Predicting genetic merit in Harnali sheep using machine learning techniques.

Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuable traits accurately. In this study, we evaluated seven machine learning algorithms viz., K-nearest Network (KNN), Multiple Linear Regression (MLR), Bayesian Regression (BR), Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Gradient Boosting Machine (GBM) for predicting genetic merits of Harnali sheep using pedigree and phenotypic information. The dataset comprised records of 2036 Harnali lambs spanning from 1998 to 2021, with predictors including pedigree records, birth year, sex, weight at lambing, birth weight, weaning weight, average daily gain, and six-month body weight. Breeding values for six-month body weight were estimated using the restricted maximum likelihood method under Wombat software. Machine learning algorithms were trained and tested on a 75% train set and 25% test set, respectively. The algorithms were compared based on goodness of fit criteria including coefficient of determination (R), root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and bias. The results revealed GBM as the top-performing model, achieving an R value of 0.64 and demonstrating superior predictive accuracy (r = 0.80) with lower values of RMSE, MAE, AIC, BIC and bias. This model consistently outperformed others, showcasing its effectiveness in accurately predicting breeding values for Harnali sheep. The study underscores the potential of machine learning algorithms especially GBM in optimizing breeding programs and accelerating genetic progress in Harnali sheep.