Genome‐Wide Association Studies for Next‐Generation Crop Breeding in the Postgenomic Era

In the postgenomics era, genomics‐assisted crop improvement has gained importance. Identifying closely linked markers to the desired trait is essential to practice indirect selection; association mapping through genome‐wide association studies (GWAS) has emerged as a popular approach to identify such associations owing to its relative advantages over QTL mapping. Understanding the fundamentals of GWAS is critical to reducing the high rate of false positive discoveries and effectively utilizing true associations in crop breeding. The success of GWAS depends on several key factors including population size, marker type and density, model, trait's heritability, population structure and phenotyping. Several empirical studies on GWAS realized impacts on crop improvement are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of linkage disequilibrium, mapping populations (structured and unstructured), modelling marker–trait associations, genetic associations through the statistical framework, mixed linear model, multilocus mixed model, appropriate model selection, machine learning in GWAS, confounding constraints in GWAS, population structure, rare and less frequent alleles, cryptic genetic relatedness, extreme phenotype–GWAS (XPGWAS) and current status of GWAS in crop improvement are reviewed.