A corn breeding method based on MAS technology

Abstract

The application provides a corn breeding method based on MAS technology, comprising the following steps: marking existing corn low-temperature-tolerant quantitative trait locus information to a reference map, obtaining corn low-temperature-tolerant consistent quantitative trait loci through Meta analysis on the marked reference map; obtaining corn germination stage low-temperature-tolerant quantitative trait locus information based on the strongest low-temperature-tolerant corn inbred line and the weakest low-temperature-tolerant corn inbred line; determining corn germination stage low-temperature-tolerant trait candidate region mining response low-temperature significant difference candidate genes based on the consistent quantitative trait locus information and the corn germination stage low-temperature-tolerant quantitative trait locus information; and performing corn breeding according to the candidate genes. The corn low-temperature-tolerant quantitative trait locus information of a preset quantity is integrated, the relationship between the corn low-temperature-tolerant related quantitative trait locus effect and position is evaluated, the molecular marker technology applied to the MAS technology in the development process of the corresponding section is provided with powerful help, and the MAS assisted breeding efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of breeding technology, and in particular to a maize breeding method based on MAS technology. Background Technology

[0002] The optimal growth temperature for maize is 28℃~32℃, and its seed germination, growth and development, and dry matter accumulation are highly sensitive to temperature. If the average temperature drops below 8℃ after sowing and persists for 3-4 days, it can cause seed powdering, rotting, or even prevent seedling emergence, resulting in severe yield reduction. Low-temperature chilling injury during maize germination is a serious problem in mid-to-high latitude maize-producing areas worldwide. In severe years in Northeast my country, maize yields have even decreased by more than 20%. From 1961 to 2007, Heilongjiang Province had the highest average incidence of low-temperature chilling injury, reaching 38%. Breeding and creating maize germplasm with strong low-temperature tolerance during germination is the most economical and effective way to solve the problem of low-temperature damage.

[0003] Cold tolerance in maize is a complex quantitative trait controlled by multiple genes with minor effects, making it difficult to breed and create cold-tolerant germplasm using conventional methods. With the rapid development of biotechnology, scholars at home and abroad have carried out some beneficial explorations in molecular design breeding research for cold tolerance in maize, but the major QTLs or key genes have not yet been identified.

[0004] In recent years, some progress has been made in accelerating the genetic improvement of crop stress resistance traits with the help of MAS molecular marker-assisted breeding technology. However, since most of the genes controlling crop stress resistance traits are quantitative traits, MAS technology has not been widely used in maize breeding practice due to the limitations of QTL mapping. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a maize breeding method based on MAS technology. The study uses IBM22008Neighbors as a reference map and integrates 295 maize low-temperature tolerance-related QTLs through meta-analysis to obtain "consistent QTLs" with large effect values ​​that can be detected under different conditions. The relationship between the effect and location of maize low-temperature tolerance-related QTLs is evaluated, which will provide strong support for the future development of molecular markers that can be applied to MAS technology in their corresponding segments and greatly improve the efficiency of MAS-assisted breeding.

[0006] The advantages of applying meta-analysis to the MAS (Magnetic Synthesis) of maize's cold tolerance trait are as follows: Due to various limitations, a single QTL mapping is unlikely to obtain all target QTLs, and the reliability of QTL phenotypic contribution rates is poor. This study uses meta-analysis to integrate previous maize cold tolerance-related QTLs, providing more reliable molecular markers for MAS. Typically, genetic maps constructed by QTL mapping have low marker density, limiting their reference value for fine mapping and resulting in low MAS efficiency. This study integrates previous results to construct an integrated map of maize cold tolerance-related QTLs. This map is rich in molecular markers, and with the help of meta-analysis, MQTLs that can be detected in different QTL mappings can be obtained. By developing molecular markers closely linked to MQTLs, the efficiency of MAS can be improved.

[0007] This application provides a maize breeding method based on MAS technology, the method comprising:

[0008] Existing quantitative trait loci information of maize cold tolerance were labeled onto a reference map, and the uniform quantitative trait loci of maize cold tolerance were obtained by meta-analysis of the labeled reference map.

[0009] The strongest and weakest cold-resistant maize inbred lines are selected from a preset number of maize inbred lines, and the cold resistance quantitative trait locus information of maize during germination is obtained based on the strongest and weakest cold-resistant maize inbred lines.

[0010] Two sets of ribonucleic acid data were obtained for the strongest and weakest low-temperature resistant maize inbred lines before and after low-temperature stress treatment during germination, to obtain the change data of the strongest and weakest low-temperature resistant maize inbred lines before and after low-temperature stress treatment during germination;

[0011] Based on the consistent quantitative trait locus information and the maize germination period low temperature tolerance quantitative trait locus information, candidate regions for the maize germination period low temperature tolerance trait are determined, and candidate genes with significantly different expression in response to low temperature are mined based on the change data.

[0012] Maize breeding was carried out based on the candidate genes.

[0013] Optionally, the step of annotating existing maize low-temperature tolerance quantitative trait loci information onto a reference map and obtaining maize low-temperature tolerance uniform quantitative trait loci through meta-analysis of the annotated reference map includes:

[0014] Obtain existing quantitative trait locus information for maize's low-temperature tolerance, wherein the quantitative trait locus information includes the name of the quantitative trait locus, the chromosome of the quantitative trait locus, the LOD value of the quantitative trait locus, the population type of the quantitative trait locus, and the size of the quantitative trait locus.

[0015] Construct an original map of quantitative trait loci information based on existing quantitative trait loci information for maize's low-temperature tolerance;

[0016] The original map of the quantitative trait loci information is compared with the reference map, and the original map of the quantitative trait loci information is adjusted accordingly.

[0017] The original quantitative trait locus information was proportionally labeled onto the reference map using a homogeneous function;

[0018] Meta-analysis of the annotated reference map yielded quantitative trait loci for maize's low-temperature tolerance.

[0019] Optionally, the step of obtaining the uniform quantitative trait loci of maize cold tolerance through the reference map annotated by meta-analysis includes:

[0020] The location of the quantitative trait loci on the chromosome was determined by meta-analysis of the annotated reference map;

[0021] The quantitative trait locus at the determined location is taken as the consistent quantitative trait locus.

[0022] Optionally, the step of selecting the strongest and weakest cold-resistant maize inbred lines from a preset number of maize inbred lines includes:

[0023] Obtain experimental data of the predetermined number of maize inbred lines, wherein the experimental data are field and indoor low-temperature tolerance identification data of the predetermined number of maize inbred lines for at least two consecutive years;

[0024] Based on the low-temperature resistance identification data, the growth data of the preset number of maize inbred lines are statistically analyzed, wherein the growth data includes relative emergence index, relative germination rate and relative germination index;

[0025] The strongest and weakest cold-resistant maize inbred lines were determined based on the relative emergence index, the relative germination rate, and the relative germination index.

[0026] Optionally, the step of obtaining the quantitative trait locus information of maize germination-stage low-temperature tolerance based on the strongest and weakest low-temperature tolerance maize inbred lines includes:

[0027] A segregating population was constructed using the strongest and weakest low-temperature resistant maize inbred lines as the basic data.

[0028] Extreme progeny data of the separated population were obtained by screening the extreme progeny through indoor low-temperature resistance identification.

[0029] The genomes of the extreme offspring data were resequencing using cluster separation analysis to obtain quantitative trait loci information on low-temperature tolerance during maize germination.

[0030] Optionally, the step of obtaining two sets of ribonucleic acid data for the strongest and weakest low-temperature resistant maize inbred lines before and after germination-stage low-temperature stress treatment includes:

[0031] Obtain the initial ribonucleic acid data of the strongest and weakest low-temperature tolerant maize inbred lines before low-temperature stress treatment during germination;

[0032] The strongest and weakest low-temperature resistant maize inbred lines were subjected to low-temperature stress treatment during germination to obtain target ribonucleic acid data of the strongest and weakest low-temperature resistant maize inbred lines after low-temperature stress treatment during germination.

[0033] Optionally, the step of obtaining the change data of the strongest and weakest low-temperature tolerant maize inbred lines before and after germination period low-temperature stress treatment includes:

[0034] By comparing the initial ribonucleic acid data and the target ribonucleic acid data, differentially expressed ribonucleic acid data and significantly enriched metabolic pathway data of the strongest and weakest low-temperature resistant maize inbred lines before and after low-temperature stress treatment during germination were obtained.

[0035] The differentially expressed ribonucleic acid data and the significantly enriched metabolic pathway data are used as the change data.

[0036] Optionally, the step of determining candidate regions for low-temperature tolerance during maize germination based on the consistent quantitative trait locus information and the low-temperature tolerance quantitative trait locus information during maize germination, and mining candidate genes with significantly differential expression in response to low temperature based on the change data, includes:

[0037] Genes with significantly differentially expressed transcriptomes located at the homogeneous quantitative trait loci and the maize germination period low-temperature tolerance quantitative trait loci were compared and screened.

[0038] The differentially expressed genes in the transcriptome were selected as candidate genes for differential expression in response to low temperature.

[0039] Optionally, prior to the step of breeding maize based on the candidate gene, the method further includes:

[0040] The candidate genes that showed significant differential expression in response to low temperature were validated.

[0041] Optionally, the reference map is the IBM 2 2008 Neighbors reference map for corn.

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart of a maize breeding method based on MAS technology provided in an embodiment of this application is shown;

[0045] Figure 2 This application illustrates a technical roadmap for a maize breeding method based on MAS technology, as provided in an embodiment of this application.

[0046] Figure 3 This paper illustrates a flowchart of the resequencing experiment in the maize breeding method based on MAS technology provided in an embodiment of this application.

[0047] Figure 4 A schematic diagram of the MAPK cascade signal system in the maize breeding method based on MAS technology provided in this application embodiment is shown. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0049] First, the applicable scenarios for this application will be introduced. This application can be applied to maize breeding and cultivation scenarios.

[0050] Studies have found that, since most genes controlling crop stress resistance traits are quantitative traits, and due to the limitations of QTL mapping, MAS technology has not yet been widely used in maize breeding practices.

[0051] Based on this, this application provides a maize breeding method based on MAS technology. Using IBM22008Neighbors as a reference map, 295 maize low-temperature tolerance-related QTLs were integrated through meta-analysis to obtain "consistent QTLs" with large effect values ​​that can be detected under different conditions. The relationship between the effect and location of maize low-temperature tolerance-related QTLs was evaluated, which provides strong support for the future development of molecular markers that can be applied to MAS technology in their corresponding segments and greatly improves the efficiency of MAS-assisted breeding.

[0052] The advantages of applying meta-analysis to the MAS (Magnetic Synthesis) of maize's cold tolerance trait are as follows: Due to various limitations, a single QTL mapping is unlikely to obtain all target QTLs, and the reliability of QTL phenotypic contribution rates is poor. This study uses meta-analysis to integrate previous maize cold tolerance-related QTLs, providing more reliable molecular markers for MAS. Typically, genetic maps constructed by QTL mapping have low marker density, limiting their reference value for fine mapping and resulting in low MAS efficiency. This study integrates previous results to construct an integrated map of maize cold tolerance-related QTLs. This map is rich in molecular markers, and with the help of meta-analysis, MQTLs that can be detected in different QTL mappings can be obtained. By developing molecular markers closely linked to MQTLs, the efficiency of MAS can be improved.

[0053] like Figure 1 As shown in the figure, the maize breeding method based on MAS technology provided in this application includes:

[0054] S101. Mark the existing quantitative trait loci information of maize cold tolerance onto the reference map, and obtain the uniform quantitative trait loci of maize cold tolerance through meta-analysis of the marked reference map.

[0055] S102. Select the strongest and weakest low-temperature resistant maize inbred lines from a preset number of maize inbred lines, and obtain the low-temperature resistance quantitative trait locus information of maize during germination based on the strongest and weakest low-temperature resistant maize inbred lines.

[0056] S103. Obtain two sets of ribonucleic acid data of the strongest low-temperature resistant maize inbred line and the weakest low-temperature resistant maize inbred line before and after the low-temperature stress treatment during the germination period, and obtain the change data of the strongest low-temperature resistant maize inbred line and the weakest low-temperature resistant maize inbred line before and after the low-temperature stress treatment during the germination period.

[0057] S104. Based on the consistent quantitative trait locus information and the low temperature tolerance quantitative trait locus information during maize germination, determine the candidate region for the low temperature tolerance trait during maize germination, and mine candidate genes with significantly different expression in response to low temperature based on the change data.

[0058] S105. Maize breeding is carried out based on the candidate genes.

[0059] For example, the method includes: meta-analysis of maize low-temperature tolerance-related QTLs; identification of maize low-temperature tolerance during germination; BSA resequencing analysis of maize low-temperature resistance; transcriptome analysis of maize's response to low-temperature stress during germination; and discovery and validation of candidate genes related to low-temperature response during maize germination.

[0060] For example, such as Figure 2 As shown, using the maize IBM2 2008 Neighbors as a reference map, BioMercator 2.1 software was used to perform meta-analysis on 295 maize low-temperature trait-related QTLs, obtaining maize low-temperature tolerance consistent QTLs. 296 superior maize inbred lines from both domestic and international sources were continuously tested for low-temperature tolerance in the field and in the laboratory for many years. Relative emergence index, relative germination rate, and relative germination index were statistically analyzed to screen for extreme materials that showed stable performance under low-temperature stress conditions in both the field and in the laboratory. Using the screened strongly low-temperature tolerant maize inbred line R144 (the strongest low-temperature tolerant maize inbred line) and the extremely weakly low-temperature tolerant maize inbred line S319 (the weakest low-temperature tolerant maize inbred line) as parents, segregating populations were constructed. Indoor low-temperature tolerance testing was used to screen extreme progeny. Cluster segregation analysis was employed to perform genome resequencing (BSA-seq) on DNA pools of parents, low-temperature tolerant, and low-temperature sensitive extreme progeny individuals. The sequencing results were analyzed to locate low-temperature tolerance-related QTLs during maize germination. R144 and S319 maize germination stage low-temperature stress was applied. RNA was extracted from R144 and S319 before and after treatment and sequenced (RNA-seq). Sequencing results were analyzed to screen for differentially expressed genes and significantly enriched metabolic pathways in maize resistant to extreme conditions before and after low-temperature stress treatment. Combined analysis of meta-analysis, BSA-seq, and RNA-seq results was used to identify candidate genes with significantly differentially expressed low-temperature responses within candidate regions related to low-temperature tolerance during maize germination stage obtained from meta-analysis and BSA-seq. Sequence analysis and expression pattern validation were then performed on these candidate genes.

[0061] In one possible implementation, the step of annotating existing maize low-temperature tolerance quantitative trait loci information onto a reference map and obtaining maize low-temperature tolerance uniform quantitative trait loci through meta-analysis of the annotated reference map includes:

[0062] Obtain existing quantitative trait locus information for maize's low-temperature tolerance, wherein the quantitative trait locus information includes the name of the quantitative trait locus, the chromosome of the quantitative trait locus, the LOD value of the quantitative trait locus, the population type of the quantitative trait locus, and the size of the quantitative trait locus.

[0063] Construct an original map of quantitative trait loci information based on existing quantitative trait loci information for maize's low-temperature tolerance;

[0064] The original map of the quantitative trait loci information is compared with the reference map, and the original map of the quantitative trait loci information is adjusted accordingly.

[0065] The original quantitative trait locus information was proportionally labeled onto the reference map using a homogeneous function;

[0066] Meta-analysis of the annotated reference map yielded quantitative trait loci for maize's low-temperature tolerance.

[0067] In one possible implementation, the step of obtaining the uniform quantitative trait loci of maize cold tolerance through the reference map annotated by meta-analysis includes:

[0068] The location of the quantitative trait loci on the chromosome was determined by meta-analysis of the annotated reference map;

[0069] The quantitative trait locus at the determined location is taken as the consistent quantitative trait locus.

[0070] For example, the collection and organization of QTL information related to low-temperature tolerance in maize:

[0071] QTL information related to cold tolerance in maize was downloaded and collected from the PubMed database and the MaizeGDB website, including QTL name, chromosome, LOD value, population type, and size. A total of 295 QTL records related to cold tolerance in maize from 14 mapping populations over the past 20 years were downloaded and collected from the PubMed database and the MaizeGDB website. Population types included RIL, DH, and F. 2:3 and F 2:4 The QTL positioning method is mainly CIM (composite interval mapping).

[0072] If the QTL confidence interval is not given in the literature, the 95% confidence interval can be inferred using the following formula:

[0073] CI = 530 / (N×R) 2 (1)

[0074] CI = 163 / (N×R) 2 (2)

[0075] Where CI refers to the confidence interval, N refers to the population size, and R... 2 The genetic contribution rate is used. Formula (1) applies to backcrosses and F2 populations, while formula (2) applies to recombinant inbred line populations.

[0076] Processing of QTL information related to low-temperature tolerance in maize:

[0077] The original QTL map was compared with the maize IBM 2 2008 Neighbors reference map. If the QTL end marker was shared by the original map and IBM 2 2008 Neighbors, the position of that marker in IBM 2 2008 Neighbors was recorded. If the QTL end marker was not shared by the original map and IBM 2 2008 Neighbors, the position of the adjacent shared markers in the original map in IBM 2 2008 Neighbors was recorded. QTLs that could not be mapped were deleted. If the QTL end marker was reversed in the original map and IBM 2 2008 Neighbors, the position of that marker in the original map needed to be swapped. The 295 located maize low-temperature tolerance related QTLs were sorted by MapName, QTLName, Chromosome, LODScore, and R. 2 The SMpositions from and to were loaded into their respective original maps, and the mapping function was used to map the original QTLs onto IBM 22008Neighbors. The mapped QTLs covered the entire genome, and many densely populated QTL regions with overlapping intervals were observed. QTLs whose intervals did not share a common marker with the reference map and could not be mapped were discarded. Ultimately, 204 QTLs were successfully mapped. The vertical line on the left side of the chromosome represents the QTL confidence interval, and the horizontal line represents the LOD value.

[0078] Mapping of low-temperature tolerance-related QTLs in maize:

[0079] Mapping refers to using a homogeneous function to proportionally annotate the location and end markers of the original QTLs onto a reference map. The mapping function of BioMercator 2.1 software was used to map the collected maize cold tolerance-related QTLs onto IBM 22008Neighbors: ① Load the original map and relevant markers from the IBM 22008Neighbors reference map; ② Enter the detailed information of each QTL in the original map; ③ Apply the mapping function of BioMercator 2.1 software to map the QTLs from the original map onto IBM 22008Neighbors. For single-marker QTLs, their 95% confidence intervals need to be calculated using a formula, and their locations need to be annotated. Maize cold tolerance is a complex quantitative trait, exhibiting multi-gene regulation and is easily influenced by the environment. Previous studies, due to differences in mapping populations and statistical methods, have located maize cold tolerance-related QTLs scattered across 10 chromosomes. In the meta-analysis, this study also observed that low-temperature tolerance-related QTLs were widely distributed across all 10 chromosomes. Furthermore, these QTLs exhibited clustered distribution. The results validate the QTL enrichment phenomenon in maize, likely due to pleiotropic effects. These findings provide a reference for identifying regions rich in maize stress-resistance genes and offer guidance for future MAS (Magnetic Syndrome Assay) practices.

[0080] Meta-analysis of low-temperature tolerance QTLs in maize:

[0081] Meta-analysis using BioMercator 2.1 software can calculate five models, with the model having the lowest AIC value generally considered the "consensual QTL" (MQTL). Using the software's meta-analysis function, 47 MQTLs related to maize's low-temperature tolerance and their interval markers were obtained. The 95% confidence intervals for MQTLs ranged from 0.04 cM to 102.73 cM, the number of original QTLs ranged from 3 to 14, and the average genetic contribution rate ranged from 3.32% to 14.32%.

[0082] Each QTL model is calculated to determine its most likely location on the chromosome using Gauss's theorem, as shown in the following formula:

[0083] var(QTL)=1 / ∑1 / σi 2

[0084] σi 2 The formula for calculating the 95% confidence interval for the QTL location variance is as follows:

[0085] CI = 3.92 × var(QTL) 1 / 2

[0086] The obtained MQTLs are integrations of results from multiple experiments under different genetic backgrounds, and can be used to average R.2 To evaluate MQTL's R 2 .

[0087] In one possible implementation, the step of selecting the strongest and weakest cold-resistant maize inbred lines from a preset number of maize inbred lines includes:

[0088] Obtain experimental data of the predetermined number of maize inbred lines, wherein the experimental data are field and indoor low-temperature tolerance identification data of the predetermined number of maize inbred lines for at least two consecutive years;

[0089] Based on the low-temperature resistance identification data, the growth data of the preset number of maize inbred lines are statistically analyzed, wherein the growth data includes relative emergence index, relative germination rate and relative germination index;

[0090] The strongest and weakest cold-resistant maize inbred lines were determined based on the relative emergence index, the relative germination rate, and the relative germination index.

[0091] For example, a field test was conducted to assess the low-temperature tolerance of maize. The soil was neutral black calcareous soil, and the previous crop was maize. During autumn tillage, 150 kg / hm² of diammonium phosphate and 50 kg / hm² of potassium chloride were applied. The field test to assess the low-temperature tolerance of maize seedlings was conducted in two stages. The first stage involved sowing at a stable soil temperature above 5℃ at a depth of 5-10 cm, followed by sowing at a suitable temperature when the soil temperature was stable above 10℃. Sprinkler irrigation was applied promptly after both sowing stages. A randomized block design was used, with two rows per block, each 5 m long, with a row spacing of 65 cm. Single seeds were sown, and the plant spacing was 20 cm. Each treatment was replicated three times.

[0092] During the experiment, meteorological data were collected daily from observation points near the experimental field, including soil temperature, maximum field temperature, minimum field temperature, and daily average temperature. After emergence, the number of seedlings was recorded daily. Once emergence ceased, the field emergence rate was tallied, and the relative emergence rate and relative emergence index were calculated.

[0093] Select uniformly sized corn seeds and soak them in a 0.5% NaClO solution for 5 minutes. Then rinse the seeds three times with tap water. Place the seeds in a petri dish lined with filter paper, ensuring the filter paper is thoroughly moistened but not waterlogged. Germinate the seeds in an artificial climate chamber at 5℃ for 4 days, then transfer them to a 25℃ artificial climate chamber for 3 days. Calculate the germination rate, relative germination rate, and relative germination index. Ensure adequate moisture and stable temperature. The experiment was conducted in triplicate, with 100 seeds per replicate. The control group was cultured in a 25℃ artificial climate chamber for 7 days.

[0094] Germination rate (%) = (Number of germinated seeds / Number of tested seeds) × 100

[0095] Relative germination rate (%) = (germination rate under low temperature treatment / germination rate at suitable temperature) × 100

[0096] Emergence index = ∑Gt / Dt Gt: Number of seedlings on day t, Dt: Corresponding number of days since emergence

[0097] Relative emergence index (%) = (emergence index under low temperature treatment / emergence index under suitable temperature sowing) × 100.

[0098] The average emergence rate of 296 maize inbred lines sown at suitable temperatures was 86.87%, while the average emergence rate sown at low temperatures was 62.48%, indicating that low temperature stress caused a decrease in the average emergence rate. Based on relative emergence rates, the 296 maize inbred lines were divided into five levels: strongly cold-resistant maize inbred lines (relative emergence rate above 86%), moderately cold-resistant maize inbred lines (relative emergence rate 70–85%), moderately weakly cold-resistant maize inbred lines (relative emergence rate 55–69%), weakly cold-resistant maize inbred lines (relative emergence rate 36–54%), and extremely weakly cold-resistant maize inbred lines (relative emergence rate 0–35%). Twenty-five materials, including R144, LX71, LX111, and LX107, were identified as strongly cold-resistant maize inbred lines with relative emergence rates above 86%; LX35, LX1, LX27, and S319 had relative emergence rates of 0–35%, and were identified as extremely weakly cold-resistant maize inbred lines. Maize inbred lines that ranked in the top 25% and above 85% of the field relative emergence index for more than two consecutive years were screened to obtain 26 maize inbred lines with stable relative emergence index across different years, good uniformity in cold tolerance, and strong field cold tolerance, including LX207, LX16, R144, and LX131.

[0099] Due to the non-reproducible nature of field identification trials, results vary from year to year. Combining field relative emergence rate and field relative emergence index for comprehensive screening can significantly reduce the impact of uncontrollable factors. This study identified 16 strongly cold-tolerant maize inbred lines, including LX131, R144, LX116, and LX208, and 6 extremely weakly cold-tolerant maize inbred lines, including LX35, S319, and LH27, through field identification using both indicators.

[0100] In one possible implementation, the step of obtaining the quantitative trait locus information of maize germination-stage cold tolerance based on the strongest and weakest cold-tolerant maize inbred lines includes:

[0101] A segregating population was constructed using the strongest and weakest low-temperature resistant maize inbred lines as the basic data.

[0102] Extreme progeny data of the separated population were obtained by screening the extreme progeny through indoor low-temperature resistance identification.

[0103] The genomes of the extreme offspring data were resequencing using cluster separation analysis to obtain quantitative trait loci information on low-temperature tolerance during maize germination.

[0104] For example, the average relative germination rates of 296 maize inbred lines were 83.45%, 86.73%, and 85.46% respectively over three years, showing a relatively consistent average germination rate over the three years. Based on the average relative germination rate, the 296 maize inbred lines were divided into five levels: strongly cold-resistant maize inbred lines (relative germination rate above 86%), moderately cold-resistant maize inbred lines (relative germination rate 70–85%), moderately weakly cold-resistant maize inbred lines (relative germination rate 55–69%), weakly cold-resistant maize inbred lines (relative germination rate 36–54%), and extremely weakly cold-resistant maize inbred lines (relative germination rate 0–35%). 119 strongly cold-resistant maize inbred lines with a relative germination rate above 86% and 24 extremely weakly cold-resistant maize inbred lines with a relative germination rate of 0–35% were identified. Maize inbred lines that ranked in the top 25% and above 85% of the indoor relative germination index for more than two consecutive years were screened. Eight maize inbred lines with stable relative germination index across different years, good uniformity in low-temperature tolerance, and strong indoor low-temperature tolerance were obtained, including LX163, LX101, LX131, and R144. Eight maize inbred lines with extremely weak indoor low-temperature tolerance were obtained, namely LX100, LX39, S319, and LX87.

[0105] The correlation between low-temperature tolerance traits in field emergence period and indoor germination period was analyzed. Significant correlations were found between the indoor relative germination rate in 2017 and the indoor relative germination rate in 2018, 2019, and field relative emergence rate, with correlation coefficients of 0.53, 0.61, and 0.32, respectively. Significant correlations were also found between the indoor relative germination rate in 2018 and the indoor relative germination rate in 2019, with correlation coefficients of 0.59 and 0.25, respectively. A significant correlation was also found between the indoor relative germination rate and the field relative emergence rate in 2019, with a correlation coefficient of 0.16. The field relative emergence rate was significantly correlated with the field relative emergence index in 2017 and 2018, with correlation coefficients of 0.62 and 0.54, respectively. Among the significantly correlated indicators, the field relative emergence rate had the largest correlation coefficient with the field relative emergence index in 2017 (0.62); the indoor relative germination rate had the smallest correlation coefficient with the field relative emergence rate in 2019 (0.16).

[0106] The combined results of field and indoor low-temperature tolerance assessments revealed inconsistencies in the low-temperature tolerance of different maize inbred lines. Some inbred lines exhibited strong low-temperature tolerance indoors but only moderate tolerance in the field. This is primarily because indoor low-temperature tolerance tests are subject to controllable stress conditions, focusing mainly on the germination ability of maize inbred lines under low-temperature stress. In contrast, field low-temperature tolerance tests involve more influencing factors, such as soil moisture content, sowing depth, compaction intensity, and seed emergence ability. Based on the combined results of field and indoor low-temperature tolerance tests, 10 strongly low-temperature tolerant maize inbred lines (R144, LX85, XZD270, and LX174) were selected, along with 4 extremely weakly low-temperature tolerant maize inbred lines (LH27, S319, LX120, and LX83).

[0107] For example, the identification of low-temperature tolerance during the germination period of maize in indoor environments and the screening of individuals with extreme phenotypes:

[0108] The harvested F2 single-plant ears of seeds were used for indoor identification of maize low-temperature resistance. The germination rate of F2 single-plant ears of seeds under indoor low-temperature stress was counted, and individuals with extreme phenotypes in the F2 population were determined based on the germination rate results.

[0109] For example, such as Figure 3 As shown, the modified CTAB method was used to extract genomic DNA from the extreme phenotype individuals and young leaves of the parental strong low-temperature tolerant maize inbred line R144 and the extremely weak low-temperature tolerant maize inbred line S319, as well as the constructed F2 population. The concentration and purity of the DNA were detected. The qualified extreme phenotype individuals' DNA was mixed in equal amounts and used to construct the progeny resistance DNA pool.

[0110] The DNA to be sequenced was randomly fragmented to a size of approximately 350 bp, the ends of the fragments were modified, a sequencing library was constructed, and the DNA was resequencing using an Illumina HiSeq scanner.

[0111] Specifically, including:

[0112] Data quality control. Raw data is filtered, and quality is assessed based on factors such as base sequencing quality distribution and base type distribution. Once the assessment is satisfactory, the raw data is further filtered to remove sequences containing adapters and low-quality sequences, thereby obtaining sequences without redundancy.

[0113] Alignment with the reference genome. Locate the non-redundant sequence to the maize reference genome (B73RefGen-v4) using bwa software to locate the position of the non-redundant sequence on B73RefGen-v4.

[0114] SNPs and Small InDels were detected and annotated. GATK software was used to detect SNPs and Small InDels. Based on the location results of non-redundant sequences in B73 RefGen-v4, Picard deduplication and GATK re-alignment were used to detect and filter SNPs and Small InDels. SnpEff software was used to annotate and predict the obtained SNPs and Small InDels. Based on the location information of SNPs and Small InDels in B73 RefGen-v4, the regions where SNPs and Small InDels were located were identified, and it was analyzed whether SNP and Small InDel variants resulted in synonymous mutations.

[0115] Association analysis of SNPs and Small InDel. Low-quality SNPs and Small InDel values ​​were filtered out. The Euclidean distance (ED) method was used to calculate ED values, and the DISTANCE method was used to fit the ED values. The median + 3SD of the fitted values ​​was selected as the association threshold for determining the associated regions. The SNP-index and InDel-index methods were used to screen for markers with significantly different genotype frequencies among pooled samples. The DISTANCE method was used to fit ΔSNP-index and ΔInDel-index, and regions above the association threshold were selected as associated regions related to the low-temperature tolerance trait during maize germination. The intersection of the association region results obtained from the ED algorithm, SNP-index, and InDel-index methods was used to determine the candidate regions related to the low-temperature tolerance trait during maize germination.

[0116] SNP and InDel functional annotation were performed. Non-synonymous and frameshift mutations in candidate regions related to low-temperature tolerance during maize germination were screened, and BLAST software was used for annotation in databases such as NR, GO, KEGG, and COG.

[0117] In one possible implementation, the step of obtaining two sets of ribonucleic acid data for the strongest and weakest cold-resistant maize inbred lines before and after germination-stage low-temperature stress treatment includes:

[0118] Obtain the initial ribonucleic acid data of the strongest and weakest low-temperature tolerant maize inbred lines before low-temperature stress treatment during germination;

[0119] The strongest and weakest low-temperature resistant maize inbred lines were subjected to low-temperature stress treatment during germination to obtain target ribonucleic acid data of the strongest and weakest low-temperature resistant maize inbred lines after low-temperature stress treatment during germination.

[0120] In one possible implementation, the step of obtaining the change data of the strongest and weakest cold-resistant maize inbred lines before and after germination-stage low-temperature stress treatment includes:

[0121] By comparing the initial ribonucleic acid data and the target ribonucleic acid data, differentially expressed ribonucleic acid data and significantly enriched metabolic pathway data of the strongest and weakest low-temperature resistant maize inbred lines before and after low-temperature stress treatment during germination were obtained.

[0122] The differentially expressed ribonucleic acid data and the significantly enriched metabolic pathway data are used as the change data.

[0123] In one possible implementation, the step of determining candidate regions for low-temperature tolerance during maize germination based on the consistent quantitative trait locus information and the low-temperature tolerance quantitative trait locus information during maize germination, and mining candidate genes with significantly differentially expressed responses to low temperatures based on the change data, includes:

[0124] Genes with significantly differentially expressed transcriptomes located at the homogeneous quantitative trait loci and the maize germination period low-temperature tolerance quantitative trait loci were compared and screened.

[0125] The differentially expressed genes in the transcriptome were selected as candidate genes for differential expression in response to low temperature.

[0126] For example, total RNA from maize was extracted and purified, and then reverse transcribed into cDNA following the operating steps of TaKaRa's Reverse Transcriptase M-MLV (RNase H-) Reverse Transcriptase Kit.

[0127] Four DEGs were randomly selected for qPCR validation of transcriptome sequencing results. qRT-PCR primers were designed using Beacon Designer software. Maize actin1 was used as an internal reference gene, and the reaction conditions were 95℃ for 30 sec; 95℃ for 5 sec, 60℃ for 30 sec, 40 cycles. The relative gene expression levels were calculated using the 2-ΔΔCT method, and variance and significance analysis were performed using SPSS Statistics v23.0 software.

[0128] Specifically, GO functional enrichment analysis of differentially expressed genes:

[0129] GO function enrichment analysis was performed on DEGs using clusterProfiler software. The results showed that after 12 h of low-temperature stress treatment, 1770 DEGs were annotated with functions in the strongly low-temperature tolerant maize inbred line R144 and the extremely weakly low-temperature tolerant maize inbred line S319. Among them, 792, 690, and 288 DEGs were annotated with BP, CC, and MF functions, respectively. After 24 h of low-temperature stress treatment, 1397 DEGs were annotated with functions in the strongly low-temperature tolerant maize inbred line R144 and the extremely weakly low-temperature tolerant maize inbred line S319. Among them, 681, 242, and 474 DEGs were annotated with BP, CC, and MF functions, respectively.

[0130] Based on the significance of the annotated GO functions, we screened the GO entries that were significantly enriched (p-value ≤ 0.01, padj ≤ 0.01), and obtained a total of 27 significantly enriched GO annotation categories, including 5 biological process functions, 10 cellular component functions, and 12 molecular functions. Ten common and significantly enriched GO annotation categories were found for low-temperature stress treatment at different times for the low-temperature tolerant maize inbred line R144 and the extremely low-temperature tolerant maize inbred line S319. These categories were: bioprocess function (peptide metabolism, transformation, peptide biosynthesis, amide metabolism, amide biosynthesis); cellular composition (ribosomes, ribonucleoprotein complex, ribonucleoprotein complex, ribonucleoprotein complex); and molecular function (ribosome structure, ribosome activity, ribosome structure ... Seven GO annotation categories—non-membranous organelles (GO:0043228), intracellular non-membranous organelles (GO:0043232), proteasome core complex (GO:0005839), proteasome complex (GO:0000502), endopeptidase complex (GO:1905369), peptidase complex (GO:1905368), proteasome core complex, and alpha subunit complex (GO:0019773)—were significantly enriched only during 12 hours of low-temperature stress treatment. In contrast, only two of the twelve GO annotation categories related to molecular function were significantly enriched across different stress treatment times. This suggests that there are certain differences in the functional classification of DEGs (determinants) in the response of the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319 to low-temperature stress at different time points.

[0131] KEGG functional enrichment analysis of differentially expressed genes, such as Figure 4 As shown:

[0132] To clarify the metabolic pathways involved by DEGs in the response of the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319 to low-temperature stress, KEGG annotation was performed on the DEGs of R144 and S319 in response to low-temperature stress. Five KEGG pathways, including the plant MAPK signaling pathway, plant diurnal rhythm, and plant hormone signal transduction, were significantly enriched. The results showed that both the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319 were significantly affected by low-temperature stress, including adaptation pathways and signal transduction pathways. This indicates that plants activate corresponding metabolic pathways in response to external abiotic stress, and the differences in resistance among different materials may stem from differences in the metabolic pathways involved and the number or expression levels of genes involved in those pathways.

[0133] Plants typically stimulate gene expression and protein synthesis through plant signal transduction pathways, prompting them to initiate positive responses to adapt to adverse environments or enhance their stress resistance. In this study, under low-temperature stress treatment, multiple DEGs in the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319 were significantly enriched in pathways related to signal transduction. Among them, 68 DEGs were annotated to the plant MAPK signaling pathway, and 106 DEGs were annotated to the plant hormone signal transduction pathway.

[0134] The mitogen-activated protein kinase (MAPK) signaling pathway is highly conserved in plants. Its function is to amplify signals sensed on the receptor cell membrane and transmit them into the cell, stimulating changes in intracellular transcription and metabolic levels. After 12 h and 24 h of low-temperature stress treatment, 18 and 32 significant DEGs (determinants) of the plant MAPK signaling pathway were annotated in the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319, respectively. During low-temperature stress treatment, among all DEGs annotated in the MAPK signaling pathway-plant pathway, most DEGs were co-differentially expressed in both the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319. Furthermore, the expression trends of these co-differentially expressed DEGs were largely consistent between the two lines. Among the specific DEGs in both lines, the expression levels of three genes—LOC103651289, LOC103647946, and LOC100285223—showed the most significant changes, encoding MAP3K, ethylene-insensitive protein 3, and protein phosphatase, respectively.

[0135] Plant hormones are a class of trace organic substances produced by plant metabolism, playing a crucial role in plant adaptation to abiotic stress. After 12 h and 24 h of low-temperature stress treatment, 25 and 41 significant DEGs (determinants) in the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319, respectively, were annotated to the plant hormone signal transduction pathway. Under low-temperature stress, the extremely low-temperature tolerant maize inbred line S319 had more DEGs involved in the plant hormone signal transduction pathway compared to the strongly low-temperature tolerant maize inbred line R144. Furthermore, the DEGs involved in this pathway in the extremely low-temperature tolerant maize inbred line S319 responded to low-temperature stress faster than the related genes in the strongly low-temperature tolerant maize inbred line R144. In other words, after 12 hours of low-temperature stress, more DEGs in S319 were involved in the regulation of the plant hormone signal transduction pathway, among which the expression levels of the genes PYL5 and LOC103647946 changed most significantly.

[0136] In one possible implementation, prior to the step of breeding maize based on the candidate gene, the method further includes:

[0137] The candidate genes that showed significant differential expression in response to low temperature were validated.

[0138] For example, using HISAT2 The software compared the non-redundant sequences with B73RefGen-v4. The results showed that the success rate of matching the sequences with B73RefGen-v4 was over 85% for all 18 samples, indicating that the sequencing data was consistent with B73RefGen-v4, there was no contamination in the related experiments, and the sequencing data could be used for the next step of data analysis.

[0139] qRT-PCR validation of RNA-seq results:

[0140] Differentially expressed genes in response to low temperature in the strongly cold-tolerant maize inbred line R144 and the extremely weakly cold-tolerant maize inbred line S319 were randomly selected for qRT-PCR validation (LOC100273598, LOC103651289, LOC103645931, LOC103652527, with the maize UBI gene as the internal reference). The results showed that the trends in the expression levels of the four genes were basically consistent with the RNA-seq results, thus validating the accuracy of RNA-seq.

[0141] For example, based on the "consistent QTLs" related to low temperature tolerance in maize obtained from meta-analysis, combined with candidate regions related to low temperature tolerance traits during maize germination obtained from BSA resequencing, and differentially expressed genes screened by transcriptome sequencing, differentially expressed genes in transcriptomes located within the candidate regions related to low temperature tolerance obtained from meta-analysis and BSA resequencing are compared and screened, and these genes are identified as candidate genes related to low temperature response during maize germination.

[0142] In one possible implementation, the reference map is the maize IBM 2 2008 Neighbors reference map. IBM2 2008 Neighbors is a public map based on the segregating population of B73 × Mo17, in which the parental maize inbred line B73 has undergone whole-genome sequencing and was used to construct the physical map. It exhibits high recombination rate and resolution, making it an excellent population for detecting low-efficiency QTLs. The map is 8054.28 cM long and contains 15991 markers of various types. It shares many markers with completed or currently being mapped QTL mapping maps, which can improve the accuracy of MQTLs.

[0143] Using the MAS-based maize breeding method, 47 homogeneous QTLs related to low-temperature tolerance in maize were obtained, with confidence intervals ranging from 0.04 cM to 102.73 cM. The original number of QTLs ranged from 3 to 14, and the average genetic contribution rate ranged from 3.32% to 14.32%. Ten strongly low-temperature tolerant maize inbred lines, including R144, LX85, XZD270, and LX174, and four extremely weakly low-temperature tolerant maize inbred lines, including LH27, S319, LX120, and LX83, were selected. Resequencing yielded 221.72 Gbp of data with an average sequencing depth of 25.96X, locating four candidate regions related to low-temperature tolerance during maize germination, with a total length of 25.71 Mb. 1513 genes were annotated, including 456 non-synonymous mutant genes and 111 frameshift mutant genes. After 12 hours of low-temperature treatment, 1741 and 1165 specific DEGs were found between R144 and S319, respectively; after 24 hours of low-temperature treatment, 2109 and 1808 specific DEGs were found between R144 and S319, respectively. GO analysis yielded 27 significantly enriched annotation categories. KEGG analysis revealed significant enrichment of five metabolic pathways, including the plant MAPK signaling pathway, plant diurnal rhythm, and plant hormone signal transduction pathway, in both inbred lines. Two candidate genes with significantly differential expression in response to low temperature were identified within the candidate regions related to low-temperature tolerance during maize germination obtained from meta-analysis and BSA-seq: MAPKKK17 and MAP3K A-like genes. qRT-PCR validation of these two candidate genes demonstrated highly significant differences in their expression patterns between the extreme materials R144 and S319 after low-temperature treatment.

[0144] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for corn breeding based on MAS technology, characterized in that, The application relates to a corn low-temperature-tolerance quantitative trait locus information processing method. The application comprises the following steps: annotating existing corn low-temperature-tolerance quantitative trait locus information to a reference map, obtaining corn low-temperature-tolerance consistent quantitative trait loci through Meta analysis of the annotated reference map; selecting the strongest corn inbred line and the weakest corn inbred line in a preset number of corn inbred lines, and obtaining corn germination stage low-temperature-tolerance quantitative trait locus information based on the strongest corn inbred line and the weakest corn inbred line; obtaining two ribonucleic acid data of the strongest corn inbred line and the weakest corn inbred line before and after low-temperature stress treatment in the germination stage, and obtaining change data of the strongest corn inbred line and the weakest corn inbred line before and after low-temperature stress treatment in the germination stage; determining a corn germination stage low-temperature-tolerance trait candidate region based on the consistent quantitative trait locus information and the corn germination stage low-temperature-tolerance quantitative trait locus information, and mining a low-temperature-significantly-differentially-expressed candidate gene according to the change data; 2. The MAS technology based corn breeding method according to claim 1, wherein, carrying out corn breeding according to the candidate gene. The step of annotating existing corn low-temperature-tolerance quantitative trait locus information to a reference map, and obtaining corn low-temperature-tolerance consistent quantitative trait loci through Meta analysis of the annotated reference map, comprises the following steps: obtaining existing corn low-temperature-tolerance quantitative trait locus information, wherein the quantitative trait locus information is quantitative trait locus information name, quantitative trait locus information chromosome, quantitative trait locus information LOD value, quantitative trait locus information population type and quantitative trait locus information size; constructing a quantitative trait locus information original map based on the existing corn low-temperature-tolerance quantitative trait locus information; adjusting the quantitative trait locus information original map by comparing the quantitative trait locus information original map with the reference map; annotating the original quantitative trait locus information to the reference map in proportion by using a sequence alignment function; 3. The MAS technology based corn breeding method according to claim 2, wherein, obtaining corn low-temperature-tolerance consistent quantitative trait loci through Meta analysis of the annotated reference map. The step of obtaining corn low-temperature-tolerance consistent quantitative trait loci through Meta analysis of the annotated reference map comprises the following steps: determining the position of the quantitative trait locus on a chromosome through Meta analysis of the annotated reference map; 4. The MAS technology based corn breeding method as claimed in claim 1, wherein, taking the quantitative trait locus with the determined position as the consistent quantitative trait locus. The step of selecting the strongest corn inbred line and the weakest corn inbred line in a preset number of corn inbred lines comprises the following steps: obtaining experimental data of the preset number of corn inbred lines, wherein the experimental data are at least two years of continuous field and indoor low-temperature-tolerance identification data of the preset number of corn inbred lines; statistically obtaining growth data of the preset number of corn inbred lines based on the low-temperature-tolerance identification data, wherein the growth data include relative emergence index, relative germination rate and relative germination index; determining the strongest corn inbred line and the weakest corn inbred line based on the relative emergence index, the relative germination rate and the relative germination index.

5. The MAS technology based corn breeding method as claimed in claim 1, wherein, The step of obtaining the corn germination stage low-temperature tolerance quantitative trait locus information based on the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line comprises the following steps: Taking the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line as basic data, a separation population is constructed; Through indoor low-temperature identification screening, extreme offspring of the separation population are obtained, and extreme offspring data are obtained; Through cluster separation analysis, the extreme offspring data are subjected to genome resequencing, and corn germination stage low-temperature tolerance quantitative trait locus information is obtained.

6. The MAS technology based corn breeding method as claimed in claim 1, wherein, The step of obtaining the two ribonucleic acid data of the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line before and after low-temperature stress treatment during the germination stage comprises the following steps: Initial ribonucleic acid data of the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line before low-temperature stress treatment during the germination stage are obtained; The strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line are subjected to low-temperature stress treatment during the germination stage, and target ribonucleic acid data of the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line after low-temperature stress treatment during the germination stage are obtained.

7. The MAS technology based corn breeding method according to claim 6, wherein, The step of obtaining the change data of the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line before and after low-temperature stress treatment during the germination stage comprises the following steps: The initial ribonucleic acid data and the target ribonucleic acid data are compared, and differential ribonucleic acid data and significantly enriched metabolic pathway data of the strongest low-temperature tolerance corn inbred line and the weakest low-temperature tolerance corn inbred line before and after low-temperature stress treatment during the germination stage are obtained; The differential ribonucleic acid data and the significantly enriched metabolic pathway data are taken as the change data.

8. The MAS technology based corn breeding method as claimed in claim 1, wherein, The step of determining a corn germination stage low-temperature tolerance trait candidate region based on the consistency quantitative trait locus information and the corn germination stage low-temperature tolerance quantitative trait locus information, and mining a low-temperature significantly differential expression candidate gene according to the change data comprises the following steps: Transcriptome significantly differential expression genes located in the consistency quantitative trait locus information and the corn germination stage low-temperature tolerance quantitative trait locus information are screened and compared; The transcriptome significantly differential expression genes are taken as low-temperature significantly differential expression candidate genes.

9. The MAS technology based corn breeding method as claimed in claim 1, wherein, Before the step of breeding corn according to the candidate genes, the following step is further included: The low-temperature significantly differential expression candidate genes are verified.

10. The MAS technology based corn breeding method as claimed in claim 1 wherein, The reference map is the IBM 2 2008 Neighbors reference map of corn.

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