Molecular Plant Breeding 2024, Vol.15, No.5, 295-307 http://genbreedpublisher.com/index.php/mpb 297 al., 2020; Fruzangohar et al., 2022). Additionally, targeted capture sequencing has been employed to analyze specific genes associated with important agronomic traits, such as grain size and weight, in rice. This method has successfully identified superior haplotypes and haplotype combinations that can be used in breeding programs (Liu et al., 2023). 3.2 Recent advances in genomic sequencing technologies The field of genomic sequencing has seen significant advancements with the development of third-generation sequencing technologies. These technologies, such as ONT and PacBio, produce long reads that span larger genomic regions, facilitating direct haplotype phasing and reducing the reliance on statistical methods. Long-read sequencing has proven particularly useful in clinical settings for variant calling and phasing, enabling the analysis of complex genomic regions with high accuracy (Maestri et al., 2020). Additionally, linked-read sequencing techniques, such as haplotagging, have been developed to retain haplotype information while sequencing large populations, providing a cost-effective and efficient approach for whole-genome haplotyping (Meier et al., 2020; Kolesnikov et al., 2024). These advancements have significantly improved the resolution and accuracy of haplotype analysis, making it more accessible for various applications, including rice breeding. 3.3 Integration of bioinformatics tools in haplotype analysis The integration of bioinformatics tools is crucial for the accurate assembly and analysis of haplotypes from sequencing data. Several algorithms and software tools have been developed to address the challenges of haplotype reconstruction (Sivabharathi et al, 2024). For instance, established statistical model of Quantitative Trait mapping to Haplotype (lcQTH) using low depth sequencing data for mapping population haplotype traceability, significantly reduce the cost of single sample genotyping, and the effective number significantly increased, marker density in the genome distribution, improve the efficiency of haplotype excavation, provides a new method for molecular design breeding. HaploMaker is a reference-based haplotype assembly algorithm that uses paired-end short reads and longer PacBio reads to phase long haplotypes with high accuracy (Fruzangohar et al., 2022). Another tool, Ranbow, is designed for polyploid genomes and integrates various types of small variants to reconstruct haplotypes efficiently (Moeinzadeh et al, 2020). Additionally, PERHAPS is a novel approach that directly calls haplotypes from short-read, paired-end sequencing data, demonstrating high concordance rates with traditional methods (Huang et al., 2020). These bioinformatics tools enhance the ability to analyze complex genomic data, facilitating the identification of superior haplotypes for breeding applications in rice. 4 Case Studies: Haplotype Analysis in Action 4.1 Examples of successful haplotype analysis in rice Haplotype analysis has been instrumental in identifying superior genetic combinations that enhance grain yield and quality in rice. A comprehensive study analyzed 120 functionally characterized genes across the 3K rice genome panel, focusing on 87 genes related to grain yield and 33 genes associated with grain quality. This analysis revealed significant variations and identified superior haplotypes such as SD1-H8, MOC1-H9, and IPA1-H14, among others. These haplotypes were associated with improved traits like early flowering, medium duration flowering, and bold grains, demonstrating the potential for developing tailor-made rice varieties with enhanced genetic gains (Figure 1) (Abbai et al., 2019). The candidate gene-based association analysis targeting 42-grain size- and genes in a panel of 180 rice accessions was conducted based on targeted capture sequencing. Among the 42 genes, 69 SNPs/Indels were detected to be associated with grain length (GL), rain width (GW), ratio of grain length-width (L/W) and thousand grain weight (TGW). Superior haplotypes and haplotype combinations for the seven genes were also identified. Furthermore, used the haplotype-specific marker panel for the superior haplotype combination TGW-HC4, it was analyzed in Minghui63 and some widely used restorer lines derived from Minghui63, as well as our breeding varieties and lines. The results showed that the TGW-HC4 is indeed an excellent haplotype combination of TGW, and it could be utilized in rice breeding. Haplotype-based breeding (HBB) provides selection targets for genomics-assisted breeding and contributes to the future development of rice varieties with high yield potential and high quality through this strategy (Figure 2) (Liu et al., 2023).
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