Plant Gene and Trait 2024, Vol.15, No.2, 85-96 http://genbreedpublisher.com/index.php/pgt 88 biofortification (Raza et al., 2019). These advances highlight the importance of integrating various genetic mapping techniques to enhance the accuracy and utility of QTL identification in rice breeding. Figure 2 Box plots of five rice grain shape and appearance quality traits in two environments and years (Adopted from Chen et al., 2022) Image caption: GL, Grain length; GW, Grain width; GLWR, Grain length to width ratio; DEC, Degree of endosperm chalkiness; PGWC, Percentage of grains with chalkiness. Light pink, light yellow, light blue and cyan colors indicate 2015 in PX, 2015 in SZ, 2016 in PX and 2016 in SZ, respectively (Adopted from Chen et al., 2022) 3.2 Integration of genomic tools in QTL mapping: from lab to field The integration of genomic tools has revolutionized QTL mapping, making it more efficient and applicable to field conditions. Techniques such as QTL-seq, which combines whole-genome resequencing with bulked segregant analysis, have enabled the rapid identification of QTLs in rice. This method has been successfully applied to identify QTLs for traits like partial resistance to rice blast disease and seedling vigor, demonstrating its potential for accelerating the breeding process (Takagi et al., 2013). Additionally, the use of recombinant inbred lines (RILs) and backcross inbred lines (BILs) has facilitated the validation and fine-mapping of QTLs under field conditions. For example, a study using RILs derived from a cross between two Iranian rice cultivars identified seven QTLs associated with grain appearance and quality traits, which were validated using polymorphic SSR markers. Another study fine-mapped two QTLs for grain size within a 460-kb region on chromosome 1, providing valuable targets for marker-assisted selection (Dong et al., 2018). These genomic tools not only enhance the precision of QTL mapping but also bridge the gap between laboratory research and practical field applications, thereby accelerating the development of high-quality rice varieties. 3.3 Challenges in accurately mapping QTLs for grain quality Despite the advancements in QTL mapping techniques, several challenges remain in accurately mapping QTLs for grain quality traits. One major challenge is the genetic background and environmental dependency of QTL expression. Many QTLs identified in controlled environments fail to express consistently under field conditions due to interactions with other genetic factors and environmental variables. For instance, a study on grain appearance quality in rice found that genetic background significantly affected QTL mapping results, with different QTLs being identified in different populations and environments (Chen et al., 2022). Another challenge is the detection of minor-effect QTLs, which often require large populations and high-density genetic maps for accurate identification. A study on grain shape in high-yielding rice identified 91 medium/minor-effect QTLs, highlighting the complexity of genetic control over grain quality traits. Additionally, the presence of linked QTLs within small genomic regions can complicate the fine-mapping process, as seen in the dissection of two QTLs for grain size within a 460-kb region on chromosome 1 (Dong et al., 2018).
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