Unlocking genetic hotspots: GWAS reveals key nitrogen responsive genomic regions and key genes for root and yield traits in indica rice.

Nitrogen (N) induced environmental pollution from rice cultivation has resulted in undesired environmental impacts. To minimize the impacts, improvement of inherent NUE is very crucial in rice as it has the lowest NUE among the cereals. Though many family based and association based QTL studies have been reported earlier on NUE in rice, the reports on indica rice and precise evaluation of root parameters till physiological maturity is lacking. This study reports the identification of candidate genes and QTLs through a genome-wide association study (GWAS) involving 96 diverse indica rice genotypes, grown under contrasting N regimes in hydroponics till maturity. Genotyping was carried out using 80 K Affymetrix chip containing 47,686 curated SNP markers. The differential response of ten different N-responsive traits indicated separate breeding program for each N-regime. The population structure analysis revealed two sub-populations with varying degrees of admixtures in the association panel. The linkage disequilibrium (LD) analysis revealed a LD block of 108.4 kb. GWAS using MLM, FarmCPU, and BLINK could identify 568 marker-trait associations (MTAs) across different traits and N-conditions. Out of 24 common MTAs identified, 13 were novel with 156 candidate genes in the genomic region spanning the LD blocks. Yield and root-related MTAs were found to be the most prominent. N-responsive genes were found to be associated with other abiotic stresses like drought and salinity, as seen from the available literature. Candidate genes (OsWAK15, OsNIN8, OsHCT2, Os02 g0612900, Os02 g0613100, and Os02 g0612700) showed a similar expression pattern under N-stress in both N use- efficient and inefficient genotypes, which can be potential targets for modulating gene expression for N stress tolerance. These MTAs and candidate genes can serve as key resources for enhancement of NUE in rice upon functional validation.