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

Abstract

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.

Keywords: Bos indicus; Extreme Gradient Boosting; RNA-seq; Random Forest; co-expression network; residual feed intake; supporting vector machine.

FIGURE 1

Heatmap of cluster analysis using DGE identified by the RX in muscle (A) and pituitary gland (B). H refers to HFE bulls and L refers to LFE bulls.

FIGURE 2

Co-expression network of predictor genes identified by the RX in adrenal gland (A), hypothalamus (B), liver (C), muscle (D), and pituitary (E). Deep color (blue) and bigger circles indicate the genes with stronger control power (higher betweenness value) over the network.

FIGURE 3

Co-expression networks in LFE (A) and HFE (B), colors are relative to the tissue of maximum expression: yellow represents liver, green represents muscle, orange represents pituitary, purple represents hypothalamus, and blue represents adrenal gland. The results are based on the genes selected by the RX.

FIGURE 4

Connections of each shared gene in LFE and HFE. The results are based on the genes selected by the RX.