1 Introduction

The core of haplotype breeding is to identify and utilize good allele combinations. This can integrate the key genes related to yield together, improving the efficiency of breeding. Studies have found that haplotype combinations of different genotypes, especially functional haplotypes of core regulatory genes such as Ghd7, DTH8, and PRR37, can significantly improve traits such as grain count per panicle and 1000-grain weight of rice, thereby achieving high and stable yields (Sun et al., 2023; Sachdeva et al., 2024). In addition, the introduction of haplotypes from genetic resources such as wild rice also provides more materials for broadening the genetic basis of cultivated rice and discovering new superior haplotypes (Bharamappanavara et al., 2023).

Nowadays, technologies such as high-throughput sequencing, genome-wide association study (GWAS), and molecular marker-assisted selection (MAS) are developing rapidly. This enables us to have a more systematic understanding of the genetic basis of rice yield traits. Many studies have identified a large number of haplotypes, quantitative trait loci (QTLs), and candidate genes related to yield and its components through GWAS and QTL mapping, and have also revealed their roles in different genetic backgrounds and environments (Ashfaq et al., 2023). Based on this, haplotype molecular design breeding and QTL pyramiding strategies have successfully achieved simultaneous improvement of multiple traits in the field, and new high-yield lines have also been cultivated (Withanawasam et al., 2022; Yadavalli et al., 2022).

This study summarizes the latest progress in haplotype breeding of rice yield traits, introduces the application of haplotype analysis and genomic tools in yield improvement, explores how to identify excellent haplotypes, conduct functional analysis, and their practical application in molecular design breeding. By integrating genomic information and field performance, it promotes a better combination of theory and practice in high-yield rice breeding. This study aims to provide a theoretical basis and technical support for the precise improvement of rice yield traits in the future.

2 Genomic Basis of Yield-Related Traits in Rice

2.1 Key yield traits: grain size, grain number, panicle architecture, and biomass

The yield of rice is determined by many complex traits together. It mainly includes grain size (grain length, grain width, grain thickness), the number of grains per panicle, the structure of the panicle (the length of the main panicle, the number of primary and secondary branches), and the biomass of the entire plant (Wang, 2024). These traits not only directly affect the yield, but also have a significant relationship with indirect traits such as photosynthetic efficiency, nutrient utilization, and stress resistance. Research has found that grain size, 1000-grain weight, number of grains per panicle, panicle length, and effective tillering number are the core traits that determine yield. There are obvious genetic correlations and phenotypic interactions among these traits (Ata-Ul-Karim et al., 2022; Yin et al., 2024).

2.2 Discoveries from QTL mapping, GWAS, and pan-genome studies

Over the past three decades, researchers have identified many yield-related QTLs and candidate genes across the entire rice genome by using QTL mapping in parent populations and GWAS in natural populations. QTL mapping has identified major and minor loci that control traits such as grain weight, grain width, panicle length, and tillering number. Some QTLs exhibit stability in different genetic backgrounds and environments (Padmashree et al., 2023; Daryani et al., 2024). GWAS has made localization more precise and has also discovered many new pleiotropic loci and candidate genes. Some genes have a synergistic regulatory effect among multiple traits, such as GS3, GL3.1, OsCIPK17, GNP12, etc. (Yu et al., 2022; Roy et al., 2024). Meanwhile, pan-genome and large population sequencing studies have found that rice yield traits have strong polygenicity and complex genetic interaction networks. Major genes such as OsMADS22 and OsFTL1 have been functionally verified (Wang et al., 2020; Wei et al., 2024).

2.3 Functional validation of major yield-related genes

Through gene cloning, expression analysis and mutant studies, many major yield genes have been functionally verified. For example, genes such as GS3, GW2, and GL3.1 respectively control grain length, grain width, and grain type; genes such as NOG1 and qHI6 affect sourge-reservoir relationship and grain filling; genes such as OsMADS22 and OsFTL1 regulate heading number and heading time (Khahani et al., 2020; Li et al., 2022; Wei et al., 2024). Furthermore, some QTLs and candidate genes have been applied in high-yield rice breeding through molecular marker-assisted selection (MAS) and gene editing techniques, significantly improving the efficiency of yield trait improvement (Kulkarni et al., 2020; Zhong et al., 2021).

3 Concept and Identification of Haplotypes

3.1 Definition of haplotypes and haplotype blocks in rice genomics

Haplotype refers to a group of alleles or variant sites that are closely linked and inherited together on the same chromosome. In rice genomics, haplotype block usually refers to a region with a relatively high linkage disequilibrium (LD) in the population. These regions have less recombination and relatively stable allele combinations. The structure of haplotype blocks is related to factors such as ancestral recombination, natural selection, and population history, and is an important unit for studying the genetic basis of complex traits and conducting molecular breeding (Figure 1) (Zhang et al., 2021; Shipilina et al., 2022).

Figure 1 Haplotype blocks defined through identity by descent (IBD) (Adopted from Shipilina et al., 2022)

3.2 Haplotype detection tools: sequencing platforms, variant calling, and haplotype phasing

The detection of haplotypes mainly relies on high-throughput sequencing platforms, such as Illumina short-read sequencing, Oxford Nanopore and PacBio long-read sequencing. By combining variant calling and haplotype phasing algorithms, haplotype information can be obtained. Commonly used phase determination tools include EAGLE2, BEAGLE, SHAPEIT2, etc. Different tools perform differently under different data types and group structures. There are mainly two methods for dividing haplotype blocks: the method based on linkage disbalance (LD) and the sliding window method. The LD method is usually more accurate. Analyzing with multiple tools together can make the phase determination results more reliable. In addition, new methods such as HaploBlocker can adapt to data with different marker densities and genetic diversity by focusing on the linkage structure within the population (Otte and Schlotterer, 2020; Weber et al., 2023).

3.3 Characterizing haplotype diversity across global rice germplasm collections

Large-scale sequencing projects such as 3K-RG and MiniCore have revealed the wide distribution of haplotypes in the rice genome and their specificity among different subspecies and regions. Most haplotypes are from specific subspecies or specific populations. In the modern breeding process, the frequency of some major haplotypes has undergone significant changes. There are also many unique haplotypes between wild rice and cultivated rice, some of which have been selected during domestication and adaptation. In-depth research on haplotype diversity provides a solid foundation for the exploration of superior alleles and molecular design breeding (Shang et al., 2022; Aung et al., 2024; Huang et al., 2024).

4 Haplotype-Trait Associations

4.1 Statistical models linking haplotypes to yield phenotypes

Methods such as genome-wide association study (GWAS) and mixed linear models (MLM) can control population structure and kinship, reducing false positives. GWAS, combined with the best linear unbiased prediction (BLUP) values and multi-year phenotypic data, can effectively identify haplotype blocks and candidate genes related to yield. Some researchers used 2.8 million SNPS and BLUP values to detect 816 SNP signals significantly related to 13 agronomic traits in 259 rice materials, and identified candidate genes through haplotype block construction (Wang et al., 2020; Wang et al., 2021; Wang et al., 2023). Anandan et al. (2022) and Al-Daej et al. (2023) found that mixed linear models and unified mixed models were also used for association analysis, reducing the interference caused by group structure and kinship.

4.2 Multi-environment validation of haplotype effects

Verifying the results in different environments is an important step to ensure the reliability of haplotype-trait associations. Studies have shown that some haplotypes or QTLs can significantly affect yield traits in different ecological environments, different years and different genetic backgrounds. For instance, GWAS was conducted on traits such as flowering period under field and greenhouse conditions in the United States, Bangladesh and the United Kingdom, and it was found that ten genomic regions were associated with candidate genes in one or more environments. A single SNP can explain 5% to 50% of phenotypic variations. Some QTLs have stable effects in different geographical environments, indicating that these haplotypes have strong environmental adaptability and breeding potential (Bharamappanavara et al., 2023).

4.3 Functional haplotypes vs. neutral haplotypes: prioritization for breeding

Functional haplotypes refer to haplotypes that have a significant impact on the target trait. Such haplotypes are preferred in molecular design breeding. Studies have found that in modern rice varieties, the frequencies of favorable haplotypes of most known yield-related genes are relatively low, indicating that by mining and aggregating these favorable haplotypes, it is expected to significantly increase the yield (Zhang et al., 2021; Wang et al., 2023). The prioritization of functional haplotypes usually takes into account their interpretability for phenotypes, stability across environments, and association with known functional genes. For instance, some haplotypes have been precisely localized in near-isogenic lines, showing significant structural and expression differences, which directly affect the expression of yield QTLs. On the contrary, neutral haplotypes have no significant impact on traits, so they are not given priority in breeding. The identification and sequencing of functional haplotypes provide a theoretical and practical basis for high-yield molecular breeding of rice.

5 Breeding Strategies Using Superior Haplotypes

5.1 Marker-assisted haplotype selection and pyramiding

Marker-assisted selection precisely screens and aggregates haplotypes related to the target trait by developing molecular markers that can distinguish different haplotypes. For important traits such as yield and stress resistance, haplotype analysis can identify “superior haplotypes” that are significantly superior to other haplotypes, and they can be aggregated into breeding materials through labeling assistance. Researchers have developed a series of haplotype-specific markers on traits such as grain weight and grain length in rice, which can effectively distinguish and track the transmission of superior haplotypes, improving genetic gain and breeding efficiency (Liu et al., 2023; Alam et al., 2024). Haplotype aggregation strategies can also help overcome linkage arrest by combining superior haplotypes of multiple genes, supporting the realization of “design breeding” (Sivabharathi et al., 2024; Meena et al., 2025).

5.2 Integration with genomic selection, speed breeding, and genome editing

The combination of haplotype information and modern breeding techniques such as genome selection (GS), speed breeding, and genome editing can accelerate the utilization of superior haplotypes. Haplotype-based genomic selection models are more accurate than traditional SNP models in predicting complex traits such as yield and stress resistance, and can better reflect the complex relationship between genotype and phenotype (Figure 2) (Bhat et al., 2021; Yoosefzadeh-Najafabadi et al., 2022; Weber et al., 2023). Rapid incubation technology shortens the generation cycle, enabling superior haplotypes to aggregate and fix more quickly. Genome editing technology can also directly modify target genes, create or introduce superior haplotypes, and accelerate the creation of new varieties (Sivabharathi et al., 2024; Meena et al., 2025).

Figure 2  Mining of SNPs and construction of haplotypes for detecting marker-trait associations (GWAS) and computing genomic estimated breeding values (GS) (Adopted from Bhat et al., 2021) Image caption: This diagram describes the comparative potential of the Haplotype-Based GWAS/Haplotype-Based GS in relation to SNP-Based GWAS/SNP-Based GS for the development of improved crop cultivars via genomics-assisted breeding (GAB). It showed that Haplotype-Based GWAS/Haplotype-Based GS in combination with the high-throughput phenotyping (HTP) has great potential to enhance the precision and accuracy in the gene identification and GAB (Adopted from Bhat et al., 2021)

5.3 Combining haplotype information with hybrid rice breeding programs

Haplotype information has brought new ideas for parent selection and hybrid combination design in hybrid rice breeding. Liu et al. (2023) and Singh et al. (2024) found that by analyzing the haplotypes of key genes in parental materials such as restorer lines and maintainer lines, parents carrying superior haplotypes can be selected, thereby enhancing the yield potential and stability of hybrid offspring. The utilization of haplotype diversity can also broaden the genetic basis of hybrid rice and enhance its stress resistance and adaptability. Studies have shown that 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 (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 combination of these technologies enables genomic information to be transformed into field performance more quickly, providing strong technical support for high-yield rice breeding (Bhat et al., 2021).

7.3 Farmer participatory breeding and real-world adoption challenges

Farmer participatory breeding can make haplotype breeding more closely integrated with actual production demands. Allowing farmers to directly participate in variety selection and field trials can better identify excellent haplotype materials that not only meet agronomic requirements but also are suitable for the local environment. However, there are still many challenges in the actual promotion, such as farmers’ acceptance of new varieties, the matching of variety quality and market demand, technical services and policy support during the promotion process, etc. Although some farmers' selected strains have high yield and early maturity, they have deficiencies in terms of quality and other aspects, and ultimately failed to be widely promoted. This also reminds us that haplotype breeding must take into account multiple traits and market demands in practical applications (James et al., 2024).

8 Challenges and Research Gaps

8.1 Haplotype resolution limits in complex genomes

The yield traits of rice are controlled by many genes, and there are also complex interactions and linkage disequilibrium among these genes. This makes haplotype analysis face the problem of insufficient resolution in the context of complex genomes. Although high-throughput sequencing and GWAS technologies have facilitated the localization of QTLS and candidate genes, polyalleles, gene interactions, and environmental influences have made the precise analysis and functional verification of haplotypes very difficult. Especially in the context of the introduction of intersubspecies or wild rice, genomic structural variations and segregation biases complicate the problem (Adam et al., 2023; Bharamappanavara et al., 2023; Sachdeva et al., 2024).

8.2 Data integration: linking genomics, transcriptomics, and phenomics

At present, the integrated analysis of genomic, transcriptomic and phenome data is still insufficient, which hinders the in-depth understanding of the regulatory network of complex traits. Multi-omics joint analysis has been initially applied in candidate gene mining and functional annotation, but the efficiency of data standardization, heterogeneity processing and large-scale data integration remains a technical challenge. The significant variations in the field environment and the insufficient popularity of high-throughput phenotypic techniques have also affected the accuracy of genotype-phenotypic associations (Kiranmai, 2023; Bejjam and Basuthkar, 2024; Sachdeva et al., 2024).

8.3 Socio-economic and policy considerations for large-scale deployment

In the field application and large-scale promotion of haplotype breeding, it is not only necessary to solve technical problems, but also to take into account multiple factors such as society, economy and policy. The promotion of superior haplotype varieties is restricted by farmers’ acceptance of new varieties, the construction of seed systems, intellectual property protection and policy support, etc. Especially in developing countries, insufficient resource allocation, inadequate technical training and weak infrastructure have all affected the implementation of haplotype breeding achievements. Moreover, policies on biodiversity conservation and sustainable agriculture have also put forward higher requirements for the promotion of new varieties (Demeke et al., 2022; Withanawasam et al., 2022; Pallavi et al., 2024).

9 Future Prospects

9.1 Multi-omics-driven haplotype discovery for complex traits

By integrating multi-omics data, researchers can more comprehensively identify key genes and superior haplotypes related to complex traits such as yield and stress resistance. The combination of genome-wide association study (GWAS) and multi-omics platforms can reveal functional genes and their regulatory networks that regulate traits such as yield and drought resistance, providing theoretical basis and molecular markers for precision breeding (Mahmood et al., 2022). The combination of phenotype, genotype and multi-omics data can significantly improve the prediction accuracy of complex traits such as hybrid rice (Xu et al., 2020; Hu et al., 2021). In the future, with the rapid accumulation and analysis of multi-omics data, the discovery and application of superior haplotypes will be further accelerated, which is helpful for achieving molecular design breeding of complex traits (Yang et al., 2021; Wang et al., 2024).

9.2 AI-powered haplotype prediction and breeding decision support systems

Artificial intelligence (AI) and machine learning (ML) technologies provide powerful tools for the integration of multi-omics big data and the prediction of complex traits. AI can efficiently process large-scale genomic, phenotypic and environmental data, enhance the recognition ability of haplotypes and trait associations, and help optimize breeding decisions (Yan and Wang, 2022; Wu et al., 2024). AI-driven multi-omics integrated models can achieve multi-level prediction of genotype - environment - phenotype, helping breeders make more accurate decisions in the early screening stage (Wu and Xie, 2024). In the future, with the continuous advancement of high-throughput phenoomics and AI algorithms, AI-based haplotype prediction and decision support systems will play an important role in molecular design breeding of crops such as rice (Cembrowska-Lech et al., 2023; Wu et al., 2024).

9.3 Toward climate-resilient, high-yield rice through haplotype-based breeding

Haplotype breeding provides a new idea for cultivating climate-resilient and high-yield rice varieties by exploring and aggregating excellent haplotypes related to adverse adaptability such as drought resistance and heat tolerance . Under drought stress conditions, Naqvi et al. (2024) and Singh et al. (2024) have identified multiple superior haplotypes associated with high yield and drought resistance, and have applied them in molecular breeding. In the future, the combination of multi-omics and AI will accelerate the discovery and aggregation of climate-adaptive haplotypes, promoting the development of rice varieties towards high yield, stable yield and climate resilience (Mahmood et al., 2022).

Acknowledgments

The authors appreciate the comments from two anonymous peer reviewers on the manuscript of this study.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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