Abstract
BackgroundImproving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts.ResultsGene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R2 = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression.ConclusionFrom small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications.
Highlights
Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production
The feed conversion ratio (FCR) averaged 2.85 kg feed/kg body weight (BW), and covered a large range of values (Min = 1.92; Max = 5.00). It was generally lower for pigs of the low residual feed intake (RFI) line than for pigs of the high RFI line, but there was an interpenetration between the two lines within each experiment and between experiments
The random forest (RF) and gradient tree boosting (GTB) procedures were applied to this merged dataset to find the most important transcripts that allow the classification of pigs
Summary
Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. Circulating molecules provide valuable insights into complex phenotypes such as obesity and diabetes [8, 9], health status [10], sensitivity to heat stress [11] and nutrient efficiency for productive outputs [3, 7, 12]. They hold much promise for the identification of biomarkers for particular phenotype prediction [13]. In conditions where small variations in the data may cause significant changes in the prediction, these methods generally overcome complex, noisy and hidden relationships when ranking the most important genes for prediction and avoid the pitfalls of overfitting
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