Genomic selection has been considered as a novel methodology beyond traditional marker assisted selection methods (MAS). GS can be considered as a variant of MAS selecting favourable individuals largely based on estimated breeding values derived genomically. It involves genotyping markers and phenotyping individuals in reference population then predicting phenotypes of candidates for selection using statistical machine learning models. New candidate individuals get predictions performed on them post trained model output if genotypic information happens to be available somehow. Selection of training population proves highly crucial for testing purposes and ultimately determines accuracy in genomic selection processes. Genomic selection models frequently utilize involve stepwise regression, ridge regression, genomic best linear unbiased prediction, Ridge regression best linear unbiased Prediction, Bayes A, Bayes B, Bayes care Bayesian model and least absolute shrinkage selection operator. This review aims to present an overview of genomic selection as an advanced breeding strategy that integrates genome wide markers and statistical model to accelerate genetic improvement in plants. It highlights the principles, methods and applications of genomic selection in enhancing crop traits and breeding efficiency.
