AUTHOR=Sheoran S. , Jaiswal S. , Raghav N. , Sharma R. , Sabhyata , Gaur A. , Jaisri J. , Tandon Gitanjali , Singh S. , Sharma P. , Singh R. , Iquebal M. A. , Angadi U. B. , Gupta A. , Singh G. , Singh G. P. , Rai A. , Kumar D. , Tiwari R. TITLE=Genome-Wide Association Study and Post-genome-Wide Association Study Analysis for Spike Fertility and Yield Related Traits in Bread Wheat JOURNAL=Frontiers in Plant Science VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.820761 DOI=10.3389/fpls.2021.820761 ISSN=1664-462X ABSTRACT=

Spike fertility and associated traits are key factors in deciding the grain yield potential of wheat. Genome-wide association study (GWAS) interwoven with advanced post-GWAS analysis such as a genotype-phenotype network (geno-pheno network) for spike fertility, grain yield, and associated traits allow to identify of novel genomic regions and represents attractive targets for future marker-assisted wheat improvement programs. In this study, GWAS was performed on 200 diverse wheat genotypes using Breeders’ 35K Axiom array that led to the identification of 255 significant marker-trait associations (MTAs) (–log10P ≥ 3) for 15 metric traits phenotyped over three consecutive years. MTAs detected on chromosomes 3A, 3D, 5B, and 6A were most promising for spike fertility, grain yield, and associated traits. Furthermore, the geno-pheno network prioritised 11 significant MTAs that can be utilised as a minimal marker system for improving spike fertility and yield traits. In total, 119 MTAs were linked to 81 candidate genes encoding different types of functional proteins involved in various key pathways that affect the studied traits either way. Twenty-two novel loci were identified in present GWAS, twelve of which overlapped by candidate genes. These results were further validated by the gene expression analysis, Knetminer, and protein modelling. MTAs identified from this study hold promise for improving yield and related traits in wheat for continued genetic gain and in rapidly evolving artificial intelligence (AI) tools to apply in the breeding program.