PT  - JOURNAL ARTICLE
AU  - Christian Maltecca
AU  - Duc Lu
AU  - Costantino Schillebeeckx
AU  - Nathan P McNulty
AU  - Clint Schwab
AU  - Caleb Schull
AU  - Francesco Tiezzi
TI  - Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms
AID  - 10.1101/363309
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 363309
4099  - http://biorxiv.org/content/early/2018/07/06/363309.short
4100  - http://biorxiv.org/content/early/2018/07/06/363309.full
AB  - In this paper, we evaluated the power of metagenome 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 metagenomic information taken at week 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 metagenomic 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 microbiome on feed efficiency as well as carcass and meat quality should be investigated.