Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms

17 páginas, 4 tablas, 3 figuras

Feed efficiency (FE) is an essential trait in livestock species because of the constant demand to increase the productivity and sustainability of livestock production systems. A better understanding of the biological mechanisms associated with FEs might help improve the estimation and selection of superior animals. In this work, differentially methylated regions (DMRs) were identified via genome-wide bisulfite sequencing (GWBS) by comparing the DNA methylation profiles of milk somatic cells from dairy ewes that were divergent in terms of residual feed intake. The DMRs were identified by comparing divergent groups for residual feed intake (RFI), the feed conversion ratio (FCR), and the consensus between both metrics (Cons). Additionally, the predictive performance of these DMRs and genetic variants mapped within these regions was evaluated via three machine learning (ML) models (xgboost, random forest (RF), and multilayer feedforward artificial neural network (deeplearning)). The average performance of each model was based on the root mean squared error (RMSE) and squared Spearman correlation (rho2). Finally, the best model for each scenario was selected on the basis of the highest ratio between rho2 and RMSE.

This work has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No 772787 (SMARTER). PASF is the beneficiary of a Maria Zambrano Grant of the University of Leon funded by the Ministry of Universities (Madrid, Spain) and financed by the European Union-Next Generation EU.

Peer reviewed