Plant Gene and Trait 2024, Vol.15, No.2, 85-96 http://genbreedpublisher.com/index.php/pgt 87 and more resilient rice cultivars (Takagi et al., 2013; Yun et al., 2014; Nawaz et al., 2015; Kinoshita et al., 2017; Zhang et al., 2020; Selamat and Nadarajah, 2021). Figure 1 Distribution of specific gene loci on maize chromosomes (Adopted from Kinoshita et al., 2017) Image caption: Chromosomal locations of QTLs for eating quality, grain appearance quality and yield related traits in the RILs derived from the cross between Yukihikari and Joiku462. The chromosome number is shown at the top. Vertical bars denote the linkage maps constructed for the RILs (Kinoshita et al. 2016). Map positions of the QTLs are shown to the right of each chromosome. The length of the vertical bars represents the QTL confidence interval (P < 0.05) and the horizontal bars represent the highest LOD score peak. White and black arrows on the top show that Yukihikari and Joiku462 alleles, respectively, increase the respective traits. Abbreviations: 2014P, 2014 Pippu; 2014S, 2014 Sapporo; 2015P, 2015 Pippu; 2015S, 2015 Sapporo; DTH, days to heading; AAC, apparent amylose content; PC, protein content; BGW, brown grain weight per plant; TBGW, 1000 brown grain weight; BGL, brown grain length; BGWI, brown grain width; BGT, brown grain thickness; GN, grain number per plant; GNP, grain number per panicle; FGN, filled grain number per plant; UFG, unfilled grain ratio; PL, panicle length; PN, panicle number; CL, culm length (Adopted from Kinoshita et al., 2017) In summary, understanding and mapping QTLs are fundamental steps in the genetic improvement of rice. The integration of traditional and advanced QTL mapping techniques, along with the identification of stable QTLs across diverse environments, provides a robust framework for enhancing rice grain quality and yield through targeted breeding strategies. 3 Genetic Mapping Techniques 3.1 Advances in genetic mapping for QTL identification Quantitative Trait Loci (QTL) mapping has significantly advanced our understanding of the genetic determinants of rice grain quality. Traditional linkage analysis and genome-wide association studies (GWAS) have been instrumental in identifying QTLs associated with various grain quality traits. For instance, a study utilizing high-resolution QTL mapping through genotyping-by-sequencing identified 15 QTLs related to grain quality traits such as transparency and chalkiness, with several novel loci being discovered (Jin et al., 2023). Similarly, the use of multi-parent advanced generation inter-cross (MAGIC) populations has enabled the identification of QTLs that are consistent across different genetic backgrounds and environments, thus providing robust targets for breeding programs (Figure 2) (Chen et al., 2022). Moreover, meta-analysis techniques have been employed to refine QTL regions, thereby increasing the precision of QTL mapping. A meta-analysis of QTLs associated with grain iron and zinc content identified 48 meta-QTLs with significantly narrower confidence intervals, facilitating the identification of candidate genes for
RkJQdWJsaXNoZXIy MjQ4ODYzMg==