Image phenotype-based wheat grain anthocyanin related gene detection method
The method for detecting anthocyanins in wheat grains by using image phenotype and genotype association analysis has solved the problem of high-throughput, non-destructive, and low-cost detection of anthocyanin traits in wheat breeding, and has achieved rapid and accurate gene screening, thus promoting the breeding process.
Patent Information
- Application Number
- CN202511764604.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-02-24
AI Technical Summary
Existing technologies cannot achieve high-throughput, non-destructive, and low-cost detection of anthocyanin traits in wheat, making it difficult to screen for anthocyanin-related genes during the breeding process.
By acquiring wheat grain images and performing automated processing, RGB color space features were extracted and converted to Lab color space. Combined with SNP genotype data, genome-wide association analysis was used to identify anthocyanin-related gene loci and construct an association model between image phenotype and genotype.
It enables rapid, stable, and non-destructive detection of anthocyanin traits in wheat grains, allowing for efficient screening of related genes, greatly accelerating the breeding process, reducing costs, and improving detection accuracy.
Smart Images
Figure CN121565245A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to wheat breeding, and more specifically, to a method for detecting anthocyanin-related genes in wheat grains based on image phenotype. Background Technology
[0002] Wheat, as an important food crop, provides humans with a major source of calories and protein. With population growth and increased health awareness, the market demand for wheat varieties with special nutritional functions is becoming increasingly urgent. Anthocyanins, as a natural water-soluble pigment, have powerful antioxidant, anti-inflammatory, and chronic disease prevention functions. Cultivating purple or blue wheat rich in anthocyanins can significantly enhance its nutritional value and market competitiveness.
[0003] Currently, the identification of anthocyanin traits in wheat mainly relies on visual color grading. This method suffers from high subjectivity, poor repeatability, and difficulty in achieving precise quantification. While chemical analysis techniques such as high-performance liquid chromatography (HPLC) provide accurate results, they are cumbersome, costly, and can damage seeds, thus hindering rapid, non-destructive testing and breeding applications. Summary of the Invention
[0004] To address the problem that traditional methods cannot perform high-throughput, non-destructive, and low-cost preliminary screening of anthocyanin genotypes in individual wheat seeds, this invention provides a method for detecting anthocyanin-related genes in wheat grains based on image phenotypes. This method associates visible image color phenotypes with intrinsic gene loci, thereby enabling the location of anthocyanin-related genes from the genetic population without damaging the seeds.
[0005] This invention enables the rapid quantification of color indicators related to anthocyanin traits in wheat grains, thereby providing a method for detecting anthocyanin-related genes using characteristic phenotypes. This method can provide rapid, stable, and non-destructive image phenotypic indicators for the breeding of high-anthocyanin wheat, and efficiently screen related genes, which is of great significance for accelerating breeding.
[0006] To achieve the above objectives, the present invention provides a method for detecting anthocyanin-related genes in wheat grains based on image phenotype, comprising the following steps: S1. Acquire images of wheat grain samples and perform automated image processing; S2. Extract the RGB color space features of the image and convert them to the Lab color space to obtain the L value, a value and b value; S3. Obtain SNP genotype data; S4. Based on the association model between wheat grain L value, a value, b value and SNP genotype, determine whether the SNP locus is an anthocyanin-related gene locus.
[0007] Specifically, in step S1, a high-resolution RGB scanner is used to acquire images of wheat grains in a standardized light source box; automated processing includes background segmentation, noise filtering, and single grain identification and extraction.
[0008] Specifically, in step S2, the RGB color space features include: extracting the mean and standard deviation of the R, G, and B channels in the RGB color space.
[0009] Specifically, in step S3, the method for obtaining SNP genotype data is as follows: Novogene completes library construction and sequencing, and the mixed library is sequenced using the Illumina Hiseq PE150 platform. After quality control, the high-quality sequencing reads are compared with the wheat reference genome (Chinese Spring Genome v2.1) to obtain high-quality SNPs.
[0010] Specifically, in step S4, the method for determining whether the SNP site is an anthocyanin-related gene site is as follows: Genome-wide association analysis identified significant SNP sites, which were then identified using annotations from the wheat reference genome (Chinese Spring Genome v2.1). If an anthocyanin-regulating gene is located near a significant SNP site, then the SNP site is an anthocyanin-related gene site; otherwise, it is not.
[0011] Specifically, in step S3, the method for constructing the association model between wheat grain a-values, b-values, and SNP genotypes includes: S3.1 Collect diverse natural or breeding populations of wheat with known genotypes as the training set; S3.2 Acquire images of wheat grains and perform automated image processing; S3.3 Extract the RGB color space features of the image and convert them to the Lab color space; S3.4 Extracting the texture and morphological features of the image; S3.5. Using genome-wide association analysis, scan the entire genome to identify SNP sites that are significantly associated with key image color features.
[0012] Further, in step S3.3, the mean and standard deviation of the R, G, and B channels are extracted from the RGB color space and converted to the Lab color space.
[0013] Furthermore, in step S3.4, texture features including contrast, energy, and homogeneity are extracted based on the gray-level co-occurrence matrix; at the same time, morphological parameters including the area, perimeter, aspect ratio, and roundness of the grain are extracted.
[0014] Furthermore, in step S3.5, the method for identifying SNP sites that are significantly associated with the color features of key images is as follows: using the feature values obtained from image scanning, combined with SNP data, to perform genome-wide association analysis; and to obtain significant SNP sites.
[0015] Through the above technical solution, the present invention achieves the following beneficial effects: 1. Originality: This invention is the first to construct a complete and quantifiable detection pathway from "wheat grain RGB image" to "anthocyanin regulatory gene", providing a new and non-destructive technical approach for gene detection.
[0016] 2. High throughput and low cost: After the model is built, the detection of a single seed can be completed in seconds, with extremely low cost, making it suitable for the initial screening of large-scale breeding materials.
[0017] 3. Non-destructive and highly practical: The testing process does not damage the seeds at all, and the selected high-quality seeds can be directly used for the next generation of planting, which greatly accelerates the breeding process and solves the core problem that molecular marker technology must destroy seeds.
[0018] 4. High accuracy: By combining multi-dimensional image features with rigorous GWAS analysis, the selected image markers have a strong correlation with genotypes, and the prediction accuracy is far higher than that of manual visual inspection. Attached Figure Description
[0019] Figure 1 This is a screenshot of the ImageJ software window. Figure 2 Wheat seed image (a) and wheat seed image (b) after thresholding; Figure 3 Extract RGB metrics from seed images for ImageJ software; Figure 4 Manhattan plot for genome-wide association analysis of image phenotypic indicators (RGB) using GAPIT software; Figure 5 Manhattan plot for genome-wide association analysis of image phenotypic indicators (Lab) using GAPIT software. Detailed Implementation
[0020] The specific embodiments of the present invention will be described in detail below with reference to examples. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0021] Example 1: Construction and Feature Extraction of a Standardized Image Phenotypic Database 1. Experimental materials: 359 wheat natural populations with broad genetic diversity were selected.
[0022] 2. Image Acquisition: Using a professional scanner, the Microtek ScanMaker i360, in a sealed light-proof box with a built-in D65 standard light source, seed images were acquired in batches at a resolution of 300 DPI and a color depth of 48 bits, and saved in TIFF format.
[0023] 3. Image Processing and Feature Extraction: Open ImageJ software ( Figure 1 ), import the seed image file that needs to be analyzed.
[0024] Click Image→Type→8-bit in the menu bar to convert the image to grayscale mode. Use the Threshold tool to perform thresholding segmentation on the image. Adjust the threshold slider through Image→Adjust→Threshold to separate the seed region from the background, so that the seed region appears white (or black) in the binary image, and the background is black (or white). Figure 2 ).
[0025] Select Analyze → Analyze Particles, and set the relevant parameters in the pop-up dialog box, such as area range and roundness range. These parameter settings will affect the accuracy of the final analysis results and need to be adjusted according to the actual characteristics of the seeds.
[0026] Use Image→Color→Split Channels to split the image into three single-channel images: Red (R), Green (G), and Blue (B), and view the grayscale images of each channel. Use the Ellipse or Polygon tool in the toolbar to select one or more seeds, and optionally copy the ROI to other seeds.
[0027] Activate each channel image separately, and use Analyze→Measure to record the average values of the R, G, and B components. Figure 3 ), and optionally use ROI Manager for batch measurements, saving the measurement results as Excel or text files.
[0028] Convert the original RGB image to Lab Stack using Image→Type→Lab Stack, and then use Analyze→Measure on the L, a, and b channels respectively to obtain the Lab values of the seed.
[0029] Example 2: Genome-wide association analysis based on image phenotypes 1. Genotype data: Genome sequencing was performed on the above 359 materials (library construction and sequencing were completed by Novogene, and the mixed libraries were sequenced using the Illumina Hiseq PE150 platform. After quality control, the high-quality sequencing reads were compared with the wheat reference genome (Chinese Spring Genome v2.1)) to obtain high-density SNP markers (approximately 500,000).
[0030] 2. Association Analysis: The seed color values obtained in Example 1 were used as the target phenotype. Using GAPIT v3.0 software and an MLM model, genome-wide association analysis was performed between the SNP genotype data and color values to identify significant SNP loci.
[0031] Example 3: Detection of anthocyanin-related genes in wheat grains based on image phenotype 1. Select a new natural wheat population with an unknown genotype and perform genome sequencing to obtain high-density SNP markers; 2. The L value, a value, and b value of the grains were obtained using the methods described in steps 2-4 of Example 1; 3. Based on 1 and 2, perform genome-wide association analysis to obtain significant SNPs (SNPs). P <10 -4 Near significant SNPs, wheat reference genome annotation results were searched, and QTLs stably expressed in different environments were detected on chromosomes 2A, 3D, and 4D, with phenotypic variation explanation rates of 31.1%, 9.4%, and 13.1%, respectively. Figure 4 We analyzed QTLs on chromosome 4D and found [the following] within the localization intervals. TaMYB4D and TaMYC4D These two genes have been verified to be transcription factor genes involved in the regulation of anthocyanins in blue wheat grains. Therefore, the detection method of this invention is reliable.
[0032] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.
[0033] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.
[0034] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.
Claims
1. A method for detecting anthocyanin-related genes in wheat grains based on image phenotype, characterized in that, Includes the following steps: S1. Acquire images of wheat grain samples and perform automated image processing; S2. Extract the RGB color space features of the image and convert them to the Lab color space to obtain the L value, a value and b value; S3. Obtain SNP genotype data; S4. Based on the association model between wheat grain L value, a value, b value and SNP genotype, determine whether the SNP locus is an anthocyanin-related gene locus.
2. The detection method according to claim 1, characterized in that, In step S1, a high-resolution RGB scanner is used to acquire images of wheat grains inside a standardized light source box. The automated processing includes: background segmentation, noise filtering, and single seed identification and extraction.
3. The detection method according to claim 1, characterized in that, In step S2, the RGB color space features include: extracting the mean and standard deviation of the R, G, and B channels in the RGB color space.
4. The detection method according to claim 1, characterized in that, In step S3, the method for obtaining SNP genotype data is as follows: first, library construction and sequencing are performed, then the mixed library is sequenced using the Illumina Hiseq PE150 platform, and after quality control, the high-quality sequencing reads are compared with the wheat reference genome to obtain high-quality SNPs.
5. The detection method according to claim 1, characterized in that, In step S4, the method for determining whether the SNP site is an anthocyanin-related gene site is as follows: Genome-wide association analysis detected significant SNP sites. By searching the wheat reference genome annotation, if there are anthocyanin-regulating genes near the significant SNP site, then the SNP site is an anthocyanin-related gene site; otherwise, it is not.
6. The detection method according to any one of claims 1 to 5, characterized in that, In step S3, the method for constructing the association model between wheat grain a-values, b-values, and SNP genotypes includes: S3.1 Collect diverse natural or breeding populations of wheat with known genotypes as the training set; S3.2 Acquire images of wheat grains and perform automated image processing; S3.3 Extract the RGB color space features of the image and convert them to the Lab color space; S3.4 Extracting the texture and morphological features of the image; S3.
5. Using genome-wide association analysis, scan the entire genome to identify SNP sites that are significantly associated with key image color features.
7. The detection method according to claim 6, characterized in that, In step S3.3, the mean and standard deviation of the R, G, and B channels are extracted from the RGB color space and converted to the Lab color space.
8. The detection method according to claim 6, characterized in that, In step S3.4, texture features including contrast, energy, and homogeneity are extracted based on the gray-level co-occurrence matrix; at the same time, morphological parameters including the area, perimeter, aspect ratio, and roundness of the grain are extracted.
9. The detection method according to claim 6, characterized in that, In step S3.5, the method for identifying SNP sites that are significantly associated with the color features of key images is as follows: using the feature values obtained from image scanning, combined with SNP data, a genome-wide association analysis is performed to obtain significant SNP sites.