Screening and regulation of micro-effect genes for environmental adaptability in wheat breeding

Abstract

This invention discloses a method for screening and regulating environmentally adaptive minor genes during wheat propagation, comprising the following steps: Step S1: Define the target environment and adaptability phenotype, extract key environmental covariates of the target ecological zone, and construct a digital environmental profile; screen physiological and ecological traits corresponding to environmental stress and measure them using high-throughput phenotyping technology; Step S2: Construct genetic materials and design experiments, using a genetic material system composed of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines, and setting up multi-environment gradient experiments and precise stress control experiments; Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change synergistically with environmental signals; determine gene information strongly correlated with adaptability phenotypes through weighted gene co-expression network analysis; Step S4: Functional verification and mechanism analysis to confirm the environment-specific function of the genes.

Description

Technical Field

[0001] This invention relates to the field of wheat propagation technology, and more specifically, to a method for screening and regulating environmentally adaptive minor genes in the wheat propagation process. Background Technology

[0002] Wheat is one of the world's most important food crops, with its cultivation area extending from 67°N to 45°S. It provides approximately 20% of the world's dietary energy and is also a major food crop in my country. Its cultivation techniques vary widely due to regional climate differences. With global population growth and environmental changes, wheat production faces unprecedented challenges, and the large and complex genome of wheat makes it difficult to elucidate the regulatory relationship between genetic variation and phenotype.

[0003] Existing research indicates that minor genes (genes whose trait is influenced by multiple genes, each with a small impact on the phenotype, whose effects are cumulative, have no dominant-recessive relationship, and are sensitive to the environment) play a crucial role in wheat environmental adaptability. However, traditional wheat breeding models have significant limitations: on the one hand, blindly introducing high-yielding varieties from other regions carries extremely high risks, as different varieties exhibit significant differences in environmental adaptability traits such as frost resistance, salt tolerance, and disease resistance, making it difficult for growers to select the right ones; on the other hand, traditional breeding often pursues major genes that are universally applicable, neglecting the interaction effects of minor genes with specific environments, and failing to meet the precise needs of different ecological zones for stable and high yields. With the publication of the wheat reference genome, the development of omics technologies, and the application of mutant libraries, researchers have identified a large number of genetic loci regulating key agronomic traits in wheat. However, with so many related genetic loci, it is difficult to effectively and accurately screen wheat varieties that can adapt to the local environment during wheat propagation.

[0004] The present invention aims to propose a new method for wheat propagation to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for screening and regulating environmentally adaptive minor genes in the process of wheat breeding. By utilizing the interaction effect between genotype and environment, a system for the discovery and application of environmentally adaptive minor genes is constructed, thus solving the technical problem of insufficient environmental adaptability in traditional breeding.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for screening and regulating environmentally adaptive minor-effect genes in wheat propagation, comprising the following steps:

[0007] Step S1: Define the target environment and fit phenotype, extract key environmental covariates of the target ecoregion, and construct a digital environmental profile; screen physiological and ecological traits corresponding to environmental stress and measure them using high-throughput phenotyping technology;

[0008] Step S2: Constructing genetic materials and designing experiments. A genetic material system consisting of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines was adopted. Multi-environment gradient experiments and precise stress control experiments were set up.

[0009] Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change in synergy with environmental signals; identify gene information that is strongly associated with the fit phenotype through weighted gene co-expression network analysis.

[0010] Step S4: Functional verification and mechanism analysis. Through allele effect verification, reverse genetics verification, and physiological mechanism explanation, the gene environment-specific function is confirmed.

[0011] The present invention is further configured to include step S5: integrating and applying genetic information to guide parental selection and breeding, propagating in adverse environments of the target ecological zone, removing impurities during critical periods of environmental stress, and customizing cultivation plans.

[0012] The present invention is further configured such that, in step S1, the key environmental covariates include temperature, precipitation, radiation, soil dynamics, and biological stress pressure during the key growth period.

[0013] The present invention is further configured such that, in step S2, the core germplasm library: selects a wheat population containing extensive genetic diversity for preliminary scanning of genome-wide association analysis to screen for potential environmental interaction genetic loci.

[0014] The present invention is further configured such that, in step S2, the multi-parent design population adopts a nested association mapping population or a multi-parent high-generation interbreeding population, which utilizes its high genetic diversity and high recombination rate to efficiently decompose complex genotype and environmental interaction effects.

[0015] The present invention is further configured such that, in step S2, the chromosome segment substitution line is: a specific donor chromosome segment is introduced into the selected wheat genetic background as material to verify the interaction between candidate genes and the environment, and to locate the target gene.

[0016] The present invention is further configured such that, in the data analysis process of step S3, GWAS involving environmental covariates: environmental factors are directly incorporated as covariates or interaction terms into the genome-wide association analysis model, which can directly detect gene loci whose effects change linearly or nonlinearly with environmental variables.

[0017] The present invention is further configured such that, in the data analysis process of step S3, QTL×environment interaction analysis is performed: joint analysis of variance is conducted on multiple environmental phenotypic data to identify QTLs with significant environmental interactions, and the effect values ​​of each environmental covariate and candidate gene can be clearly defined.

[0018] The present invention is further configured such that, in the data analysis process of step S3, the environment interaction model in genome selection is: a predictive model that embeds genotype and environmental information together.

[0019] The present invention is further configured such that, during the data analysis process in step S3, multi-omics integration and network analysis are performed: under specific environmental treatment, transcriptomic and metabolomic analyses are performed on the materials; and through weighted gene co-expression network analysis, gene information strongly associated with the fit phenotype is determined.

[0020] The present invention is further configured such that, in step S4, the phenotypic differences of different allelic variant vectors of candidate genes are compared in target and non-target environments to confirm the environment-specific effect; phenotypic identification is performed using yield traits in controlled matched and unmatched environments; and it is determined that the gene mediates environmental adaptation by influencing specific physiological pathways.

[0021] The present invention is further configured such that, in step S5, multiple beneficial, environment-matched minor alleles are directionally aggregated in early generations through molecular marker-assisted selection or whole-genome selection; then the selected varieties are propagated; during the propagation process, the core propagation base is directly located in the typical adverse environment of the target ecological zone, so that environmental pressure becomes selection pressure.

[0022] In summary, the present invention has the following beneficial effects:

[0023] By extracting environmental covariates from the target ecoregion, a digital environmental archive was constructed. High-throughput phenotyping technology was used for measurement, which enabled precise quantification of environment and phenotype. Combined with multi-omics integrated analysis, the efficiency and accuracy of discovering environmental adaptation micro-effect genes were significantly improved.

[0024] By constructing genetic materials and experimental designs that interact with the environment, precise and quantitative environmental stresses can be applied during critical growth periods in artificial climate chambers, smart greenhouses, or field water / temperature control facilities. This simulates critical stress windows in real environments, enabling the simulation of environmental covariates in the target ecozone and forming effective and accurate environmental and genetic experiments.

[0025] By exploring environmentally sensitive micro-effect genes, we can address the core pain points of poor stress resistance and unstable yield when introducing varieties to different regions, improve the stable yield of cultivated varieties in target ecological zones, and significantly reduce planting risks. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the field layout for variety selection in this embodiment;

[0027] Figure 2This example illustrates the growth of wheat plants under deep-buried drip irrigation combined with wide and narrow row planting.

[0028] Figure 3 This is a schematic diagram illustrating the wide and narrow row planting configuration in this embodiment;

[0029] Figure 4 This is a schematic diagram illustrating the growth pattern in this embodiment, where there are fewer spikelets in the second layer and higher germination potential of the large spikelets.

[0030] Figure 5 This is a schematic diagram of the traditional planting control area in this embodiment. The control area has a large population, vigorous seedlings, and severe frost damage, which is not conducive to seed production.

[0031] Figure 6 This is a schematic diagram of the traditional planting control area in this embodiment. Control area: There are many ears in the second layer, the disease is serious, which is not conducive to seed production;

[0032] Figure 7 This is a schematic diagram of the wide and narrow row planting of seeds in this embodiment. The seeds are uniform and of good quality.

[0033] Figure 8 This is a schematic diagram of seeds under conventional planting conditions in this embodiment. There are many small and shriveled seeds, and the germination potential varies greatly, resulting in poor seed quality. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] This embodiment discloses a method for screening and regulating environmentally adaptive minor genes during wheat propagation, including the following steps:

[0036] Step S1: Define the target environment and fit phenotype, extract key environmental covariates of the target ecoregion, and construct a digital environmental profile; screen physiological and ecological traits corresponding to environmental stress and measure them using high-throughput phenotyping technology;

[0037] Step S1 is the foundation of the entire plan, determining the direction and accuracy of the excavation; specifically, this step includes:

[0038] Environmental fingerprinting: Instead of using vague geographical concepts like "North China Plain," environmental covariates of the target ecological zone are extracted. These covariates include temperature, precipitation, radiation, soil moisture / salt / nutrient dynamics, and major biological stresses (pathogenic flora) pressures during key growth periods. A digital environmental profile of the target ecological zone is constructed using long-term meteorological data, IoT sensors, and remote sensing imagery, with a focus on the relevant parameters of the aforementioned environmental covariates.

[0039] Fitting phenotypic groups: Physiological and ecological traits directly corresponding to the stress parameters of the aforementioned environmental covariates are selected as targets for analysis. For example: For hot and dry winds during the heading-grain-filling stage: canopy temperature, diurnal variation of stomatal conductance, and thermal stability of grain-filling rate. For winter frost damage: frost-resistant sugar content of tillering nodes and recovery ability from low-temperature photoinhibition. For nitrogen stress: nitrogen uptake efficiency and nitrogen use efficiency (grain yield / nitrogen uptake per plant). High-throughput phenotyping techniques, such as UAV thermal imaging, multispectral analysis, and ground robots, are used for non-destructive, dynamic, and precise measurements to obtain the physiological and ecological traits of plants in the target ecological zone under the influence of corresponding environmental covariates.

[0040] Step S2: Constructing genetic materials and designing experiments. A genetic material system consisting of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines is used to set up multi-environment gradient experiments and precise stress control experiments. Specifically, the genetic materials in this step include a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines.

[0041] 1. Core germplasm banks: Contain populations with broad genetic diversity, used for genome-wide association studies (GWAS) and preliminary scanning. 2. Multiparental design populations: Such as nested association mapping populations or multiparental high-generation intercross lines. They possess higher genetic diversity and recombination rates, and can more effectively decompose complex genotype × environment interactions. 3. Chromosomal fragment substitution lines: Introducing specific donor fragments into a superior genetic background, these are the "gold standard" for validating candidate gene-environment interactions.

[0042] The experimental design employed multiple environmental gradient experiments and precise stress control experiments. Multiple environmental gradient experiments involved setting up repeated experiments at a series of locations representing the target environmental pressure gradient (e.g., from water and fertilizer abundance to mild and moderate drought). Precise stress control experiments involved applying precise and quantitative environmental stresses during critical growth periods in artificial climate chambers, intelligent greenhouses, or field water / temperature control facilities to simulate critical stress windows in the real environment.

[0043] Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change in synergy with environmental signals; identify gene information that is strongly associated with the fit phenotype through weighted gene co-expression network analysis.

[0044] Using statistical models, genetic loci that change in tandem with environmental signals are "extracted" from massive datasets. Specifically:

[0045] 1. GWAS with Environmental Covariates: This method directly incorporates environmental factors (such as soil water potential) as covariates or interaction terms into genome-wide association analysis models. It can directly detect gene loci whose effects change linearly or non-linearly with environmental variables. For example, a gene locus might only significantly affect yield when soil moisture content is below a certain threshold.

[0046] 2. QTL × Environment Interaction Analysis: Joint ANOVA was performed on multi-environment phenotypic data to identify QTLs with significant environment interactions. These QTLs are strong candidate regions for "environment-matching". By analyzing their effect values ​​in each environment, the optimal environmental range for their effectiveness was determined.

[0047] 3. Environment-Genome Interaction Model in Genome Selection: Constructing a predictive model that co-embeds genotype and environmental information. By analyzing the model's weights, it is possible to deduce which genomic regions contribute most to the prediction of a specific environment, thereby identifying key regions.

[0048] 4. Multi-omics integration and network analysis: Transcriptomic and metabolomic analyses of materials under specific environmental conditions.

[0049] Weighted gene co-expression network analysis identified gene modules highly correlated with key "fitting phenotypes." The pivotal genes within these modules are highly likely to be the core of the network regulating minor effects of environmental responses. Future research will focus on these pivotal genes.

[0050] Step S4: Functional verification and mechanism analysis. Through allele effect verification, reverse genetics verification, and physiological mechanism elucidation, the environment-specific function of the gene is confirmed. Specifically, step S4 involves:

[0051] 1. Verification of allele effect: In target and non-target environments, compare the phenotypic differences of different allelic variant vectors of candidate genes to confirm their "environment-specific" effect.

[0052] 2. Reverse genetics verification: Phenotypic identification is performed using yield traits under controlled matched and unmatched environments.

[0053] 3. Physiological mechanism elucidation: Investigate how this gene mediates environmental adaptation by influencing specific physiological pathways (such as the antioxidant system, hormone signal transduction, and osmotic regulation).

[0054] Step S5: Integrate and apply genetic information (i.e., genetic information strongly correlated with the fit phenotype) to guide parental selection and breeding, propagate in the adverse environment of the target ecological zone, remove hybrids during critical periods of environmental stress, and customize cultivation plans. Step S5 achieves the ultimate goal of the research, guiding practice to form a closed loop, specifically:

[0055] 1. Guided parent selection and design breeding: Based on the mined gene database, parents are precisely selected for specific ecological zones to conduct "designed hybridization". Through molecular marker-assisted selection or whole-genome selection, multiple beneficial, environment-matched minor alleles are directionally aggregated in early generations, and the selected varieties are then propagated.

[0056] 2. Innovative Breeding Process (Environmental Stress Selection Breeding Method): Breeding Base Site Selection: The core breeding base is located directly in the typical adverse environment of the target ecological zone (e.g., breeding drought-resistant varieties in arid areas), making environmental stress a natural and powerful selection pressure. The process includes:

[0057] Critical period removal: Remove impurities during critical periods of environmental stress (such as after frost damage or at the peak of drought). Impurities and degenerated plants that are not adapted to the environment will exhibit abnormalities (such as death or severe wilting), thus being efficiently and accurately removed. This method ensures the "environmental suitability purity" of seeds better than simply relying on morphological removal.

[0058] Production management matching: The cultivation management (such as water and fertilizer) of the seed breeding field should not pursue the extreme high yield, but should simulate the medium and high production conditions of the target area to ensure that the adaptability traits of the variety can be fully expressed and "refined".

[0059] 3. Driving Intelligent Cultivation Decisions: Based on the "environment-matching gene" information contained in a variety, customized cultivation plans are developed. For example, for varieties containing deep-rooted drought-resistant genes, water-conserving cultivation and supplemental irrigation during critical periods are recommended; for varieties containing specific nitrogen-efficient genes, precise nitrogen reduction fertilization is recommended. Combining production levels and yield targets, crops are domesticated, and seeds are precisely adapted to the environment, ultimately constructing a "genotype-environment-management" decision support system to achieve "one variety, one digital cultivation map."

[0060] Specifically, this embodiment selects the arid and semi-arid ecological zone of Hebei Province as the target area. The core environmental pressures in this area are hot and dry winds during the wheat heading and grain-filling stage (daily maximum temperature ≥32℃, relative humidity ≤30%), winter frost damage (extreme low temperature ≤-15℃), and drought stress during the growing season. The soil types are mainly brown soil and alluvial soil, with soil salinity of 0.1%-0.3%, and nitrogen deficiency is a secondary environmental limiting factor.

[0061] Implementation steps

[0062] Step S1: Target Environment and Adaptive Phenotype Definition

[0063] 1. Constructing an environmental fingerprint map

[0064] Environmental covariate collection: Meteorological data of the target area over the past 30 years (China Meteorological Data Network) were collected to extract average temperature, extreme temperature, total precipitation, precipitation distribution, and solar radiation intensity during key wheat growth stages (sowing-emergence, tillering, jointing, heading-grain filling, and maturity). Soil moisture content (0-20cm soil layer, monitoring frequency 2 hours / time), soil salinity, and dynamic changes in soil nitrogen, phosphorus, and potassium were monitored in real time using IoT sensors (1 monitoring point every 50m). Disease pressure of pathogens such as rust and powdery mildew was recorded through field disease surveys (surveyed every 15 days) (expressed as disease incidence and disease index).

[0065] Digital environmental archive construction: The above data were integrated using Excel and SPSS software to construct an environmental archive, identifying hot and dry winds during the heading-grain filling period (occurring from mid-May to early June), winter frost damage (occurring from late December to early January), and drought during the growing season (mainly occurring from the jointing to heading stage) as the core environmental pressures.

[0066] 2. Determine the appropriate phenotypic group and measurement

[0067] Phenotypic selection: For hot and dry wind stress, select canopy temperature, diurnal variation of stomatal conductance, and thermal stability of grain filling rate; for winter frost damage, select tillering node antifreeze sugar content (sucrose, fructose) content and low temperature light inhibition recovery ability; for drought stress, select root depth, leaf water retention capacity, and proline (osmotic regulator) content; for nitrogen stress, select nitrogen absorption efficiency and nitrogen use efficiency (grain yield / plant nitrogen uptake).

[0068] Phenotypic measurement methods:

[0069] Canopy temperature: UAV thermal imaging technology was used to measure the temperature daily from 10:00 to 14:00 during the heading-grain-filling stage. Each measurement was repeated three times, and the average value was taken.

[0070] Daily variation of stomatal conductance: Stomatal conductance was measured daily at 8:00, 10:00, 12:00, 14:00 and 16:00 during the heading stage using a ground-based mobile robot equipped with a stomatal conductance meter. Ten plants were randomly selected from each plot, and three functional leaves were measured from each plant.

[0071] Sugar content of tillering nodes under freezing conditions: 7 days after winter freezing damage, tillering node samples were collected, and the sucrose and fructose content was determined by high performance liquid chromatography.

[0072] Root depth: During the harvest period, the profile excavation method was used, and 5 plants were randomly selected from each plot to measure the maximum root depth;

[0073] Nitrogen use efficiency: After harvest, the total nitrogen uptake of the plant (Kjeldahl method) and the grain yield are measured, and the ratio is calculated.

[0074] Step S2: Constructing genetic materials and designing experiments for interaction analysis with the environment

[0075] 1. Planting of genetic materials: The core germplasm bank, nested association mapping population, and chromosome segment substitution system were sown in mid-October at a rate of 14 kg / mu, with 3 replicates, randomized block design, and protection rows.

[0076] 2. Multi-environment gradient experiment: Three treatment groups were set up: 0 waterings (severe drought), 1 watering (mild drought), and 2 waterings (moderate moisture). The watering time was at the jointing stage (1 watering group) and the jointing stage + heading stage (2 watering group), with each watering being 30 m³ / mu.

[0077] 3. Seed propagation related experimental design: The core seed propagation base was set up at the Hebei Academy of Agricultural and Forestry Sciences Dryland Water-Saving Experimental Station. Two weed removals were carried out 10 days after the winter frost and during the peak of drought at the jointing stage. The water and fertilizer management of the seed propagation field simulated the superior production conditions in the target area.

[0078] Step S3: Integrating Data Analysis and Candidate Gene Mining

[0079] 1. Data preprocessing: Organize phenotypic data such as plant height, number of ears per mu, and yield of the three watering treatment groups (see Tables 1, 2, and 3 for details), remove outliers, and perform standardization.

[0080] 2. GWAS involving environmental covariates: Using TASSEL 5.0 software, soil moisture content was included as a covariate in the model. Loci at 1.8Mb on chromosome 2D (AX-94725638) and at 3.2Mb on chromosome 5A (AX-94851267) were found to be significantly associated with drought-affected yield and antifreeze sugar content, respectively.

[0081] 3. QTL × Environment Interaction Analysis: Using QTL IciMapping 4.2 software, three significant environment interaction QTL regions, Qyld-2D, Qyld-5A, and Qyld-7B, were identified.

[0082] 4. Environmental interaction model in genome selection: Using the R software "BGLR" package, it was confirmed that the regions of chromosome 2D 1.7-1.9Mb and chromosome 5A 3.1-3.3Mb contributed the most to the prediction of yield in arid environments.

[0083] 5. Multi-omics integration and network analysis: Through transcriptome analysis and WGCNA, blue gene modules highly associated with yield were screened, and the pivotal genes TaAFP1 (1.8Mb on chromosome 2D) and TaLTP1 (3.2Mb on chromosome 5A) were identified as core candidate genes.

[0084] Table 1. Growth and yield data of varieties that were watered 0 times.

[0085]

[0086] Table 2. Growth and yield data of varieties after one watering.

[0087]

[0088] Table 3. Growth and yield data of varieties that were watered twice.

[0089]

[0090] Step S4: Functional verification and mechanism analysis of candidate genes

[0091] 1. Verification of allele effect: Six varieties carrying different allelic variations of TaAFP1 were selected and planted in environments with 0 waterings and 2 waterings to confirm the drought-specific synergistic effect of TaAFP1 (the C allelic variant vector increased yield by 23.5% compared with the T allelic variant vector).

[0092] 2. Reverse genetics verification: Using RNA interference technology to silence the TaAFP1 gene, the yield of the interfered lines was significantly reduced compared with the wild type under drought conditions, which clearly shows that the gene positively regulates drought resistance.

[0093] 3. Physiological mechanism: The expression level of TaAFP1 gene was significantly upregulated under drought stress, which improved wheat drought resistance by regulating the synthesis of osmotic regulatory substances and the activity of the antioxidant system.

[0094] Step S5: Guiding parent selection and design breeding

[0095] Based on the discovered "environment-matched allele" database, parents are precisely selected for specific ecological zones to conduct "designed hybridization." Through marker-assisted selection or genome-wide selection, multiple beneficial, environment-matched minor alleles are directionally aggregated in early generations. The selected varieties are then propagated.

[0096] Based on the TaAFP1, TaLTP1 and other gene databases, Heng0816 (carrying the TaAFP1 C allelic variant) and Shimai28 (nitrogen-efficient) were selected for a design hybridization. Superior single plants were screened by molecular marker-assisted selection to cultivate a drought-resistant and high-yielding new line "Henghanmai 3".

[0097] Step S6: Driving Smart Cultivation Decisions

[0098] Based on the "environment-matching gene" information contained in the variety, a customized cultivation plan is developed. For example, for varieties containing deep-rooted drought-resistant genes, water-conserving cultivation and supplementary irrigation during critical periods are recommended. Combining production level and yield target requirements, the crop is domesticated, and the seeds for propagation are precisely matched with the environment. Finally, a "genotype-environment-management" decision support system is constructed to achieve "one variety, one digital cultivation map".

[0099] Implementation effect detection and statistics

[0100] Regarding the phenotypic test results, in terms of stress resistance, the sugar content of the tillering nodes of "Henghanmai No. 3" was 32.4% higher than that of the control variety, the water retention capacity of the leaves was 15.6% higher, and the survival rate of winter frost damage reached 94.7%. In terms of yield, the average yield was 276.8 kg / mu under conditions of 0 irrigations, an increase of 21.4% compared to the control; an increase of 15.7% under conditions of 1 irrigation; and an increase of 10.2% under conditions of 2 irrigations. Regarding seed quality, the thousand-seed weight was 38.7 g, the grain uniformity was 92.5%, and the rate of small and shriveled grains was only 3.2%.

[0101] The "Henghanmai No. 3" variety developed in this embodiment showed significant improvements in drought adaptability, yield stability, and seed quality in the target ecological zone, verifying the feasibility of this scheme and providing technical support for stable and high-yield wheat production in arid and semi-arid regions.

[0102] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for screening and regulating environmentally adaptive minor-effect genes during wheat propagation, characterized in that, Includes the following steps: Step S1: Define the target environment and adaptation phenotype, extract key environmental covariates of the target ecoregion, and construct a digital environmental profile; Step S2: Constructing genetic materials and designing experiments. A genetic material system consisting of a core germplasm bank, a multi-parental design population, and chromosome segment substitution lines was adopted. Multi-environment gradient experiments and precise stress control experiments were set up. Step S3: Integrate data analysis and candidate gene mining, integrate environmental covariates, and screen genetic loci that change in synergy with environmental signals; identify gene information that is strongly associated with the fit phenotype through weighted gene co-expression network analysis. Step S4: Functional verification and mechanism analysis. Through allele effect verification, reverse genetics verification, and physiological mechanism explanation, the gene environment-specific function is confirmed.

2. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 1, characterized in that, It also includes step S5: integration and application, guiding parent selection and breeding based on genetic information, propagating in the adverse environment of the target ecological zone, removing hybrids during the critical period of environmental stress, and customizing cultivation programs.

3. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 1, characterized in that, In step S1, key environmental covariates include temperature, precipitation, radiation, soil dynamics, and biological stress during critical growth periods.

4. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 1, characterized in that, In step S2, the core germplasm bank selects wheat populations with broad genetic diversity for preliminary scanning of genome-wide association analysis to screen for potential environmental interaction genetic loci. In step S2, the multi-parent design population is: a nested association mapping population or a multi-parent high-generation interbreeding line population, which utilizes its genetic diversity and recombination rate to decompose genotype and environmental interaction effects; In step S2, the chromosome segment substitution line is: a specific donor chromosome segment is introduced into the selected wheat genetic background as material to verify the interaction between candidate genes and the environment, and to locate the target gene.

5. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 1, characterized in that, In the data analysis process of step S3, GWAS involving environmental covariates: environmental factors are directly incorporated into the genome-wide association analysis model as covariates or interaction terms, which can directly detect gene loci whose effects change linearly or nonlinearly with environmental variables.

6. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 5, characterized in that, In the data analysis process of step S3, QTL × environment interaction analysis is used: joint analysis of variance is performed on multiple environmental phenotypic data to identify QTLs with significant environmental interactions, and the effect values ​​of each environmental covariate and candidate gene can be clearly defined.

7. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 6, characterized in that, In the data analysis process of step S3, the environment interaction model in genomic selection is adopted: a prediction model is constructed that co-embeds genotype and environmental information.

8. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 7, characterized in that, In the data analysis process of step S3, multi-omics integration and network analysis are adopted: under specific environmental treatments, transcriptomic and metabolomic analyses are performed on the genetic materials; through weighted gene co-expression network analysis, gene information strongly associated with the fit phenotype is identified.

9. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 1, characterized in that, In step S4, under target and non-target environments, the phenotypic differences of different allelic variant vectors of candidate genes are compared to confirm the environment-specific effect; using yield traits, phenotypic identification is performed under controlled matched and unmatched environments; and it is determined that the gene mediates environmental adaptation by influencing specific physiological pathways.

10. The method for screening and regulating environmentally adaptive minor genes in wheat propagation according to claim 2, characterized in that, In step S5, multiple favorable, environment-matched minor alleles are directionally aggregated in early generations through molecular marker-assisted selection or whole-genome selection; then the selected varieties are propagated; during the propagation process, the core propagation base is directly set up in the typical adverse environment of the target ecological zone, so that environmental pressure becomes selection pressure.

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