“…High-confidence MTAs identified through GWAS serve as potential targets for extracting CGs associated with the trait of interest. Several studies utilized GWAS-identified MTAs to extract the potential CGs associated with: (i) agronomic traits including days to anthesis, days to maturity, tiller number, spike length, spikelet number, grain number per spike, grain weight, and grain yield ( Gill et al., 2022 ; Gudi et al., 2024 ); (ii) physiological traits such as chlorophyll fluorescence, chlorophyll content, vegetation index, gas exchange, and stomatal conductance ( Hamdani et al., 2019 ; Gudi et al., 2023 ); (iii) stress tolerance such as drought, heat, salinity, etc ( Tanin et al., 2022 , Tanin et al., 2023 ; Tian et al., 2023 ); (iv) biochemical compounds such as proline, abscisic acid, and hydrogen peroxides (H 2 O 2 ) ( Verslues et al., 2014 ; Kamruzzaman et al., 2022 ); and (v) quality traits including grain protein content, sedimentation volume, kernel hardiness, solvent retention capacity, Fe content, and Zn content ( Gudi et al., 2022b ; Halladakeri et al., 2023 ). Similarly, in the present study we used 13 high-confidence MTAs explaining >10% phenotypic variance and having the LOD scores >5 to extract 216 CGs models.…”