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

BACKGROUND

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.

RESULTS

In total, 12,257, 9,328, and 6,723 genes were annotated for DMRs detected in the RFI, FCR, and Cons groups, respectively. These genes are associated with important pathways for regulating FE in dairy sheep, such as protein digestion and absorption, hormone synthesis and secretion, control of energy availability, cellular signaling, and feed behavior pathways. With respect to the ML predictions, the smallest mean RMSE (0.17) was obtained using RF, which was used to predict RFI. The highest mean rho (0.20) was obtained when the RFI was predicted via the mean methylation within the DMRs identified, the consensus groups were compared, and the genetic variants mapped within these DMRs were included. The best overall models were obtained for the prediction of RFI using the DMRs obtained in the comparison of RFI groups (RMSE = 0.10, rho = 0.86) using xgboost and the DMRs plus the genetic variants identified via the Cons groups (RMSE = 0.07, rho = 0.62) using RF.

CONCLUSIONS

The results provide new insights into the biological mechanisms associated with FE and the control of these processes through epigenetic mechanisms. Additionally, the potential use of epigenetic information as a biomarker for the prediction of FE can be suggested based on the obtained results.