Molecular Plant Breeding 2025, Vol.16, No.5, 268-277 http://genbreedpublisher.com/index.php/mpb 272 the combination of superior haplotype aggregation and hybrid rice breeding can simultaneously improve multiple traits such as yield and disease resistance, providing a reliable genomic basis for the continuous innovation of hybrid rice (Sinha et al., 2020). 6 Case Study: Haplotype-Based Improvement of Yield Traits 6.1 Choice of target yield traits and rice populations Traits such as grain weight, the number of grains per panicle, panicle length, plant height, flag leaf size and biomass are the main improvement targets of rice haplotype breeding. These traits not only directly affect the yield, but are also closely related to stress resistance and adaptability. Studies usually employ populations with diverse genetic backgrounds, including cultivated rice (indica rice, japonica rice), wild rice (O. rufipogon, O. glaberrima), and their backcross derivative lines. This can maximize the exploration and utilization of beneficial haplotypes (Ashfaq et al., 2023; Bharamappanavara et al., 2023; Udaya et al., 2023). For instance, some studies analyzed the effects of GRF4 haplotypes on yield and biomass using 335 rice samples, or systematically evaluated the contributions of different haplotypes to yield traits by constructing an introduction line library containing multiple AA genomic germplasm (Zhang et al., 2022; Sahoo et al., 2024). 6.2 Experimental workflow: sequencing, haplotype analysis, and candidate selection The experimental process generally includes high-throughput genomic sequencing, SNP typing, haplotype construction, association study (GWAS or QTL mapping), and candidate haplotype screening. Firstly, conduct genome-wide SNP testing on the target population and perform association analysis in combination with phenotypic data to identify haplotypes or QTLS that are significantly associated with yield traits. For instance, GWAS could detect multiple major and multi-effect loci related to traits such as grain weight, panicle length, and seed setting rate in 100 to 400 diverse materials (Ashfaq et al., 2023). Then, through molecular marker-assisted selection (MAS) or gene editing techniques (such as CRISPR/Cas9), superior haplotypes were introduced into breeding materials to achieve precise improvement (Sahoo et al., 2024). 6.3 Field performance, yield gains, and scalability of breeding outcomes Haplotype improved materials have demonstrated significant yield increases and trait stability in multi-environment field trials. The superior haplotype of GRF4 (Hap1) can increase yield and biomass. Some QTL polymer lines have yields more than 50% higher than control varieties under drought or high-temperature stress (Withanawasam et al., 2022; Zhang et al., 2022; Sahoo et al., 2024). The introduced lines obtained by introducing the superior haplotypes of wild rice into the cultivated rice background showed high yield and wide adaptability in different genetic backgrounds and ecological regions (Zhang et al., 2022; Bharamappanavara et al., 2023). Haplotype breeding strategies can specifically enhance yield traits and have excellent scalability and application prospects. 7 From Genomic Insights to Field Applications 7.1 Multi-location trials for stability and environmental adaptability Field trials of rice materials at multiple locations and in different seasons can effectively identify haplotype combinations that are high-yielding and stable in various environments. James et al. (2024) utilized backcrossover introduction systems and, through three consecutive seasons of field trials, combined with statistical methods such as AMMI and GGE, screened out materials that performed excellently in various environments, providing a scientific basis for subsequent large-scale promotion. Evaluating the environmental adaptability of superior haplotypes is helpful for identifying genotypes with broad adaptability or specific adaptability and enhancing the application value of new varieties under complex ecological conditions (Bharamappanavara et al., 2023). 7.2 Integration with precision agriculture and digital phenotyping tools Tools such as high-throughput phenotypic platforms, remote sensing technologies and big data analysis can monitor the growth and yield traits of rice in the field in real time and non-destructive, accelerating the screening and evaluation of superior haplotypes (Bhat et al., 2021). Predictive models that combine genomic selection (GS) and machine learning have enhanced the prediction accuracy and breeding decision-making efficiency of complex yield traits by leveraging large-scale phenotypic and genotypic data (Bejjam and Basuthkar, 2024). The
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