New perspectives of post-GWAS analyses: From markers to causal genes for more precise crop breeding

Le résumé fourni par la source

Crop breeding advancement is hindered by the imperfection of methods to reveal genes underlying key traits. Genome-wide Association Study (GWAS) is one such method, identifying genomic regions linked to phenotypes. Post-GWAS analyses predict candidate genes and assist in causative mutation (CM) recognition. Here, we assess post-GWAS approaches, address limitations in omics data integration and stress the importance of evaluating associated variants within a broader context of publicly available datasets. Recent advances in bioinformatics tools and genomic strategies for CM identification and allelic variation exploration are reviewed. We discuss the role of markers and marker panel development for more precise breeding. Finally, we highlight the perspectives and challenges of GWAS-based CM prediction for complex quantitative traits. • Causal genes with CMs must be identified to make crop breeding more precise. • There are strategies to overcome GWAS failure in CM identification. • Post-GWAS analyses assist in reducing false positive candidate genes. • Accuracy assists in the selection of highly efficient markers for CM identification. • Publicly available big data in genomics can be leveraged for CM identification.

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