Plant Gene and Trait 2024, Vol.15, No.5, 230-242 http://genbreedpublisher.com/index.php/pgt 234 4.3 Whole-genome sequencing (WGS) and analysis WGS provides a comprehensive approach to understanding the genetic basis of traits by sequencing the entire genome of an organism. This method identifies all genetic variations, including SNPs, insertions, deletions, and structural variants. In rice, WGS has been used to dissect the genetic basis of grain shape and quality traits. For instance, a study employed WGS to fine-map the gw-5 gene, which controls grain width and length-width ratio, to a 49.7 kb region on chromosome 5 (Wan et al., 2008). Another study used WGS to identify novel QTLs for grain shape, such as qLG2, qWG2, and qLG8 and fine-mapped them to small genomic intervals (Wang et al., 2020). These studies highlight the power of WGS in providing detailed insights into the genetic architecture of complex traits in rice. Jiang et al. (2019) utilized WGS to identify a novel allele, gs9-1, which controls grain shape in rice. Their work highlighted the use of WGS to create segregating populations and gene pools, ultimately revealing genetic variations for traits such as grain length, width, and grain weight. By integrating these methodologies, researchers can gain a deeper understanding of the genetic basis of rice grain shape and palatability, ultimately aiding in the development of rice varieties with improved traits. 5 Key Findings on the Genetic Basis of Grain Shape 5.1 Major genes and QTLs identified Research on the genetic basis of rice grain shape has led to the identification of over 400 QTLs associated with key traits, including grain length (GL), grain width (GW), and grain thickness (GT). Several major genes, such as GS3, GW5, and qSW5, play significant roles in determining these grain characteristics (Huang et al., 2013; Niu et al., 2020). Meng et al.(2022) conducted a GWAS that identified 39 important QTLs related to grain shape traits such as GL, GW, and grain length to width ratio (GLWR) (Figure 2). In addition, novel QTLs such as qTGW3.1, qTGW9, and qTGW11 for grain weight, and qGL4/qRLW4, qGL10, qGL11, and qRLW1 for grain shape were discovered (Niu et al., 2020). GWAS have also mapped several QTLs associated with grain shape, including those located on chromosomes 2, 3, and 6, which have been linked to variations in grain length and width (Wang et al., 2021; Wang et al., 2023). The identification of QTLs like qGL-7a, qGL-8, and qGL-11a further expands the genetic resources available for breeding programs (Kang et al., 2021). Fine mapping efforts have also pinpointed major QTLs such as qGL1.3, which significantly influence grain length and weight (Zhou et al., 2019). 5.2 Functional analysis of grain shape genes Functional analysis of grain shape genes has provided valuable insights into their roles in rice development. The gene GS3 has been identified as a major determinant of grain length, while GW5 influences grain width (Meng et al., 2022). The functional annotation of these genes has revealed their involvement in various biochemical pathways that regulate cell division and expansion in the grain (Meng et al., 2022). GS3 acts as a negative regulator of grain length by inhibiting cell proliferation and elongation in the developing panicle (Fan et al., 2006). Another study identified candidate genes such as Os03g0186600 and Os09g0544400, which are predicted to play roles in grain shape regulation through gene-based association and haplotype analyses (Niu et al., 2020). The gene ORF3 (LOC_Os07g42410) was found to control the ratio of grain length to width, with a single nucleotide deletion causing a frameshift mutation that affects grain shape (Shang et al., 2020). These functional analyses are crucial for understanding the molecular mechanisms underlying grain shape and for developing targeted breeding strategies. 5.3 Comparative genomics of grain shape Comparative genomics has been instrumental in identifying conserved and unique genetic elements influencing grain shape across different rice varieties. Studies have shown that QTLs such as qLWR-12c/qGW-12 are consistently detected across multiple environments, indicating their stable genetic effects (Kang et al., 2021). Comparative analysis of QTLs identified through linkage mapping and GWAS has revealed co-detection of QTLs like qGLE-12-1 and qGLE-12-2, which govern grain length and width (Kang et al., 2020). Additionally, the identification of overlapping QTLs such as qGS7, which is consistent with GL7/GW7, highlights the conserved genetic basis of grain shape traits (Chen et al., 2021). These comparative genomics approaches not only provide valuable insights into the evolutionary conservation and divergence of grain shape genes but also facilitate the transfer of beneficial traits across different rice cultivars. The integration of genomic data from various sources
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