Designing of an Artificial Neural Network model to evaluate the association of three combined Y specific microsatellite loci on the actual and predicted post thaw motility in crossbred bull semen

Abstract

The freezing of bull semen significantly hamper the motility of sperm which reduces the conception rate in dairy cattle. The prediction of postthaw motility (PTM) before freezing will be useful to take the decision on discarding or freezing of the germplasm. The artificial neural network (ANN) methodology found to be useful in prediction and classification problems related to animal science, and hence, the present study was undertaken to compare the efficiency of ANN in prediction of PTM on the basis of the number of ejaculates, volume, and concentration of sperms. The combined effect of Y-specific microsatellite alleles on the actual and predicted PTM was also studied. The results revealed that the prediction accuracy of PTM based on the semen quality parameters was comparatively lower because of higher variability in the data set. The ANN gave better prediction accuracy (34.88%) than the multiple regression analysis models (32.04%). The root mean square error was lower for ANN (8.4353) than that in the multiple regression analysis (8.6168). The haplotype or combined effect of microsatellite alleles on actual and predicted PTM was found to be highly significant (P < 0.01). On the basis of results, it was concluded that the ANN methodology can be used for prediction of PTM in crossbred bulls.