Molecular Plant Breeding 2024, Vol.15, No.5, 295-307 http://genbreedpublisher.com/index.php/mpb 296 genomics technologies with crop physiology, haplotype analysis can facilitate high-throughput identification of the genetic architecture of adaptive traits, making it a powerful tool for precision breeding (Varshney et al., 2021a). This study aims to explore the application of haplotype analysis in creating high-yielding rice varieties. We focus on pinpointing optimal haplotype combinations within key genes that affect grain yield and quality, assessing their impact on global rice production, and examining the viability of using this approach to develop new, customized rice varieties that satisfy future nutritional and food demands. We seek to enhance precision breeding methods and respond to the critical need for more reliable and sustainable rice production. 2 Genetic Basis of Rice Yield 2.1 Understanding genetic factors influencing yield traits Rice yield is a complex trait influenced by multiple genetic factors. The primary components determining rice yield include the number of grain number per panicle (GNP), thousand-grain weight (TGW), and effective panicle number per plant (ENP). These traits are controlled by numerous minor-effect loci, making it challenging to identify specific yield-related genes directly (Su et al., 2021). Quantitative trait loci (QTLs) play a significant role in controlling these yield components, and their effects can vary depending on the genetic background of the rice variety. For instance, editing QTLs using CRISPR/Cas9 has shown that the same QTL can have diverse effects on grain yield in different rice varieties (Shen et al., 2018). Additionally, genetic interactions, such as epistatic interactions, contribute significantly to yield-related traits, further complicating the genetic architecture of rice yield (Li et al., 2022; Wei et al., 2024). 2.2 Role of genetic diversity in yield optimization Genetic diversity is crucial for optimizing rice yield. Studies have shown significant genetic variation among different rice genotypes for various yield-related traits, such as plant height, effective panicle number per plant, flag leaf area, grain number and grain size (Nath and Kole, 2021; Cao et al., 2023; Liu et al., 2023; Wang et al., 2023). This diversity allows for the selection of superior alleles that can enhance yield under different environmental conditions. For example, haplotype analysis of key genes governing grain yield and quality traits has revealed superior haplotypes that can be used to develop tailor-made rice varieties with enhanced genetic gains (Abbai et al., 2019). Moreover, understanding the genetic basis of correlations among growth duration and yield traits can help in developing crop varieties that efficiently use light and energy resources, thereby producing high yields (Li et al., 2018). 2.3 Overview of haplotype analysis and its relevance to genetics Haplotype analysis is a powerful tool for understanding the genetic basis of complex traits like rice yield. It involves examining combinations of alleles at multiple loci to identify superior haplotypes that contribute to desirable traits. In rice, haplotype analysis has been used to identify superior haplotypes for genes influencing grain yield and quality traits (Abbai et al., 2019; Sivabharathi et al, 2024). This approach allows for the development of next-generation rice varieties with tailored genetic profiles that meet specific breeding goals. For instance, haplotype analysis of 120 functionally characterized genes revealed significant variations in the 3K rice genome panel, providing insights into the genetic basis of yield and quality traits. By leveraging haplotype-based breeding, it is possible to combine superior alleles from different genes to create high-yielding rice varieties that are better suited to future food and nutritional demands (Abbai et al., 2019; Liu et al., 2023). 3 Haplotype Analysis Techniques 3.1 Methods for identifying and analyzing haplotypes in rice Haplotype analysis in rice involves the identification and characterization of specific combinations of alleles at multiple loci that are inherited together. Traditional methods rely on short-read sequencing technologies, which require computational assembly of shorter haplotype fragments due to the limited length of reads. This process can be challenging due to high variability and repeat sequences in the genome (Garg, 2021). Recent advancements have introduced long-read sequencing technologies, such as those provided by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), which allow for more direct and accurate haplotype phasing (Maestri et
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