Molecular Plant Breeding 2024, Vol.15, No.5, 308-316 http://genbreedpublisher.com/index.php/mpb 309 insights into the practical applications of QTLs, this study aims to contribute to the ongoing efforts to enhance rice yield and quality, thereby supporting global food security and sustainable agricultural practices. 2 QTL Mapping in Rice 2.1 Historical development and milestones in QTL mapping The journey of QTL mapping in rice has seen significant advancements over the past few decades. Initial efforts focused on constructing genetic maps using simple sequence repeats (SSR) and other molecular markers. For instance, a study on recombinant inbred lines (RILs) derived from the popular rice hybrid KRH-2 utilized SSR loci to construct a genetic map spanning 294.2 cM, identifying 22 QTLs related to yield and its associated traits (Kulkarni et al., 2020). The development of high-density genetic maps, such as those created by the Rice Genome Project (RGP), has been pivotal. These maps have facilitated the identification of QTLs for various agronomic traits, including yield, disease resistance, and abiotic stress tolerance. The integration of multiple studies through meta-analysis has further refined our understanding, revealing consensus regions and candidate genes across different rice species (Swamy and Sarla, 2011). 2.2 Techniques and methodologies for QTL mapping QTL mapping methodologies have evolved from traditional linkage analysis to more sophisticated approaches. Early studies relied on phenotyping and genotyping information to identify QTLs, as demonstrated by the identification of major effect QTLs for traits like total grain yield and panicle length using SSR markers (Figure 1) (Kulkarni et al., 2020). The advent of genotyping-by-sequencing (GBS) has significantly enhanced the resolution and precision of QTL mapping. For example, GBS was used to develop high-density linkage maps in rice, identifying stable QTLs for grain yield under drought stress (Figure 2) (Yadav et al., 2019). Another innovative approach, QTL-seq, combines whole-genome resequencing with bulked segregant analysis, allowing rapid identification of QTLs by comparing DNA from populations with extreme trait values (Takagi et al., 2013). Dynamic QTL mapping, which considers the temporal expression of traits, has also been employed to study protein content and index in rice, revealing stage-specific QTLs (Zheng et al., 2011). 2.3 Tools and software for QTL mapping The advancement of QTL mapping has been supported by the development of various tools and software. High-density genetic maps and consensus maps have been instrumental in QTL identification and meta-analysis (Swamy and Sarla, 2011). Software like QuLine has been used to simulate breeding strategies and demonstrate the application of identified QTLs in rice quality improvement (Wang et al., 2007). The integration of QTL mapping with functional genomics, as seen in the construction of rice function maps, has provided deeper insights into the genetic control of agronomic traits (Ishimaru et al., 2001). These tools have not only facilitated the identification and validation of QTLs but also enabled their application in marker-assisted selection (MAS) and breeding programs. 3 Key QTLs for Rice Yield 3.1 Major QTLs associated with yield components Quantitative Trait Loci (QTL) mapping has identified several major QTLs associated with yield components in rice. For instance, a study using an F2 and F3 population derived from an indica-indica cross identified 44 QTLs across nine chromosomes, including QTLs for the number of panicles, number of filled grains, total number of spikelets, spikelet fertility, 1 000-grain weight, grain weight per plant, plant height, and panicle length. Another study using a recombinant inbred line (RIL) population derived from the rice hybrid KRH-2 identified 22 QTLs, with major QTLs for total grain yield per plant (qYLD3-1), panicle weight (qPW3-1), plant height (qPH12-1), flag leaf width (qFLW4-1), and panicle length (qPL3-1) (Kulkarni et al., 2020). 3.2 Mechanisms of action The mechanisms by which these QTLs influence yield components often involve complex genetic interactions and environmental factors. For example, the QTLs identified in the F2 and F3 populations showed significant QTL×environment interactions, suggesting that the expression of these QTLs can be influenced by environmental conditions. Similarly, the QTLs identified in the RIL population also exhibited significant
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