Application of SNP (Single Nucleotide Polymorphism) molecular marker in identifying peanut seed coat color
By applying a combination of SNP molecular marker sites and molecular probes in peanut cultivation, the challenges of peanut seed coat color identification and breeding have been solved, breeding efficiency has been improved, and the development of the peanut cultivation industry has been promoted.
Patent Information
- Application Number
- CN202511047870.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-29
- Publication Date
- 2025-11-07
AI Technical Summary
Existing technologies are insufficient to effectively identify and utilize genes related to peanut seed coat color, thus failing to meet the needs of peanut molecular-assisted breeding.
Using SNP molecular marker sites and combining them with the peanut reference genome, a set of SNP molecular marker sites was screened out, and corresponding molecular probe combinations and detection products were developed for the identification and breeding of peanut seed coat color.
It has significantly improved the peanut variety selection process, increased breeding efficiency, and promoted the healthy development of the peanut planting industry.
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Figure CN120905425A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of biotechnology, and more particularly to the application of SNP molecular markers in identifying peanut seed coat color and / or peanut breeding. BACKGROUND
[0002] Peanut (Arachis hypogaea L.) is an annual dicotyledonous plant in the Leguminosae family, and is not only an important oil and economic crop, but also a characteristic agricultural product with both nutritional value and medicinal function. As a "double" supply source of vegetable oil and high-quality protein, the kernel of peanut contains 44%-56% oil and 22%-32% protein, and is also rich in bioactive substances such as resveratrol and β-sitosterol.
[0003] Seed coat color is one of the important agronomic traits of peanut. In recent years, with the development of functional agriculture and the improvement of people's living standards, characteristic peanut varieties with bright color and rich active ingredients such as anthocyanins have attracted much attention, and the health function of peanuts has become a major attraction for consumers. Different seed coat colors of peanuts increase the new economic value of peanuts and become a research object with development potential. Generally, the seed coat referred to in the research is the inner epidermis of peanut, which is developed from the integument and is rich in nutrients such as flavonoids and resveratrol. The seed coat color can be divided into pink, red, purple, black, purple black, purple red, red white, and color grain, etc. This classification of peanut color is generally based on visual observation, and there are some deficiencies in the accuracy of identification. The use of color space models can help to quantify the classification of peanut seed coat color. Lab model, RGB model and HSV model are the three color models that are currently more widely used.
[0004] Previous studies have shown that different color of peanut seed coat may be controlled by one or two pairs of genes. And the genes that may control the peanut seed coat color to present red, purple and black have been located on chromosome 3, 10, 12 of peanut. Zhao et al. used YH29 and WH10 peanut hybrid combination, located on the 4.7 Mb region of A10 chromosome a single major gene AhTc1 related to peanut purple seed coat, which encodes R2R3-MYB transcription factor. Chen et al. found that the red seed coat was controlled by a single dominant locus AhRt1 in a F2 population constructed by crossing peanut pink seed coat variety Fuhua 8 and red seed coat variety Quanhonghua 1, and located the candidate gene of AhRt1 to a bHLH transcription factor on A03 chromosome. Zhang et al. found a recessive gene named AhRt2 on chromosome 12 that controls the formation of peanut red seed coat by constructing two hybrid populations of pink and red seed coat peanuts, and anthocyanidin reductase gene (Arahy.IK60LM) is the possible candidate gene of AhRt2. Zhao Yuhuan et al. screened a SSR marker pTsaSSR107.16 on chromosome 10 of peanut that is closely related to peanut black seed coat. However, these findings are far from meeting the needs of peanut molecular assisted breeding.
[0005] Therefore, it is an urgent problem for those skilled in the art to mine more peanut seed coat color related regulatory genes or molecular markers for peanut breeding. SUMMARY
[0006] Therefore, the present application provides the application of SNP molecular markers in identifying peanut seed coat color and / or peanut breeding.
[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:
[0008] The application of SNP molecular markers in identifying peanut seed coat color and / or peanut breeding, the physical information of the SNP molecular markers is shown in Table 6, and the physical position information in Table 6 is determined based on the sequence alignment of peanut reference genome arahy.Tifrunner.gnm1.KYV3.genome_main.fna.
[0009] Another purpose of the present application is to provide a molecular probe combination for identifying peanut seed coat color, which is used for detecting the SNP molecular markers shown in Table 6.
[0010] Another purpose of the present application is to provide a detection product for identifying peanut seed coat color, which comprises the above-mentioned molecular probe combination.
[0011] A further object of the present application is to provide a method for identifying the color of peanut seed coat in the seedling stage, which uses the above-mentioned molecular probe combination or the above-mentioned detection product to detect the peanut sample to be tested.
[0012] A further object of the present application is to provide the use of the above-mentioned molecular probe combination or the above-mentioned detection product in peanut breeding.
[0013] Beneficial effects: The present application screens a set of peanut SNP molecular marker sites based on the color phenotype of different peanut varieties and sequencing data combined with the peanut reference genome. The physical position information of the SNP molecular marker sites is shown in Table 6. The application of the SNP sites and detection probes to peanut seed coat color identification can improve the breeding process of peanut varieties, significantly improve the breeding efficiency, and promote the healthy development of peanut planting industry. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.
[0015] Figure 1 The peanut seed coat color processing effect diagram for the embodiments of the present application, wherein A is a peanut picture with an original background, and B is a peanut picture after removing the background.
[0016] Figure 2 The RGB value analysis software used in the embodiments of the present application, wherein Access Key ID and Access Key Secret are the account name and password corresponding to the Ali Vision open account, respectively.
[0017] Figure 3 The classification of different phenotypic peanut colors according to p value for the embodiments of the present application, wherein A, B, C and D represent black, purple, pink and white, respectively.
[0018] Figure 4 The phenotypic distribution of peanut seed coat color related traits in the 499 population in three periods for the embodiments of the present application, wherein A1-A4 represent the frequency distribution histogram of R value, G value, B value and p value of the seed coat color of 499 peanut planted in the Lai Xi test site in 2020; B1-B4 represent the frequency distribution histogram of R value, G value, B value and p value of the seed coat color of 499 peanut planted in the Lai Xi test site in 2022; C1-C4 represent the frequency distribution histogram of R value, G value, B value and p value of the seed coat color of 499 peanut planted in the Lai Xi test site in 2023.
[0019] Figure 5 R, G, B, p-value GWAS results of peanut seed coat color phenotype data for the embodiments of the present application, wherein A-D represent Manhattan plot and QQ-Plot plot of R, G, B, p-value GWAS results of peanut seed coat color phenotype data, respectively.
[0020] Figure 6 Distribution map of 139 SNP sites developed for the embodiments of the present application on the chromosome. DETAILED DESCRIPTION
[0021] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.
[0022] Embodiment 1
[0023] 1. Material planting
[0024] 499 germplasm resources collected and preserved by the research group from all over the world (of which 259 were from the United States, 238 from China, and 2 from Australia, see Table 1) were planted in the Laixi Experimental Station of Peanut Research Institute of Shandong Province in 2020, 2022 and 2023. After mechanical ridging, artificial sowing was carried out in the field, with a ridge width of 85 cm and a ridge height of 20 cm. There were 2 rows per ridge, with a hole distance of 15 cm. Each resource was planted in one ridge, with 5 holes in each row, a total of 10 plants (2 rows x 5 holes), and mulch cultivation. Field management was consistent with general field production.
[0025] Table 1
[0026]
[0027]
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[0029]
[0030]
[0031] 2. Peanut seed coat image acquisition and color phenotype data determination
[0032] Raw image acquisition of peanut seed coat: After harvesting, 5 peanut seeds with uniform appearance and color were taken from each peanut population. The camera was fixed vertically above the shooting object using a tripod and Sony camera. The camera shooting parameters were adjusted to make the camera display consistent with the actual color of the seed coat. Three photos were taken each time, and one photo with correct exposure and accurate color was selected. Adobe Photoshop was used to remove the shooting background to eliminate the influence of the background on the color of the seed coat. The picture was saved as a JEPG format to obtain the raw image of the peanut seed coat (see Appendix Figure 1 ).
[0033] Raw image RGB value extraction of seed coat color: 1497 raw images of peanut seed coat (499 peanut populations in three environments) were taken. Based on the color recognition module in the Ali Vision Intelligent Open Platform (https: / / vision.aliyun.com / imagerecog), the API of the module was called in Python to write software (see Appendix Figure 2 ). The RGB value recognition results (percentage, color, and hexadecimal color code) of the 8 color blocks were output to the Microsoft 365 Excel table. The VBA (Visual Basic for Applications) tool was used to convert the hexadecimal code of the color block to RGB value. The average value of R, G, and B values of each peanut sample was calculated using RGB value and proportion.
[0034] Phenotype data analysis based on RGB and HSV color models: To further eliminate the influence of shooting on the color of peanut seed coat and unify the peanut phenotype data, the RGB value of each sample was converted to HSV value based on the conversion formula (Hamachi T, Tanabe H, Yamawaki A. Development of a generic RGB to HSV hardware [C]. The Proceedings of the 1st International Conference on Industrial Application Engineering 2013. The Institute of Industrial Applications Engineers, 2013: 169-173.). The peanut seed coat color intelligent recognition model developed by Zhu Shuliang et al. (Zhu Shuliang, Zhao Kun, Gao Guqiang, et al. Establishment and application of peanut seed coat color intelligent recognition model [J]. China Oils and Oilseeds, 2022, 44(2): 324-330.) was introduced. The peanut seed coat color index (p) was calculated based on the following formula:
[0035]
[0036] The peanut seed coat color is divided into four categories according to the p value, i.e. black (p value greater than or equal to 0.6000±0.0238), purple (p value between 0.6000±0.0238 and 0.4000±0.0238), pink (p value between 0.4000±0.0238 and 0.2000±0.0238), and white (p value less than or equal to 0.2000±0.0238), and the corresponding phenotypes are shown in the following table. Figure 3 .
[0037] The R, G, B and p values of 499 peanut materials in three environments are arranged in an Excel table as phenotypic data of seed coat color, and a total of 1497 materials corresponding to four phenotypic data are obtained.
[0038] 3、Results
[0039] (1) The RGB values of 499 peanut materials in Table 1 were extracted, the HSV values were converted, and the seed coat color index (p) value was calculated, and a total of four indicators of seed coat color R, G, B and p in three years were obtained, and the descriptive statistical results are as follows.
[0040] Table 2 Statistics of peanut seed coat color traits in Laiwu test site in 2020
[0041]
[0042] In the peanut seed coat color traits in Laiwu test site in 2020, the R value measurement range was 27.77-171.55, the coefficient of variation was 16.42%; the G value measurement range was 27.80-174.70, the coefficient of variation was 18.33%; the B value measurement range was 28.68-158.43, the coefficient of variation was 17.28%; and the p value range was 0.30-0.84, the coefficient of variation was 17.44%.
[0043] Table 3 Statistics of peanut seed coat color traits in Laiwu test site in 2022
[0044]
[0045] In the peanut seed coat color traits in Laiwu test site in 2022, the R value measurement range was 39.01-163.10, the coefficient of variation was 15.69%; the G value measurement range was 34.62-166.85, the coefficient of variation was 17.86%; the B value measurement range was 36.23-150.64, the coefficient of variation was 16.80%; and the p value range was 0.33-0.80, the coefficient of variation was 15.65%.
[0046] Table 4 Peanut seed coat color trait statistics of LaiXi test point in 2023
[0047]
[0048] In the peanut seed coat color traits of LaiXi test point in 2023, the R value measurement range was 41.93-161.22, the coefficient of variation was 14.82%; the G value measurement range was 34.67-162.98, the coefficient of variation was 17.49%; the B value measurement range was 37.75-143.71, the coefficient of variation was 15.42%; the p value range was 0.34-0.79, the coefficient of variation was 16.73%.
[0049] From the coefficient of variation, the coefficients of variation of G value and B value in each year were relatively large, indicating that G value and B value had great improvement potential and rich phenotypic variation in peanut seed coat color traits. In general, the four phenotypic data had large coefficients of variation, and the phenotypic variation was rich.
[0050] At the same time, combined with the frequency distribution histogram of the four peanut seed coat color phenotype data containing R value, G value, B value and color index p value (see attached Figure 4 ), it was observed that each data index met or basically met the characteristics of normal distribution, had quantitative trait characteristics, and was suitable for GWAS analysis of phenotypic data.
[0051] (2) Joint variance analysis of phenotypic data of three environments found that the general genetic force of p and R was 94%, G and B also had high general genetic force, and the effect of environment on seed coat color reached a very significant level (see Table 5), which indicated that the main factor controlling the variation of peanut seed coat color was genetics, and it was also affected by environmental factors.
[0052] Table 5 Variance analysis and general genetic force of seed coat color related traits
[0053]
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[0055] Example 2
[0056] 1. Genotyping of peanut material SNP chip and data analysis
[0057] Genotyping of peanut materials using 48K SNP gene chip. The 48K SNP gene chip (Axiom_Arachis2) was developed by Clevenger et al. (Clevenger JP, Korani W, Ozias-Akins P, Jackson S. Haplotype-based genotyping in polyploids. Front Plant Sci 2018; 9: 564.) and contains 47837 SNPs, which is the second generation of peanut gene chip. Chip hybridization and data acquisition will be completed by Beijing Boao Jingdian Technology Co., Ltd. based on the Affymetrix GeneTitan platform for sequencing. The basic process is as follows:
[0058] Genomic DNA is first denatured, and then the denatured genomic DNA is neutralized before DNA amplification. The amplified product is randomly cut by endonuclease to fragment the DNA into fragments with a molecular weight of 25 to 125 bp. The fragmentation process is terminated after 30 minutes to prevent the molecular weight of the sequencing fragments from being too small; the DNA fragments are precipitated with isopropanol, centrifuged and dried to obtain dry DNA samples; under room temperature conditions, add resuspension buffer to the dry DNA to resuspend the DNA; quality control of the sample to be tested: use a microplate reader to determine the concentration, the OD 260 / OD 280 ratio of the sample concentration measured by the microplate reader is 1.8-2.0; use electrophoresis to determine the molecular weight, the molecular weight measured by electrophoresis is 25-125 bp, the main band is 75-100 bp, and record the unqualified samples; the DNA fragments are hybridized with the 48K SNP gene chip, and the process takes 24 hours; after hybridization, the chip is washed strictly to elute the DNA fragments that fail to hybridize or mismatch with the probes, remove the non-specific background and retain the specific binding. Each SNP is identified by a multicolor ligation reaction on the surface of the chip. After the ligation reaction is completed, the chip will complete single-base extension, staining, washing, etc. on the GeneTitan multi-channel automated chip workstation; then scan the chip and output the genotyping results.
[0059] Quality assessment of the genotyping data obtained by sequencing, analysis of the missing rate, heterozygosity rate, and minimum allele frequency of each marker, and obtaining 34,588 high-quality SNP markers.
[0060] 2. Determination of the threshold value of whole genome association analysis
[0061] The missing rate, heterozygosity rate and minimum allele frequency (MAF) of each marker were analyzed using PLINK software (www.cog-genomics.org / plink2) for the 34588 SNP markers of the whole genome association analysis data set. The polymorphic markers with missing rate >20%, heterozygosity rate >20% and MAF ≥0.05 were selected, and 10948 SNP sites were obtained. The significance threshold was calculated: P=1 / N=1 / 10948=9.13×10 -5 , where N is the number of independent SNP markers.
[0062] 3. Combined with the genotype data and the phenotype data of 499 peanut seed coat color related traits, the GWAS analysis was performed using the mixed linear model (MLM) in TASSEL5.0 software (Yu J, Buckler E S. Genetic association mapping and genome organization of maize [J]. Current Opinion in Biotechnology, 2006, 17(2): 155-160.) combined with the population structure (Q) and kinship (K) analysis, and the SNP sites significantly associated with the target traits were obtained. The detected significant SNP sites were analyzed by LD, combined with the physical position information of the SNP and the r 2 of the significant SNPs on each chromosome. In the case of r 2 ≥0.2, the LD block was divided according to the standard. The most significant SNP in each block was defined as the Lead SNP, which was called the significant association site. The significance threshold between the SNP and the trait was set to -log 10 (P) ≥4. The Manhattan plot and QQ-Plot plot were used to realize the visualization of the GWAS data, and the difference between the observed value and the predicted value of the estimated P value was displayed.
[0063] The association degree of 10948 SNP sites and the four phenotype data R, G, B and p value of peanut seed coat color was detected, and the significance threshold was set to -log 10 (P) ≥4. The mapping was made according to the positioning results (see Figure 1). Figure 5), the significant correlation degree was observed. The results of 139 SNP sites were located (see Table 6). The sites related to the phenotype data R traits were 100, the sites related to the phenotype data G traits were 59, the sites related to the B traits were 64, and the sites related to p were 101. Further, the co-localization analysis of SNP sites capable of simultaneously regulating multiple traits was carried out, and the results showed that the SNP sites were located on the 1st, 2nd, 3rd, 5th, 6th, 8th, 9th, 10th, 11th, 12th, 13th, 14th, 15th, 16th, 17th, 18th, 19th and 20th chromosomes of the peanut, and the SNP density on the 10th chromosome was the highest (see Table 6). Figure 6
[0064] Table 6 SNP sites associated with the peanut seed coat color traits
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[0075]
[0076] Note: The physical position information of the SNP sites in the table is taken as the reference of arahy.Tifrunner.gnm1.KYV3.genome_main.fna.
[0077] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.
[0078] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the appended claims are intended to cover all such modifications that do not depart from the true spirit and scope of the application. Therefore, the application is not limited to the embodiments shown but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. Use of a SNP molecular marker for identifying peanut seed coat color and / or in peanut breeding, characterized in that, The physical information of the SNP molecular marker sites is shown in Table 6, and the physical position information in Table 6 is determined based on sequence alignment of peanut reference genome arahy.Tifrunner.gnm1.KYV3.genome_main.fna.
2. A combination of molecular probes for identifying the color of the seed coat of peanuts, characterized in that, The molecular probe combination is used for detecting the SNP molecular markers shown in Table 6.
3. A test product for identifying the color of the seed coat of peanuts, characterized in that, The product comprises the molecular probe combination of claim 2.
4. A method for identifying the seed coat color of peanuts at the seedling stage, characterized by, The peanut sample to be tested is detected by using the molecular probe combination of claim 2 or the detection product of claim 3.
5. The molecular probe combination of claim 2 or the detection product of claim 3 is applied in peanut breeding.