Gene editing and GWAS for digital imaging analysis of wheat grain weight, size and shape are inevitable to enhance the yield

Abstract

The productivity of wheat plays a primary role in ensuring global food and nutritional security. Thousand kernel weight (TKW) and grain morphology are the notable determinants in comprehending the final output. The advancements in grain phenotyping techniques such as digital image analysis (DIA) of grain morphometric data for the identification of genomic regions by genome-wide association studies (GWAS) and the use of genome editing (CRISPR) application on identified genomic regions for higher seed size, shape offer an unprecedented approach to improve the wheat yield. In this review, we discussed the significance of DI analysis for grain morphometric traits and GWAS in the characterization of genetic regions such as QTLs or MTAs for TKW, grain size and shape. We concluded based on reports by researchers who have successfully integrated DI analysis and GWAS. We have also discussed how modern machine learning (ML) algorithms like support vector machines (SVM), random forest (RF), artificial neural network (ANN), etc., could be helpful in the prediction of TKW from grain morphometric parameters. Additionally, we synthesize the use of CRISPR in the improvement of TKW by mutating the genes controlling these traits. Lastly, we discussed the implications of the combined use of genome editing, GWAS and ML to enhance yield through targeted traits such as TKW and grain morphology. By implementing these combined methodologies to enhance the grain weight, which could be a more stable goal for yield improvement in wheat breeding aimed at multiplying wheat yield to meet the increasing food demand of global population.