Cell Biology Symposium: molecular basis for feed efficiency

Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi-Tissue Transcriptome Data

Machine learning (ML) methods have shown promising results in identifying genes when applied to large transcriptome datasets. However, no attempt has been made to compare the performance of combining different ML methods together in the prediction of high feed efficiency (HFE) and low feed efficiency (LFE) animals. In this study, using RNA sequencing data of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle, and pituitary) from nine HFE and nine LFE Nellore bulls, we evaluated the prediction accuracies of five analytical methods in classifying FE animals. These included two conventional methods for differential gene expression (DGE) analysis (t-test and edgeR) as benchmarks, and three ML methods: Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and combination of both RF and XGBoost (RX). Utility of a subset of candidate genes selected from each method for classification of FE animals was assessed by support vector machine (SVM). Among all methods, the smallest subsets of genes (117) identified by RX outperformed those chosen by t-test, edgeR, RF, or XGBoost in classification accuracy of animals. Gene co-expression network analysis confirmed the interactivity existing among these genes and their relevance within the network related to their prediction ranking based on ML. The results demonstrate a great potential for applying a combination of ML methods to large transcriptome datasets to identify biologically important genes for accurately classifying FE animals.

Improving efficiency in meat production

Selective breeding and improved nutritional management over the past 20–30 years has resulted in dramatic improvements in growth efficiency for pigs and poultry, particularly lean tissue growth. However, this has been achieved using high-quality feed ingredients, such as wheat and soya that are also used for human consumption and more recently biofuels production. Ruminants on the other hand are less efficient, but are normally fed poorer quality ingredients that cannot be digested by human subjects, such as grass or silage. The challenges therefore are to: (i) maintain the current efficiency of growth of pigs and poultry, but using more ingredients not needed to feed the increasing human population or for the production of biofuels; (ii) improve the efficiency of growth in ruminants; (iii) at the same time produce animal products (meat, milk and eggs) of equal or improved quality. This review will describe the use of: (a) enzyme additives for animal feeds, to improve feed digestibility; (b) known growth promoting agents, such as growth hormone, β-agonists and anabolic steroids, currently banned in the European Union but used in other parts of the world; (c) recent transcriptomic studies into molecular mechanisms for improved growth efficiency via low residual feed intake. In doing so, the use of genetic manipulation in animals will also be discussed.