## 3 Discussion  
The majority of modern-day mega/popular rice cultivars are low in GPC and other essential micronutrients [29; 30]. The GPC of milled rice is about 7.0%, whereas brown rice contains 8.0-10.0%. Recent reports have indicated a wide variation for GPC in rice germplasm [30; 31]. This existing variability can be exploited for the identification of QTLs, and these QTLs could be utilized through marker-assisted back cross (MABC) breeding to improve high-yielding mega varieties like BPT 5204 (or any other), which have high market acceptability. Both GPC and grain quality can be improved through MABC. The global burden of PEM in 2019 was approximately 148.0 million, \(\sim\)100.0 million in Asia and 29.0 million in Africa [32]. Interestingly, rice is the major staple diet in these two regions. Hence, any efforts to address the issue of PEM specifically in these regions should include the improvement of GPC along with other nutrients in milled rice. Conventional breeding efforts have had limited success in improving GPC due to its complex nature coupled with the influence of environment. Therefore, exploiting the genetic basis of GPC, coupled with MABC, can aid in developing high-GPC varieties with desirable qualities. Several studies have identified genomic regions affecting GPC, but most of these are for brown rice. Therefore, the present study focused on identifying QTLs for GPC in milled rice, grain quality, and other traits. BPT 5204 is one of the most popular mega varieties of India. The consumers prefer it due to its excellent eating and cooking quality along with high yield, desirable grain quality, and stable performance across different ecosystems. However, BPT 5204 has a low GPC (7.0-8.0%). In the present study, JAK-686, a high-GPC line (12.5% in milled rice), is the donor parent for GPC. We used the F\({}_{2}\) population developed from BPT5204 X JAK-686 for identifying genomic regions associated with GPC. Correlation analysis revealed interesting results among the quality traits. AC exhibited a significant negative correlation with GC, indicating that the simultaneous improvement of AC and GC is not possible [33]. However, [PERSON] et al. (2021) [29] reported a positive correlation between AC and GC. Interestingly, PC did not exhibit any significant association with any of the grain quality traits [28]. [PERSON] et al. (2016) [30] and [PERSON] et al. (2021) [34] reported a positive correlation of KL with yield, and a significant positive correlation with TSW, demonstrating that these traits can be selected and improved simultaneously. GYPP showed a significant positive correlation with yield-related traits like TGW, GPP, PL, and PTPP [34; 35; 36]. TGW, DFF, and AC exhibited high heritability coupled with moderate GAM. Heritability greater than the genetic advance indicates the influence of environment [37]. We can improve such traits by intermating superior genotypes in the segregating population developed by combination breeding [38]. PH, PTPP, PL, GPP, GYPP, and GC exhibited high heritability and high GAM. [PERSON] et al. (2007) [39] also observed similar results. Since additive gene action largely influences these traits, the potential for their improvement is through direct selection. Accordingly, selection based on these traits is more reliable. GPC exhibited moderate heritability and GAM. This study is consistent with the results of [PERSON] et al. (2017) [40], suggesting a moderate heritability of protein with higher variation in the population compared to that in the parents due to environmental interaction. Low heritability and GAM were observed for KL, KB, and L:B. These results agree with those reported in other studies [41; 42; 43]. High heritability coupled with high GAM indicates that most of the heritability is due to additive gene action, making selection potentially effective. Hence, among the thirteen traits studied, selection is effective for PH, PTPP, PL, GPP, GYPP, and GC.