microbiome data collected at Off-test is included compared to earlier growth stages, although information collected at earlier stages would prove more useful in selection and management.…”

“…In pigs, prediction of phenotypic performance of individuals is now routinely performed with the inclusion of genomic information but, to date, the advantage of including microbiome information has not been fully assessed. Recent studies [19][20][21] have reported estimates of the accuracy of microbial predictions for pig traits, but studies that include both genomic and microbiome information for the prediction of phenotypes are scarce. In particular, the effect of including microbial and hostmicrobiome interactions on the prediction of meat quality and carcass phenotypes has not been studied on a large scale and at multiple stages of the production life of the pig.…”

Predictive ability of host genetics and rumen microbiome for subclinical ketosis

“…Similarly, in a larger study in pigs (n = 207), Camarinha-Silva et al (2017) used fecal microbiota based on 16S rRNA gene sequencing to predict feed intake, daily gain, and feed conversion ratio, and found the accuracies of prediction based on microbial relationships (0.33 to 0.41) to be higher than those based on genomic relationships (0.20 to 0.35) and to have lower standard errors. Maltecca et al (2019) used Bayesian and machine-learning prediction models to predict fat and average daily gain using fecal microbial profiles in 1,043 pigs and achieved accuracies in the range of 0.40 to 0.50. More recently, the RuminOmics Consortium used microbial relationship matrices and 16S rRNA gene profiling to predict numerous phenotypes in diverse European dairy cattle breeds and found prediction accuracies for blood BHB concentration to range from 0.33 in Finnish Aryshire (n = 100) to 0.47 in UK Holsteins (n = 297; Wallace et al, 2019).…”

Opportunities and limits of combining microbiome and genome data for complex trait prediction

“…While individuals can be genotyped at birth, the microbiome in early life is not representative of adult or later stages. Maltecca et al, for instance, show that early life microbiota is not a good proxy for carcass composition in pigs [35].…”