Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity quality compounds view climate change conditions. global need for plant-based proteins has increased demand seeds with a that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as standard approach agricultural genetics examining such intricate characters. Recent development machine learning methods shows promising applications dimensionality reduction, which is GWAS. With advancement biotechnology, sequencing, bioinformatics tools, estimation linkage disequilibrium (LD) based associations between genome-wide collection single-nucleotide polymorphisms (SNPs) desired phenotypic traits become accessible. markers from GWAS could be utilized genomic selection (GS) to predict superior lines by calculating estimated breeding values (GEBVs). For prediction accuracy, an assortment statistical models utilized, ridge regression best linear unbiased (rrBLUP), predictor (gBLUP), Bayesian, random forest (RF). Both naturally diverse germplasm panels family-based populations can used mapping on nature system (inbred or outbred) plant species. MAGIC, MCILs, RIAILs, NAM, ROAM being crops. Several modifications doubled haploid NAM (DH-NAM), backcross (BC-NAM), advanced (AB-NAM), also been crops like rice, wheat, maize, barley mustard, etc. reliable marker-trait (MTAs), phenotyping accuracy equally important genotyping. Highthroughput genotyping, phenomics, computational techniques during past few years, making it possible explore enormous datasets. Each population unique virtues flaws at genomics phenomics levels, will covered more detail this review study. current investigation utilizing elite population, optimizing choice selection, size, hurdles phenotyping, analyze competitive legume breeding.