Would large reference populations unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

Abstract Background: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application. Nevertheless, the relatively small datasets used in previous studies, most with fewer than 5,000 observations may have precluded the full potential of DNN. Therefore, the objective of this study was to investigate the impact of the size of the reference population on the performance of DNN compared to Bayesian regression models for genome-enable prediction of body weight in broilers. Results: Predictive performance of DNN improved as sample size increased, reaching a plateau at about 0.32 of prediction correlation when 60% of the entire training set size was used. Interestingly, DNN showed superior prediction correlation with smaller sample sizes and poorer prediction correlation with larger samples sizes compared to Bayesian Ridge Regression (BRR) and Bayes Cπ without including the tuning data in the training data. Conversely, Bayesian models fitted with the training and tuning sets showed the best performance in terms of prediction correlation, but such advantage vanished for larger sample sizes. DNN presented the lowest mean square error of prediction regardless the amount of data used to train the predictive approaches, as well as with Bayesian models including or not the tuning set into the training set. The predictive bias was lower for DNN compared to Bayesian models regardless the amount of data used with estimates closed to the unit with larger sample sizes. Conclusions: DNN had worse prediction correlation compared to BRR and Bayes Cπ, but improved mean square error of prediction and bias relative to both Bayesian models for genome-enabled prediction of body weight in broilers. Such findings, highlights advantages and disadvantages between predictive approaches depending on the criterion used for comparison. Nonetheless, further analysis is necessary to detect scenarios where DNN can clearly outperform Bayesian benchmark models.