Plant Gene and Trait 2024, Vol.15, No.2, 85-96 http://genbreedpublisher.com/index.php/pgt 92 width, and chalkiness (Qiu et al., 2015). The integration of GWAS with genomic selection (GS) models has shown promise in improving the accuracy of breeding value predictions, as demonstrated in a study on elite tropical rice breeding lines (Spindel et al., 2015). 6.2 Future research needs for expanding QTL applications Despite the progress made, several research gaps need to be addressed to fully harness the potential of QTLs in rice breeding. One critical area is the validation and functional characterization of identified QTLs. Many QTLs have been mapped, but their underlying genes and mechanisms remain unknown. For example, a meta-analysis identified 48 meta-QTLs for grain iron and zinc concentration, but further functional studies are needed to understand their roles in micronutrient homeostasis (Raza et al., 2019). Another area of focus should be the development of stable and environment-independent QTLs. The expression of many QTLs is influenced by genetic background and environmental conditions, limiting their utility in breeding programs. Research on multi-parent advanced generation inter-cross (MAGIC) populations has shown that combining GWAS and linkage analysis can identify stable QTLs across different environments (Chen et al., 2022). Expanding such studies to include diverse genetic backgrounds and environmental conditions will enhance the robustness of QTL applications. Additionally, integrating transcriptomic and omics data with QTL mapping can provide deeper insights into the genetic architecture of complex traits. For instance, a study on grain shape and chalkiness traits used transcriptome analysis to identify differentially expressed genes co-located with QTL regions, offering potential candidate genes for functional validation (Chen et al., 2016). 6.3 The role of genomics in predictive breeding Genomics plays a pivotal role in predictive breeding by enabling the identification and utilization of genetic markers associated with desirable traits. The integration of genomic selection (GS) with traditional breeding methods has shown significant promise in improving breeding efficiency. GS models, informed by GWAS, have been used to predict the breeding value of rice lines with high accuracy, outperforming traditional pedigree-based methods (Spindel et al., 2015). The use of meta-QTL analysis further enhances predictive breeding by refining QTL regions and identifying candidate genes. For example, a meta-analysis of QTLs for drought tolerance in rice identified 70 meta-QTLs and several key regulatory proteins involved in drought response (Selamat and Nadarajah, 2021). Such refined QTLs can be incorporated into GS models to improve the prediction of complex traits under varying environmental conditions. Future research should focus on developing comprehensive genomic databases and predictive models that integrate multi-omics data, including genomics, transcriptomics, and proteomics. This holistic approach will enable the identification of key regulatory networks and pathways, facilitating the development of rice varieties with enhanced grain quality and stress tolerance. In conclusion, emerging technologies in genetic mapping, coupled with advancements in genomics, hold great potential for improving rice grain quality. Addressing the current research gaps and integrating multi-omics data into predictive breeding models will pave the way for the development of superior rice varieties, meeting the growing global demand for high-quality rice. 7 Global Impact of Improved Grain Quality 7.1 Enhancing global food security through improved rice varieties Improving rice grain quality has significant implications for global food security. Rice is a staple food for more than half of the world’s population, and enhancing its quality can directly impact nutritional intake and food availability. The identification and utilization of quantitative trait loci (QTLs) for grain yield and quality traits are crucial steps in this direction. For instance, meta-QTL analysis has identified stable and robust QTLs that can be used for marker-assisted selection to improve rice productivity and grain quality (Jin et al., 2023; Aloryi et al.,
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