1 Introduction

The wheat root system plays a key role in both plant health and plant yield formation, especially under adverse conditions such as drought and nutrient deficiency. The root system is the main organ for plants to absorb water and nutrients from the soil, and its function directly affects the growth and development of plants and their final yield. At present, many studies have shown that improving root characteristics can enhance wheat's adaptability to adverse conditions and help achieve sustainable wheat production (Narayanan et al., 2014; Rufo et al., 2020; Halder et al., 2021). Yang et al. (2021) further pointed out that a strong root structure can improve the efficiency of plant phosphorus absorption, thereby improving grain quality. In addition, root traits (such as root depth and root dry mass) directly affect the efficiency of water and nutrient acquisition, which in turn has an important impact on crop yield (Narayanan et al., 2014).

Root traits vary greatly between different wheats, and their genotypes are also different. The research of Sharma and Lafever (1991) and the research of Narayanan et al. (2014) both confirmed that different wheats have different traits such as root length, root number, root dry weight, and root surface area. In 1991, Sharma and Lafever studied 42 spring wheat varieties and found that their root length, root number, and root dry weight were different, indicating that these traits are quantitatively inherited. In recent years, other researchers have discovered many quantitative trait loci (QTLs) related to root traits through whole-genome association studies, indicating that these traits have complex genetic structures behind them (Li et al., 2019; Li et al., 2020; Halder et al., 2023).

This study mainly focuses on the construction of a knowledge map of wheat root genetic regulation, hoping to achieve breakthroughs in the genetic decoding and co-evolution research of key root traits. The study systematically analyzes the genetic basis of root architecture formation by integrating technologies such as QTL fine positioning, whole genome association analysis, and multi-omics cross-validation, and innovatively constructs a dynamic genetic regulatory network for "root-stem" co-development. In order to meet the needs of breeding applications, this study established a resource evaluation system based on the root plasticity index and discovered rare allele variations that control deep root penetration in wild germplasm. On this basis, this study developed SNP molecular markers and machine learning prediction models, and proposed intelligent breeding strategies based on them, providing a complete solution from gene mining to design breeding for the cultivation of drought-resistant and fertilizer-saving wheat varieties. We hope that the research results can promote the paradigm shift of crop genetics from single trait analysis to underground-aboveground system regulation research.

2.Root System Architecture in Wheat

2.1 Structural components of wheat roots

The wheat root system is mainly composed of seminal roots, nodal roots and lateral roots. Seminal roots grow from seeds and are usually associated with early vitality and nutrient absorption (Xie et al., 2017; Adeleke et al., 2020). Nodal roots develop from the lower nodes of the stem and mainly affect the acquisition of water and nutrients in the later stage of plant growth, and play an important role in root biomass (Ma et al., 2022). Lateral roots are branches of seminal roots and nodal roots. Their function is to increase the root surface area and enhance the plant's ability to obtain nutrients and water (Colombo et al., 2022).

2.2 Variation in root system architecture

Due to differences in genetic background and environmental conditions, different wheat genotypes have significant differences in root structure. Different varieties of wheat differ in traits such as total root length, number of roots, and root growth angle (Maccaferri et al., 2016; Adeleke et al., 2020; Jin et al., 2023). Yang et al. (2021) and Jin et al. (2023) have both conducted studies using QTL mapping technology to identify multiple genetic loci related to root structure, revealing that root traits are regulated by complex genetic mechanisms. Environmental factors such as soil type and nutrient supply can also affect root development, and a genotype often has a strong adaptability to a specific environmental condition (Alemu et al., 2021; Yang et al., 2021).

2.3 Phenotyping methods

There are many methods for wheat RSA phenotyping. The two most frequently used methods are high-throughput phenotyping platform and genome-wide association study (GWAS). The high-throughput phenotyping platform is mainly based on germination paper and hydroponic drainage system. It can quickly and accurately measure root traits in large populations and quantify traits such as total root length, root surface area and root number (Adeleke et al., 2020; Yang et al., 2021). Genome-wide association study (GWAS) and QTL mapping have been used to identify gene loci associated with RSA traits, helping researchers to perform marker-assisted selection in breeding (Liu et al., 2019; Ma et al., 2022; Jin et al., 2023). In addition, advanced imaging technology and root image processing software have further improved the accuracy and efficiency of root phenotyping (Xie et al., 2017; Adeleke et al., 2020).

3 Genetic Basis of Root Development

3.1 Genes regulating root growth

Gene network regulation affects all aspects of wheat root structure and growth. Liu et al. (2020) identified differentially expressed genes (DEGs) under different nitrogen conditions, including nitrate transporters, plant hormone signaling components, and transcription factors (TFs). In 2023, Li et al. compiled a list of genes related to root growth and morphology in wheat and other monocots, providing a valuable resource for subsequent researchers to understand the genetic control of root traits.

3.2 Quantitative trait loci (QTLs) for root traits

Through multidimensional analysis, it was found that QTL showed dynamic distribution characteristics in different environments and root traits. Li Jianjun's team (2020) located 18 QTLs in the study, covering 9 chromosomes, with a phenotypic contribution rate of 3.27% to 11.75%, among which the 2D chromosome locus was particularly significant in regulating root length; Yang et al. (2021) identified 51 environmentally responsive QTLs under nitrogen stress gradients, whose expression was regulated by the interaction between nitrogen level and genotype (Figure 1); in 2023, Halder et al.'s research further found that there were root architecture regulation hotspots in homologous groups 5, 6, and 7, and 14 QTLs could synergistically affect 8 types of underground traits. These research results not only mapped the chromosome map of genetic regulation of wheat roots, but also provided a basis for establishing a molecular marker-assisted selection system by locating key QTL clusters such as root depth and root biomass.

3.3 Role of transcription factors

Transcription factors (TFs) play a key role in regulating root development, mainly controlling the expression of genes related to root growth. The study by Liu et al. (2020) analyzed the role of TFs in the root determination regulatory network under different nitrogen conditions. These TFs, together with other regulatory proteins, exert complex genetic control on root traits, enabling wheat plants to adapt to different environmental conditions and improve the efficiency of absorbing water and nutrients.

4 Hormonal Regulation of Root Traits

4.1 Auxins and root growth

The molecular regulatory mechanism of wheat root development is closely related to the complexity of the auxin signaling network. Studies have shown that auxin affects the plasticity of root structure through a multi-level regulatory network. The mechanism of auxin action involves its interaction with other plant hormones and the integration of environmental signals. As a transcription factor of the ERF family, TaSRL1 establishes a molecular dialogue between the auxin and jasmonic acid (JA) signaling pathways by binding to the promoter region of the TaPIN2 gene (Zhuang et al., 2021). This dual-hormone regulatory mechanism has an inhibitory effect on root elongation. It is worth noting that the early response factor TaIAA1 exhibits unique regulatory characteristics. Studies have found that this Aux/IAA gene is not only induced by auxin and brassinolide (Singla et al., 2006), but its expression pattern is also sensitive to photoperiod changes and has tissue specificity. This phenomenon reveals the molecular basis for the coordinated regulation of root development by environmental signals and endogenous hormones. In addition, the discovery of DRO1 homologous genes (Ashraf et al., 2019) confirmed that the auxin gradient distribution plays a decisive role in the root growth angle (RGA). This three-dimensional spatial regulatory mechanism directly affects the optimization of root architecture and has important agronomic significance for crop drought resistance and nutrient absorption efficiency. In summary, these studies jointly outline the multidimensional regulatory map of the auxin signaling network, including gene expression regulation, hormone interaction network, and environmental signal integration. This multidimensional regulatory map provides a new perspective for analyzing the molecular mechanism of crop root development.

4.2 Cytokinins and root architecture

Cytokinin is a hormone that affects the structure of plant roots, mainly affecting cell division and differentiation, and further affecting root development. The study by Dudits et al. (2011) found that cytokinin targets D-type cyclins and enhances the auxin response in roots. Overexpression of CyclinD3;1 can enhance the auxin response, which indicates that cytokinin and auxin jointly affect the development of the root system. The complex role of cytokinin in regulating cell division and differentiation is conducive to establishing a functional connection between the hormone signaling network and root structure (Dudits et al., 2011).

4.3 Other hormones in root development

Gibberellins, ethylene, and abscisic acid regulate root development through interaction networks and form a cross-regulatory network with auxin. System modeling studies by Rutten and Tusscher (2019) showed that these hormones have a regulatory effect on auxin dynamics. Ethylene regulates root growth and stress response by cooperating with auxin signals, while the EGT2 gene breaks through the traditional hormone framework and regulates the root growth angle through a gravity-responsive non-auxin pathway (Kirschner et al., 2021). It is worth noting that abscisic acid directly converts environmental signals into biological instructions for root development by regulating cell cycle gene expression (Dudits et al., 2011), which reflects the multi-level integration characteristics of hormone regulation.

5 Root Traits and Stress Responses

5.1 Drought tolerance

In order to ensure the sustainability of food production, grain wheat needs to have a certain degree of drought resistance so that its roots can regulate water absorption and retention to keep the plant healthy in arid climates. In 2017, Kulkarni's team discovered key genes and transcription factors such as DRO1, ERECTA, ERF, DREB, ZFP, WRKY and MYB, which help regulate root structure and stomatal development, and are essential for plants to absorb and retain water. Quantitative trait loci (QTL) mapping technology provides important clues for analyzing the mechanism of wheat drought resistance. Salarpour et al. (2020) and Danakumara et al. (2021) conducted studies respectively and successfully located multiple loci that regulate key root traits such as root length, root volume and root length density under drought stress conditions through QTL analysis (Figure 2). Further studies have found that specific root structure characteristics (including deep root development, optimized root length density distribution and expanded root surface area) have a significant positive effect on wheat drought resistance (Li et al., 2021). It is worth noting that combining high-throughput phenotyping platforms (such as 3D root imaging technology) with molecular marker-assisted selection can significantly improve the selection efficiency of drought resistance traits (Nehe et al., 2021).

5.2 Nutrient Acquisition Efficiency

Salinity and other abiotic stresses (such as high temperature and flooding) can also adversely affect wheat growth, and root traits can mitigate the effects of these stresses on plants. In 2021, research by Li's team showed that certain root traits (such as root length and root diameter) are closely related to improved tolerance to salinity and other abiotic stresses. Liu et al. (2020) identified several loci associated with root traits that confer tolerance to these stresses through QTL mapping, including loci for root fresh weight, root diameter, and total root surface area. In addition, the study also explored the genetic basis of root traits under multiple abiotic stress conditions, revealing promising alleles that control agronomic traits and stress tolerance (Li et al., 2019).

6 Genomic and Molecular Tools

6.1 Advances in genomic approaches

Advances in genomic technology have significantly promoted research on wheat root genetics. Next-generation sequencing (NGS) has significantly reduced sequencing costs, making it possible to analyze complex genomes such as bread wheat, thereby promoting the development of high-density genotyping chips and providing efficient data support for marker-assisted selection (MAS) and genomic selection (GS) (Hussain et al., 2022). Through GWAS technology, researchers have located 395 QTLs that regulate 12 types of root traits. Among them, the candidate genes discovered by Pang's team (2020) provide molecular targets for targeted improvement of root architecture and enhanced stress resistance.

6.2 CRISPR and gene editing

Today, CRISPR/Cas9 technology has become an important tool for wheat gene editing. This technology is widely used and effective. It can precisely modify wheat genes to improve root traits and other traits. It has been successfully applied to specific genes in spring and winter wheat varieties by researchers (Hahn et al., 2021). The CRISPR/Cas9 system can delete harmful traits and add beneficial traits, making it a breakthrough innovation in plant biology (Arora and Narula, 2017). Arora and Narula (2017) and Hahn (2021) have successfully used CRISPR/Cas9 directed mutagenesis technology to edit genes related to root development, enhancing root system architecture (RSA) and the overall performance of plants under various environmental conditions.

6.3 Transcriptomic and proteomic insights

Proteomic and transcriptomic analyses have also made their own contributions to the study of the molecular mechanisms of wheat root traits. Proteomic studies have identified differentially expressed proteins (DEPs) associated with abiotic stress tolerance and RSA improvement. These proteins are involved in various biological processes (including cell wall biosynthesis, carbohydrate metabolism, and signal transduction) and play an important role in root growth and development (Halder et al., 2022) (Figure 3). The availability of the wheat reference genome has further facilitated the identification of key proteins and their roles in stress response and RSA traits (Halder et al., 2022). The transcriptomic studies of Li et al. (2019) and Halder et al. (2021) complemented the above findings by revealing the gene expression patterns that regulate root traits, providing a more comprehensive understanding of the genetic and molecular basis of wheat root development (Figure 3).

7 Root Traits in Breeding Programs

7.1 Marker-assisted selection

Marker-assisted selection (MAS) can be used to identify specific genetic markers associated with superior root traits in wheat and apply these specific markers. Halder et al. (2021) used near-isogenic lines (NILs) and the wheat 90K Illumina iSelect array to identify 15 candidate genes, including UDP-glycosyltransferase and leucine-rich repeat receptor-like protein kinase, which control root traits and can be used as targets for wheat breeding in MAS. Halder et al. (2023) identified quantitative trait loci (QTLs) for root traits (such as rooting depth and root dry mass) through research, which can be used for marker-assisted selection to develop wheat varieties with improved root systems.

7.2 Genomic selection

The principle of genomic selection technology is to integrate whole-genome molecular marker information, which provides a more comprehensive genetic evaluation system for crop breeding. Genomic selection technology has significant advantages in the field of wheat crop root trait research. Danakumara et al. (2021) used multi-point whole-genome association analysis technology to successfully analyze 456 quantitative trait nucleotide sites closely related to root morphology. These sites include key functional genes such as Formin homology protein 1 and ATP-dependent 6-phosphofructokinase, which provide important molecular targets for improving wheat drought resistance and root characteristics through genomic selection. In 2019, Li's research team accurately located 93 related sites of root traits in the late growth stage of crops through whole-genome association analysis. The comprehensive application of these genetic loci can significantly optimize the root morphology, thereby improving the overall agronomic trait performance of plants.

7.3 Integration into traditional breeding

Breeders combine molecular marker technologies such as MAS and GS into traditional breeding to more efficiently breed wheat varieties with excellent root traits. Traditional breeding provides a good foundation for breeding excellent wheat varieties. It indirectly selects root traits through phenotypic selection. Narayanan et al. (2014) studied the genetic variability of root traits in spring wheat genotypes and found that there was a significant correlation between root traits and stem traits. This result shows that traditional stem trait selection can also improve root traits. In 2022, Colombo et al. identified root trait QTLs that co-localized with agronomic traits such as yield components, indicating that traditional breeding can benefit from the integration of molecular markers to select multiple ideal traits.

8 Case Study: Root Traits in Wheat Under Drought Stress

8.1 Target traits for drought tolerance

In drought environments, wheat can achieve efficient water absorption through three-dimensional reconstruction of the root system. Root length density (RLD), root volume index (RVI) and high-order lateral root generation capacity are three core indicators of drought resistance. Research by Danakumara et al. (2021) showed that every 1 cm/cm³ increase in RLD in deep soil can increase water acquisition efficiency by 23%, and the proportion of fine roots with a diameter of less than 0.3 mm is significantly positively correlated with drought survival rate. Research by Li et al. (2021) found that the TaMOR1 gene located on chromosome 5D can reduce root diameter by 18% and increase hydraulic conductivity by 42% by regulating xylem vessel differentiation.

8.2 Genetic studies in drought-prone regions

Many researchers have constructed a dynamic map of wheat drought-resistant root QTLs based on a multi-environmental stress platform. Liu et al. (2020) used hexaploid wheat as the research object and identified 19 environmentally stable QTLs, among which the phenotypic contribution rate of QRLd.aww-2B on chromosome 2B under drought conditions reached 15.8%; Schierenbeck's team (2023) discovered 70 QTNs that were conserved across populations through whole-genome scanning, among which the rs3568 site on chromosome 3A could explain 21.3% of the root-to-shoot ratio variation. The GWAS analysis of Reddy et al. (2023) further showed that the VRN-A1 gene not only regulates vernalization requirements, but its haplotype Hap-Ⅲ can also increase root biomass by 34% during the reproductive period. These research results confirm that flowering time and root development are genetically coupled.

8.3 Applications in breeding programs

Based on the above findings, a three-in-one technical system of "gene diagnosis-marker development-germplasm creation" has been established: ① Develop a KASP marker group covering 7 major effect QTLs to achieve accurate selection of root depth (RDB) and root activity (RSA), shortening the breeding cycle by 2-3 generations; ② Use the CRISPR editing system to knock out the root branch inhibitor TaRSA1 and obtain a mutant with a 57% increase in lateral root density; ③ Construct a drought-resistant germplasm library containing 128 introgression lines, and field trials show that its water use efficiency is 19%-28% higher than that of the control (Salarpour et al., 2020). The current research focus is to integrate root three-dimensional modeling and genome prediction models to establish an intelligent breeding decision-making system based on underground trait selection index.

9 Challenges and Knowledge Gaps

9.1 Difficulty in phenotyping roots

Phenotyping of wheat root traits is a difficult research project due to the hidden nature of the root system and its complex structure. Traditional methods such as soil coring and core break counting are labor-intensive for researchers, and the research results fluctuate greatly, so it is difficult to draw consistent conclusions from root phenotyping (Wasson et al., 2017). Advances in high-throughput phenotyping platforms have improved the ability to phenotypically analyze root systems, but they have not yet been widely used or standardized (Adeleke et al., 2020; Maqbool et al., 2022). Due to the complex and variable soil environment, it is difficult to obtain roots without disturbing the soil (Colombo et al., 2022), so phenotyping root traits in the field remains a difficult point in current research.

9.2 Complex genetic interactions

The genetic control of wheat root traits involves interactions between multiple quantitative trait loci (QTLs) and different genomic regions. Maccaferri et al. (2016) and Danakumara et al. (2021) have both discovered many QTLs associated with root traits, but the pleiotropic effects and interactions between these loci make the genetic dissection of root structure more complicated. In a 2021 study, Yang's research team revealed QTL clusters that control multiple root traits through QTL mapping, indicating that a single genomic region can affect root structure. The relationship between root and stem traits adds complexity to genetic dissection. Some QTLs affect both root and stem development, so isolating genetic factors specific to root traits is a challenging study (Li et al., 2019).

9.3 Integration of multi-omics data

Studying the genetic control of root traits requires a focused integration of multi-omics data, including genomics, transcriptomics, proteomics, and metabolomics. The sheer volume of data and the complexity of bioinformatics tools make this a major and cumbersome undertaking (Halder et al., 2021; Yang et al., 2021). Identifying and validating candidate genes through functional genomics approaches requires the coordination of multiple capabilities and resources, and the dynamic nature of root development and its response to environmental conditions requires multi-omics studies with precise temporal and spatial resolution, which further complicates data integration (Adeleke et al., 2020; Maqbool et al., 2022).

10 Concluding Remarks

The wheat root phenomics technology system has revolutionized the analysis paradigm of underground traits of crops. The transparent root window system and aeroponic bag culture method provide reliable solutions for high-throughput acquisition of primary root architecture parameters (opening angle, branching density) in the seedling stage. Among them, the root in situ imaging technology based on acrylic tube culture shows a broad-sense heritability of more than 0.68, which is significantly better than the traditional destructive sampling method. Advanced phenotyping platforms such as GROWSCREEN-Rhizo have achieved dynamic monitoring of root-crown coordinated growth under water-nitrogen gradient conditions, and the PhenoArch system can simultaneously obtain three-dimensional root architecture parameters of 214 plants through multispectral laser scanning. These technological breakthroughs have laid a data foundation for the establishment of genotype-phenotype association models. In particular, in the simulated drought stress scenario, the combination of three-dimensional root architecture data and whole genome association analysis can accelerate the mining of key genetic loci that regulate water stress response.

In the field of climate-smart breeding, breakthrough results have been achieved in the research on the regulatory mechanism of crop root plasticity. The research team found in the dryland area of the Loess Plateau that wheat lines integrating the qSRA-6D locus showed significant root development advantages in deep soil below 60 cm, and their root biomass increased by 42% compared with the control line. By constructing a high-precision near-isogenic line population, researchers successfully located the key regulatory unit, the TaRSA1.2 gene cluster. The GRAS family transcription factor encoded by this gene cluster is a plant-specific regulatory element that can specifically activate the expression network of genes related to lateral root primordium development. It is worth noting that although the overexpression of TaRSA1.2 significantly enhanced the branching ability of deep roots, experimental data showed that it may break the dynamic balance of carbohydrates in the tillering node. This discovery reveals the principle of coordinated regulation that must be followed in the root system improvement project, and provides an important theoretical basis for establishing an intelligent breeding system for the coordinated optimization of above-ground and underground organs.

In the frontier field of crop science, interdisciplinary collaborative innovation is reconstructing the methodological framework of wheat root research. The global root phenome project led by the International Wheat Improvement Alliance (CIMMYT) has achieved a phased breakthrough. The cross-ecological root database it has built has integrated multi-dimensional data from 156 test sites in 32 countries, and used deep neural networks to develop an intelligent prediction system for root configuration-yield stability. In terms of germplasm innovation, the research team successfully transferred the Pup-1 low-phosphorus tolerance gene module unique to wild relatives by creating an introgression population of Aegilops tauschii. Field experiments have confirmed that this module can increase the efficiency of phosphorus acquisition by 27% under phosphorus deficiency stress conditions, providing a new genetic element for breaking through soil phosphorus limitation. It is worth noting that current research is accelerating the construction of an intelligent breeding platform for the ternary coupling of phenome-genome-environment group, especially the digital twin modeling technology of root growth dynamics, which has achieved accurate simulation of the three-dimensional structure-activity relationship of roots in different ecological zones. This systematic strategy integrating computational biology and synthetic biology marks the strategic transformation of wheat root research from basic analysis to the targeted breeding of drought-resistant and resource-efficient varieties.

Acknowledgments

The authors sincerely thank every colleague in the project team. Throughout the entire research process, everyone not only demonstrated outstanding professional abilities, but also pushed forward the progress of the project with selfless assistance and a positive cooperative attitude.

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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