Genomic selection

 

 

    

Climate change has significantly altered the phenology of crop species, impacting their production and productivity through various stresses such as heat, cold, drought, and flood. Traditional breeding has successfully improved crops through phenotypic selection, but recent advancements in genomics have identified key genes and QTLs for stress tolerance, enabling marker-assisted selection (MAS). MAS uses known genetic markers linked to specific traits to select individuals, improving accuracy over traditional methods. Successful MAS applications include varieties like Pusa Basmati in rice and HUW510 in wheat. However, MAS is less effective for polygenic traits controlled by numerous minor genes, which are common in stress tolerance. To address this, genomic selection (GS) has been developed. GS estimates genetic merit using dense markers across the genome, improving the accuracy of selection for complex traits. This approach starts with creating a training population with both genotypic and phenotypic data to build a predictive model, which is then applied to a breeding population with only genotypic data. GS accelerates breeding cycles and reduces phenotyping costs, but its effectiveness depends on factors like population size, genetic diversity, and marker density. Challenges include handling changes in gene frequencies and epistatic interactions, which can affect genomic prediction accuracy. While GS has advanced methods for better prediction, such as parametric and non-parametric models, its implementation remains costly compared to traditional breeding. Advances in both parametric and non-parametric approaches continue to enhance the application of GS in breeding programs.