Polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) method was used to determine the growth hormone (GH), leptin, calpain, and calpastatin polymorphism in Iranian Baluchi male sheep. An artificial neural network (ANN) model was developed to describe average daily gain (ADG) in lambs from input parameters of GH, leptin, calpain, and calpastatin polymorphism, birth weight, and birth type. The fitness of the model was tested using R2, MS error, and bias. The developed ANN-model was used to evaluate the relative importance of each input parameter on lambs ADG using a sensitivity analysis method. Three conformational patterns were detected for GH, Leptin, calpain genes, and five conformational patterns were detected for calpastatin gene. The calculated statistical values corresponding to the ANN-model showed a high accuracy of prediction (R240.90, MS error¼0.0003). The sensitivity analysis on the ANN-model indicated that birth weight and birth type are the most important variables in the growth of lambs, followed by Leptin, calpastatin, GH, and calpain, polymorphism. The optimization analysis on ANN-model for maximizing ADG of lambs revealed that the maximum ADG may be obtained with birth weight 5.2 kg, birth type of single, GH genotype of G2, leptin genotype of L1, calpastatin genotype of C3, and calpain genotype of A3. The results revealed that the ANN-model is an appropriate tool to recognize the patterns of data to predict lamb growth in terms of ADG given specific genes polymorphism, birth weight, and birth type. The platform of PCR-SSCP approach and ANN-based model analyses may be used in molecular marker-assisted selection and breeding programs to design a scheme in enhancing the efficacy of sheep production.

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