1 Introducion

Wheat (Triticum aestivum L.) stands as one of the most crucial staple crops globally, providing essential nutrients and serving as a primary food source for billions of people. The increasing global population, coupled with climate change challenges, has intensified the need for sustainable wheat production and improved crop resilience. High-density genetic mapping is a crucial tool in wheat genomics and breeding. It enables the precise identification and localization of genes and quantitative trait loci (QTLs) associated with important agronomic traits such as yield, disease resistance, and quality parameters. The development of high-density genetic maps facilitates the integration of genomic information from different wheat species, thereby enhancing the efficiency of marker-assisted selection and accelerating the breeding process (Maccaferri et al., 2015; Wu et al., 2015; Wen et al., 2017). Moreover, these maps provide insights into the genetic architecture of complex traits, allowing for the dissection of genetic interactions and the identification of candidate genes for further functional studies (Su et al., 2018; Guo et al., 2020). This approach not only accelerates the breeding process but also enhances understanding of the genetic basis underlying key agronomic traits, contributing to food security and agricultural sustainability.

High-density genetic mapping involves the use of a large number of molecular markers, such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs), to construct detailed linkage maps. These maps are generated by genotyping populations derived from biparental crosses or by integrating data from multiple mapping populations. Advances in next-generation sequencing (NGS) technologies and genotyping platforms, such as the Infinium iSelect SNP assay and genotyping-by-sequencing (GBS), have significantly increased the throughput and resolution of genetic mapping in wheat (Poland et al., 2012; Iehisa et al., 2014; Ren et al., 2021). High-density maps not only cover the entire genome with high marker density but also provide accurate marker order and genetic distances, which are essential for QTL mapping and gene cloning (Hussain et al., 2017; Marcotuli et al., 2017).

The main objective of this study is to provide a comprehensive overview of the methods and achievements in high-density genetic mapping of wheat. This includes a detailed discussion of the various genotyping platforms and mapping strategies used for constructing high-density genetic maps, as well as the integration of these maps across different wheat species, highlighting key research and breakthroughs. It will also examine the practical applications of high-density genetic maps in wheat breeding, including marker-assisted selection (MAS) and genomic selection (GS). By synthesizing current knowledge and identifying future research directions, this study aims to emphasize the importance of high-density genetic maps in advancing wheat genetics and breeding, ultimately contributing to global efforts in improving wheat production and resistance.

2 Overview of Genetic Mapping

2.1 Definition and significance

Genetic mapping, also known as linkage mapping, is a tool used to depict the genome structure by determining the positions and relative distances of genetic markers on chromosomes. This technique is crucial for understanding the relationship between genes and traits, aiding in the localization of genes or quantitative trait loci (QTL) associated with complex traits (Su et al., 2018; Guo et al., 2020; Ren et al., 2021). High-density genetic maps contain a large number of markers, providing high-resolution data that can precisely identify genomic regions associated with agronomic traits. This not only accelerates marker-assisted selection (MAS) and genomic selection (GS) in breeding programs but also promotes in-depth research into crop genetic diversity and evolutionary history (Guo et al., 2020).

2.2 Historical perspectives

2.2.1 Early genetic maps

The concept of genetic mapping dates back to the early 20th century. Thomas Hunt Morgan and his students first established the idea of gene linkage in fruit flies (Drosophila melanogaster), proposing the hypothesis that genes reside on chromosomes (Hegde and Srivastava, 2022). This foundational theory provided the theoretical basis for the subsequent construction of genetic maps. During the 1 950s and 1 960s, as research in plant and animal genetics progressed, researchers began to create rough genetic maps in various organisms. These maps were primarily based on morphological markers and simple sequence repeats (SSRs). These early maps were limited by the number of available markers and their distribution across the genome. Despite these limitations, they provided the theoretical basis for understanding the genetic architecture of complex traits in wheat and other crops (Liu et al., 2018; Gutierrez-Gonzalez et al., 2019).

2.2.2 Evolution of mapping techniques

With the advent of molecular biology, genetic mapping techniques underwent significant evolution. The advent of molecular markers, such as single nucleotide polymorphisms (SNPs), revolutionized genetic mapping. High-density SNP arrays, such as the wheat 55 K and wheat 50 K, have enabled the construction of detailed genetic maps with thousands of markers. These maps have been used to identify QTLs for a wide range of traits, including kernel size, weight, and quality traits (Pang et al., 2020; Guo et al., 2020; Ren et al., 2021; Qu et al., 2021). Techniques like genotyping-by-sequencing (GBS) have further enhanced the resolution of genetic maps, allowing for the identification of fine-scale recombination events and structural variations (Gutierrez-Gonzalez et al., 2019; Lv et al., 2021).

Recent studies have utilized these high-density maps to explore the genetic control of important traits in wheat. For example, a study using the wheat 55K SNP array identified major QTLs for kernel-related traits, providing insights into the genetic basis of yield components (Ren et al., 2021). Another study constructed a high-density map using the wheat 50K SNP array to map QTLs for plant height and grain traits, highlighting the importance of these maps in breeding programs (Pang et al., 2020). Additionally, consensus maps combining data from multiple populations have been developed to improve genome coverage and marker density, facilitating more accurate QTL mapping and gene discovery (Qu et al., 2021).

The evolution of genetic mapping techniques from early morphological markers to high-density SNP arrays has significantly advanced our understanding of the genetic architecture of complex traits in wheat. These advancements have important implications for wheat breeding and the development of high-yielding, resilient wheat varieties.

3 Methodologies in High-Density Genetic Mapping

3.1 Molecular markers

Molecular markers are pivotal in high-density genetic mapping, providing the necessary resolution to identify genetic variations associated with key traits.

3.1.1 Simple sequence repeats (SSRs)

Simple Sequence Repeats (SSRs), also known as microsatellites, are short, repetitive DNA sequences that are highly polymorphic and distributed throughout the genome. SSR markers have been extensively used in wheat genetic mapping due to their high level of polymorphism, co-dominant inheritance, and reproducibility. For instance, a study evaluated 20 wheat lines using eight SSR markers, revealing significant genetic diversity and identifying specific alleles associated with high-yielding varieties (Tahir et al., 2022).

3.1.2 Single nucleotide polymorphisms (SNPs)

Single Nucleotide Polymorphisms (SNPs) are the most abundant type of genetic variation in the genome. SNP arrays, such as the Wheat 660 K SNP array, have been developed to facilitate high-throughput genotyping. This array contains a high percentage of genome-specific SNPs with reliable physical positions, making it a valuable tool for marker-assisted selection and genomic studies in wheat (Sun et al., 2020). SNP markers have been used to construct high-density genetic maps and identify quantitative trait loci (QTLs) for various traits, including grain protein content and starch properties (Guo et al., 2020).

3.1.3 Diversity arrays technology (DArT)

Diversity Arrays Technology (DArT) is a microarray-based method that detects DNA polymorphisms without the need for sequence information. DArT markers have been used to generate high-density genetic maps and identify QTLs for important agronomic traits in wheat. This technology provides a cost-effective and efficient approach for genotyping large populations and has been successfully applied in wheat breeding programs (Jadon et al., 2023).

3.2 Mapping populations

3.2.1 Recombinant inbred lines (RILs)

Recombinant Inbred Lines (RILs) are developed by repeated selfing of F2 individuals, leading to homozygous lines that are ideal for genetic mapping. RIL populations have been used to construct high-density genetic maps and identify QTLs for various traits in wheat. For example, a study on QTL (quantitative trait loci) mapping for wheat quality traits using a high-density genetic map (Guo et al., 2020). The research team used recombinant inbred lines (RILs) derived from a cross between 'Tainong 18' and 'Linmai 6' to analyze the genetic control of protein content, sedimentation value, dough rheological properties, falling number, and starch pasting properties. Over three years of field trials, a total of 106 QTLs related to 13 quality traits were detected, distributed across 21 chromosomes (Figure 1). The study found that some QTLs exhibited high stability and frequency, which can be used for subsequent fine mapping of QTLs and identification of candidate genes, providing valuable information for marker-assisted breeding.

3.2.2 Double haploids (DH)

Double Haploids (DH) are produced through the doubling of haploid cells, resulting in completely homozygous lines in a single generation. DH populations are valuable for genetic mapping due to their high level of homozygosity and rapid development. Double haploid (DH) technology plays a crucial role in the localization of quantitative trait loci (QTL) and breeding applications for various crops, significantly improving breeding efficiency and accelerating the development of new varieties. Recent studies have shown that DH populations have been widely used for the localization of QTLs for traits such as iron and zinc concentration and protein content in wheat. Research on spring wheat DH populations from the IPK gene bank in Germany has identified several consistent QTLs that significantly influence anther extrusion. These include QTLs on chromosomes 4A, 2D, and 6B, which have demonstrated consistency across all years (Muqaddasi et al., 2019).

3.2.3 Backcross and multi-parent populations

Backcross populations are created by crossing a hybrid with one of its parents, while multi-parent populations involve crosses between multiple parental lines. Kulkarni et al. (2020) identified quantitative trait loci (QTL) controlling yield and related traits from the popular rice hybrid KRH-2 using a recombinant inbred line (RIL) population. The study constructed a genetic map spanning 294.2 cM, which included 126 simple sequence repeat (SSR) markers evenly distributed across the rice genome. QTL analysis based on phenotypic and genotypic information identified a total of 22 QTLs, among which five major QTLs were found to control total grain yield, panicle weight, plant height, flag leaf width, and panicle length. These QTLs explained 20.23% to 22.76% of the phenotypic variance (Kulkarni et al., 2020). SNP genotyping validated most of the QTLs identified through SSR genotyping. The newly identified QTLs offer potential applications for improving the yield of rice hybrids.

3.3 Genotyping platforms

3.3.1 Microarrays

Microarrays are used to detect thousands of SNPs simultaneously, providing a high-throughput genotyping platform. The wheat 660 K SNP array, for example, has been proven to be reliable and cost-effective for targeted genotyping and marker-assisted selection. Studies have shown that the wheat 660 K SNP array has the highest percentage of genome-specific SNPs, and its SNP density is evenly distributed, making it the best choice for targeted genotyping and marker-assisted selection in wheat genetic improvement (Sun et al., 2020). Additionally, compared to PCR markers and sequencing technologies, SNP arrays offer high throughput, high density, and cost-efficiency advantages, and they are widely used in gene locus detection and QTL mapping in wheat breeding projects.

3.3.2 Next-generation sequencing (NGS)

Next-Generation Sequencing (NGS) technologies provide a comprehensive approach to genotyping by sequencing entire genomes or specific genomic regions. Research has found that Next-Generation Sequencing (NGS) technology has made significant progress in wheat genetic improvement, including mapping sequencing, TILLING, and Genome-Wide Association Studies (GWAS), providing methods for identifying genes/markers related to agronomic traits (Gardiner et al., 2019). The development of NGS technology has made the development of ultra-high-density genetic linkage maps possible, thereby enabling the localization of candidate sites in the genome. Efficient genotyping and sequencing analyses have reduced the cumbersome steps of traditional fine mapping, improving the efficiency of trait analysis and fine mapping (Jaganathan et al., 2020).

3.4 Bioinformatics tools and software

3.4.1 Data analysis pipelines

Data analysis pipelines are essential for processing and analyzing the large volumes of data generated by high-throughput genotyping platforms. Data analysis pipelines, such as the Genome Analysis Toolkit (GATK) and Tassel, streamline the process of converting raw genotype data into usable formats. These pipelines include tools for quality control, alignment, variant detection, and annotation. They enable researchers to efficiently analyze genetic data and identify significant associations between markers and traits, ensuring accuracy and consistency in data analysis (Guo et al., 2020; Sun et al., 2020).

3.4.2 Mapping algorithms

Mapping algorithms, such as JoinMap, MapMaker, and R/qtl, are used to construct genetic maps from marker data and identify QTL. These algorithms analyze genetic data to determine the order and distance of markers on chromosomes. Advanced algorithms also integrate multi-parent and multi-trait data, enhancing the resolution and accuracy of genetic maps. They also identify genomic regions associated with specific traits, providing valuable information for marker-assisted selection and breeding programs (Guo et al., 2020; Jadon et al., 2023).

4 Achievements in High-Density Genetic Mapping

4.1 High-resolution maps

High-resolution genetic maps have been pivotal in advancing wheat genetics. The development of these maps involves the integration of high-density single nucleotide polymorphism (SNP) arrays and genotyping- by-sequencing (GBS) techniques. For instance, a high-density genetic map containing 10 739 loci was constructed using recombinant inbred lines (RILs) derived from a cross of 'Tainong 18×Linmai 6' (Guo et al., 2020). Similarly, another study developed a high-density genetic linkage map with 6 312 SNP and SSR markers to identify QTLs controlling kernel size and weight (Su et al., 2018). These maps have significantly enhanced the precision of QTL mapping, facilitating the identification of stable QTLs across multiple environments (Ren et al., 2021).

4.2 QTL mapping and trait discovery

4.2.1 Yield and agronomic traits

QTL mapping has been instrumental in identifying genetic loci associated with yield and other agronomic traits. For example, a study mapped QTLs for kernel length, width, and thousand kernel weight, identifying stable QTLs in multiple environments (Su et al., 2018). Another research identified 85 QTLs for traits such as grain yield, plant height, and spike length under water deficit conditions, highlighting the importance of these loci in breeding programs aimed at improving yield under stress conditions (Sisi et al., 2022).

4.2.2 Disease resistance

High-density genetic mapping has also facilitated the discovery of QTLs related to disease resistance. A high-resolution genome-wide association study (GWAS) identified 153 QTLs for resistance to leaf rust, yellow rust, and powdery mildew, with several QTLs delimited to ≤ 1.0 Mb intervals (Pang et al., 2021). This fine mapping is crucial for the identification of candidate genes and the development of disease-resistant wheat varieties.

4.2.3 Abiotic stress tolerance

Mapping QTLs for abiotic stress tolerance has provided insights into the genetic basis of traits such as salt tolerance. For instance, a study mapped 90 stable QTLs for 15 agronomic traits under salt stress, with several QTLs validated in natural populations (Luo et al., 2020). These findings are essential for breeding wheat varieties that can withstand harsh environmental conditions.

4.3 Genome-wide association studies (GWAS)

GWAS has been a powerful tool in identifying genetic loci associated with various traits. A high-resolution GWAS identified 153 QTLs for disease resistance and cold tolerance, with high prediction accuracies for genomic selection models (Pang et al., 2021). Another study utilized GWAS to map QTLs for kernel-related traits, identifying five major and stable QTLs across multiple environments (Ren et al., 2021). These studies underscore the utility of GWAS in fine mapping and candidate gene identification, contributing significantly to wheat breeding programs.

4.4 Integration with genomic selection

The integration of high-density genetic maps with genomic selection (GS) has led to the development of predictive breeding models. For example, genomic prediction models based on GWAS data showed high prediction accuracies for resistance to leaf rust, yellow rust, powdery mildew, and cold damage (Pang et al., 2021). The successful application of GS in wheat has led to the rapid development of varieties with enhanced yield, disease resistance, and stress tolerance, demonstrating the transformative impact of high-density genetic mapping on crop improvement. These models are valuable for enhancing the efficiency of breeding programs by enabling the selection of superior genotypes based on their genetic potential.

High-density genetic mapping has revolutionized wheat genetics by providing high-resolution maps, facilitating QTL mapping for key traits, enabling GWAS, and integrating with genomic selection to develop predictive breeding models. These advancements have significantly contributed to the improvement of wheat varieties with enhanced yield, disease resistance, and stress tolerance.

5 Case Studies

5.1 Notable high-density genetic maps in wheat

5.1.1 Case study 1

In a study on Fusarium head blight (FHB) resistance in tetraploid wheat, Sari et al. (2018) used high-density genetic mapping to identify quantitative trait loci (QTL) associated with resistance. The study utilized the 90K Infinium iSelect chip to genotype two doubled haploid populations and conducted phenotypic evaluations in multiple field FHB nurseries. The results showed genotype-by-environment interactions for the expression of FHB resistance QTL, indicating their significant application in breeding programs. Notably, the FHB resistance QTL on chromosomes 1A and 5A exhibited more stable expression across multiple environments, making them suitable candidates for breeding disease-resistant varieties. Additionally, the study found a negative correlation between FHB resistance and traits such as plant height and maturity, with taller plants and later-maturing varieties showing stronger resistance to FHB. These findings provide important genetic resources and markers for breeding FHB-resistant wheat, facilitating the pyramiding of resistance genes and marker-assisted backcrossing programs.

5.1.2 Case study 2

Saini et al. (2021) analyzed the consensus genomic regions associated with multiple disease resistance in wheat and identified candidate genes (CGs) related to multiple disease resistance through meta-QTL (MQTL) analysis. The study examined 493 QTLs from 58 studies, projecting 291 of these QTLs onto a consensus genetic map, resulting in the identification of 63 MQTLs (Figure 2). Among these, 60 MQTLs were anchored to the wheat reference physical map, and 38 were validated through genome-wide association studies (GWAS). Additionally, the study identified 874 CGs, with 194 genes showing differential expression in five transcriptome studies (Saini et al., 2021). The results indicate that these genes are valuable for fine mapping of multi-disease resistance genes and marker-assisted breeding. This research provides a theoretical basis for the identification and breeding of wheat with multiple disease resistance.

5.2 Disease resistance mapping

5.2.1 Rust resistance

High-density genetic maps play a crucial role in identifying and characterizing resistance genes and quantitative trait loci (QTL) for these diseases, thereby facilitating the development of resistant varieties. A comprehensive study identified multiple disease resistance meta-QTL (MDR-MQTLs) for three types of rust diseases in wheat. By analyzing 152 individual QTL mapping studies, a total of 1,146 QTLs were retrieved, and 86 MQTLs were identified through meta-analysis (Pal et al., 2022). Among these, 71 were MDR-MQTLs, and 20 were co-located with known resistance genes. This study highlights the potential of these MQTLs in breeding programs aimed at developing varieties resistant to the three types of rust diseases.

5.2.2 Fusarium head blight resistance

Fusarium head blight (FHB) is a devastating disease in wheat, with direct negative impacts on grain yield, quality, and market value. Multiple studies have focused on mapping the quantitative trait loci (QTL) associated with FHB resistance. For example, researchers utilized the Canadian wheat cultivar AAC Tenacious to study FHB resistance (Dhariwal et al., 2020). The study found that AAC Tenacious possesses significant disease resistance, mainly due to the presence of two major resistance QTL located on chromosomes 2D and 2DS in its doubled haploid population. These QTL are not only related to FHB resistance but also associated with days to anthesis (DTA) and plant height (PHT) (Figure 3). The results indicate that the resistance genes in AAC Tenacious can be used in breeding FHB-resistant wheat, helping to enhance disease resistance and yield in wheat globally. Another genome-wide association study (GWAS) identified new type II FHB resistance loci on chromosomes 4AL and 5DL, which exhibit high collinearity in gene content and order. These findings are crucial for developing FHB-resistant wheat varieties through marker-assisted selection (Hu et al., 2020).

5.3 Yield improvement

High-density genetic mapping has also been utilized to improve yield under drought conditions. Although specific studies on drought tolerance were not provided in the data, the methodologies used in disease resistance mapping can be similarly applied to identify QTLs associated with drought tolerance. The integration of high-density SNP markers allows for the precise identification of loci that contribute to yield stability under water-limited conditions.

Improving nutrient use efficiency in wheat is another area where high-density genetic mapping can be beneficial. By identifying QTLs associated with efficient nutrient uptake and utilization, breeders can develop wheat varieties that require fewer inputs while maintaining high yields. The same high-density mapping techniques used for disease resistance and drought tolerance can be applied to this trait, leveraging the power of SNP markers to accelerate breeding programs.

6 Challenges and Limitations

6.1 Technical and analytical challenges

6.1.1 Marker density and coverage

High-density genetic maps are essential for precise mapping of quantitative trait loci (QTL) in wheat. However, achieving sufficient marker density and coverage remains a significant challenge. For instance, the development of a high-density genetic linkage map with 6312 SNP and SSR markers was crucial for identifying QTL controlling kernel size and weight (Su et al., 2018). Similarly, a genetic map containing 10,739 loci was used to examine the genetic control of grain protein content and other quality traits (Guo et al., 2020). Despite these advancements, the complexity of the wheat genome, with its large size and polyploid nature, makes it difficult to achieve uniform marker coverage across all chromosomes (Gutierrez-Gonzalez et al., 2019; Ladejobi et al., 2019).

6.1.2 Data management and interpretation

The vast amount of data generated from high-density genetic mapping poses significant challenges in data management and interpretation. For example, the use of genotyping-by-sequencing (GBS) derived markers resulted in the construction of maps with thousands of markers, necessitating robust data management systems (Gutierrez-Gonzalez et al., 2019). Additionally, the integration of various data types, such as genomic, epigenetic, and phenomic data, requires sophisticated analytical tools to interpret the results accurately (Borrill et al., 2018). The complexity of the data also makes it challenging to identify and validate candidate genes for marker-assisted selection (Rimbert et al., 2018).

6.2 Biological complexity

6.2.1 Polyploidy in wheat

Wheat's polyploid nature adds another layer of complexity to genetic mapping. The presence of multiple sets of homologous chromosomes can lead to complications in marker development and QTL mapping. For instance, the identification of 3.3 million SNPs across the A, B, and D genomes of wheat highlights the challenges posed by polyploidy (Rimbert et al., 2018). Moreover, the genetic diversity and structural variations among the subgenomes further complicate the mapping process (Tyrka et al., 2021). The redundancy of homoeologous genes can mask the effects of individual loci, making it difficult to pinpoint specific genetic factors (Borrill et al., 2018).

6.2.2 Environmental interactions

Environmental factors significantly influence the expression of many traits in wheat, adding another layer of complexity to genetic mapping. For example, QTLs for kernel-related traits were mapped in multiple environments, revealing significant environmental interactions (Ren et al., 2021). Similarly, the genetic control of protein- and starch-related quality traits was found to be significantly influenced by the environment (Guo et al., 2020). These interactions complicate the identification of stable QTLs and the development of robust markers for breeding programs.

6.3 Resource and infrastructure constraints

6.3.1 Cost and accessibility

The high cost of developing and implementing high-density genetic maps is a major constraint, particularly for resource-limited breeding programs. The construction of high-density maps, such as those using the Wheat55K SNP array, involves significant financial investment (Liu et al., 2018; Ren et al., 2021). Additionally, the cost of genotyping and data analysis can be prohibitive, limiting the accessibility of these technologies to well-funded research institutions and breeding programs (Rimbert et al., 2018).

6.3.2 Capacity building in developing regions

Developing regions often lack the infrastructure and expertise required for high-density genetic mapping. Capacity building in these regions is essential to ensure that the benefits of advanced genetic mapping technologies are widely accessible. For instance, the development of a consensus genetic map using a 90K SNP array provided a valuable resource for systematic mapping and gene discovery in wheat (Qu et al., 2021). However, the successful implementation of such technologies requires investment in training and infrastructure development to build local capacity and expertise (Borrill et al., 2018).

7 Future Directions

7.1 Emerging technologies

7.1.1 CRISPR and gene editing

The advent of CRISPR/Cas9 technology has revolutionized genetic research, providing a precise and efficient method for gene editing. In wheat, CRISPR has been successfully applied to target and modify specific genes associated with important agronomic traits. For instance, the integration of CRISPR/Cas9 with genetic mapping has enabled the identification and functional validation of candidate genes in maize, demonstrating its potential in wheat as well (Liu et al., 2020). The annotated reference genome of wheat further facilitates the application of CRISPR by providing detailed gene content and structural organization, which is crucial for targeted gene editing (Appels et al., 2018). Future research should focus on optimizing CRISPR protocols for wheat and exploring its potential in creating new wheat varieties with improved traits.

7.1.2 Advanced sequencing techniques

Advanced sequencing techniques, such as genotyping-by-sequencing (GBS) and whole-genome resequencing, have significantly enhanced the resolution of genetic maps in wheat. These techniques allow for the discovery of a large number of single nucleotide polymorphisms (SNPs) across the wheat genome, which are essential for high-density genetic mapping (Gutierrez-Gonzalez et al., 2019; Pang et al., 2020). The development of high-throughput genotyping arrays, such as the Wheat55K SNP array, has further streamlined the process of QTL mapping and marker-assisted selection (Liu et al., 2018; Ren et al., 2021). Future directions should include the integration of these advanced sequencing techniques with other omics data to provide a comprehensive understanding of the wheat genome.

7.2 Integrative approaches

7.2.1 Multi-omics

The integration of multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, offers a holistic view of the genetic and molecular mechanisms underlying important traits in wheat. For example, the use of a transcriptome atlas in wheat has revealed tissue-specific gene expression and coexpression networks, which are crucial for understanding the genetic basis of complex traits (Appels et al., 2018). Combining multi-omics data with high-density genetic maps can enhance the identification of candidate genes and the elucidation of their functions. Future research should focus on developing integrative platforms that can seamlessly combine multi-omics data for comprehensive genetic analysis.

7.2.2 Systems biology

Systems biology approaches, which involve the integration of various biological data to model and understand complex biological systems, are becoming increasingly important in wheat research. These approaches can help in identifying key regulatory networks and pathways that control important agronomic traits. For instance, the use of systems biology in conjunction with high-density genetic maps has provided insights into the recombination landscape and structural diversity in wheat (Gutierrez-Gonzalez et al., 2019). Future research should aim to develop robust systems biology models that can predict the effects of genetic modifications and guide breeding programs.

7.3 Strategic roadmap for wheat genetic mapping

The future of wheat genetic mapping requires a strategic roadmap to maximize its impact and utility. First, efforts should be made to encourage the adoption and integration of emerging technologies such as CRISPR, advanced sequencing techniques, and multi-omics approaches into genetic mapping projects. These technologies will significantly enhance the resolution, accuracy, and functional validation of genetic maps, promoting more precise and efficient trait improvement (Appels et al., 2018; Liu et al., 2020; Pang et al., 2020). Developing and maintaining diverse genetic populations, such as recombinant inbred lines (RILs) and doubled haploid (DH) populations, is crucial for facilitating high-resolution mapping (Gutierrez-Gonzalez et al., 2019; Guo et al., 2020). Integrating genomics, transcriptomics, proteomics, and metabolomics data will provide a comprehensive understanding of the genetic and molecular basis of important traits (Appels et al., 2018). In addition, capacity building and collaboration are key components of this roadmap. Investment in capacity building, especially in developing countries, is essential to ensure equitable access to advanced genetic mapping technologies. Promoting international collaboration and knowledge exchange will accelerate the adoption of best practices and innovative approaches, creating a more inclusive and efficient global research community.

8 Concluding Remarks

High-density genetic mapping has significantly advanced our understanding of the genetic architecture of wheat, particularly in relation to important agronomic traits. The development and utilization of high-density genetic linkage maps, such as those incorporating SNP and SSR markers, have enabled the precise identification of quantitative trait loci (QTL) associated with key traits like kernel size, weight, and quality. For instance, the construction of a high-density genetic map with 6312 markers facilitated the detection of 78 putative QTL for kernel-related traits, with several stable QTL identified across multiple environments. Similarly, a high-density genetic map containing 10,739 loci was instrumental in identifying 106 QTL for protein- and starch-related quality traits, highlighting the genetic control of these important attributes.

The advent of fully annotated reference genomes has further propelled wheat research by providing a comprehensive framework for gene discovery and functional analysis. The International Wheat Genome Sequencing Consortium's annotated reference genome has revealed the structural organization and gene content of wheat's subgenomes, enabling more targeted breeding efforts and the application of advanced genomic tools like CRISPR. Additionally, genotyping-by-sequencing (GBS) and SNP arrays have been pivotal in constructing high-resolution maps that uncover the genetic basis of traits such as productive tiller number and kernel-related characteristics.

The achievements in high-density genetic mapping have profound implications for wheat research and breeding. The identification of stable and high-frequency QTL clusters provides valuable targets for marker-assisted selection (MAS), which can accelerate the development of wheat varieties with enhanced yield, quality, and stress resistance. The integration of high-density genetic maps with phenotypic data across multiple environments ensures the robustness of these QTL, making them reliable candidates for breeding programs.

The availability of a fully annotated reference genome has opened new avenues for functional genomics and precision breeding. Researchers can now leverage this resource to dissect the molecular mechanisms underlying complex traits and to implement genome editing techniques for trait improvement. The high-resolution mapping of QTL and the identification of candidate genes facilitate the fine mapping and cloning of genes responsible for desirable traits, thereby enhancing the efficiency of breeding programs. Moreover, the construction of consensus maps from multiple populations enhances the genome coverage and marker density, providing a more comprehensive genetic framework for wheat improvement. These maps enable the systematic comparison and clustering of QTL, aiding in the discovery of novel genes and the development of functional markers for breeding.

In conclusion, high-density genetic mapping has revolutionized wheat research and breeding by providing detailed insights into the genetic basis of important traits and by offering powerful tools for the development of superior wheat varieties. The continued integration of these methodologies with advanced genomic technologies promises to further accelerate genetic gains and ensure the sustainability of wheat production in the face of global challenges.

Acknowledgments

We thank the two peer reviewers for their professional evaluations for enhancing the paper.

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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