Method for predicting corn ear weight heterosis by using number of heterosis effect sites
By obtaining the number of sites of the significant positive ear-heavy hybrid dominance effect of corn hybrids, a prediction model was constructed, and the problem of insufficient prediction accuracy of corn hybrid dominance in the existing technology was solved, and high-precision prediction of corn ear-heavy hybrid dominance was achieved, which improved breeding efficiency and reduced costs.
Patent Information
- Application Number
- CN202510595718.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-09
- Publication Date
- 2025-08-15
AI Technical Summary
In the prior art, the direct prediction method for corn hybrid advantage is not yet mature. Traditional methods mostly indirectly deduce hybrid advantages by predicting hybrid phenotypes, and the existing models do not fully utilize the genetic basis of hybrid advantages, resulting in insufficient prediction accuracy.
By obtaining the number of sites of significant positive ear-heavy hybrid dominant effect in corn hybrids, a prediction model was constructed, and a variety of genetic effects were integrated using the EGBLUP method to establish a nonlinear relationship to predict the advantages of ear-heavy hybrids.
It significantly improves the prediction accuracy of the advantages of heavy hybrids in corn ears, improves the efficiency of hybrid screening, and reduces the cost of variety development.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the field of biological breeding technology, and specifically relates to a method for predicting the heterosis of corn ear weight by utilizing the number of heterosis effect sites. Background Art
[0002] With the development of biotechnology, the breeding methods of corn hybrids are not limited to conventional breeding. The use of efficient breeding technology has greatly promoted the breeding process of corn hybrids. Whole genome prediction is a biotechnology that infers phenotypic information of traits based on individual genotypes, and has significant application prospects in improving the efficiency of crop breeding. In theory, as long as the genotype information of corn inbred lines and the phenotypes of some hybrids (such as yield, resistance and other traits) are obtained, the phenotypes of all remaining hybrids can be predicted, and excellent hybrids can be selected based on the prediction results to enter a multi-year and multi-point evaluation process. This method does not require the evaluation of a large number of useless hybrids, can effectively reduce breeding costs, accelerate the breeding process, and help breeders make data-driven and accurate decisions without relying on experience.
[0003] Currently, existing genome-wide prediction methods, such as RR-BLUP (Ridge Regression Best Linear Unbiased Predictor), have been applied to crop phenotyping, but direct prediction of heterosis remains insufficient. Traditional methods often indirectly infer heterosis (such as mid-parent vigor) by predicting hybrid phenotypes, while direct prediction methods based on the effects of heterosis QTL loci are still immature.
[0004] Although the EGBLUP (Extended Genomic Best Linear Unbiased Prediction) model proposed by Jiang et al. (2017) incorporates a heterotic transformation matrix, it still relies on SNP (Single Nucleotide Polymorphism) marker effects and does not fully utilize the genetic basis of heterosis. Therefore, a feasible prediction method for heterosis prediction is needed in this field. Summary of the Invention
[0005] The purpose of the present invention is to provide a method for predicting the heterosis of corn ear weight by using the number of heterosis effect sites, to carry out heterosis prediction through heterosis effects, and to provide a feasible prediction method for heterosis prediction.
[0006] To achieve the above object, the present invention provides the following technical solutions:
[0007] A method for predicting heterosis of corn ear weight using the number of heterosis effect loci comprises the following steps:
[0008] (1) Using maize hybrid populations and molecular markers to determine the number of significant positive heterotic effect loci for ear weight in each hybrid;
[0009] (2) Using maize hybrid populations, we constructed a prediction model for the number of significant positive heterotic effect loci for ear weight and a prediction model for heterotic effect for ear weight;
[0010] (3) Using the constructed prediction model for the number of significant positive ear weight heterosis effect loci, the number of significant positive ear weight heterosis effect loci in the tested hybrids was predicted;
[0011] (4) The heterosis effect of ear weight of the tested hybrids was predicted based on the number of significant positive heterosis effect sites of ear weight and the established heterosis prediction model of ear weight.
[0012] Wherein, in step (1), the method for obtaining the number of significant positive ear weight heterosis effect loci in hybrids comprises the following steps:
[0013] ① Using molecular markers in maize hybrid populations and mid-parent heterosis in ear weight to identify significant heterosis loci for ear weight;
[0014] ② Further integrate the significant heterosis genetic effects identified in step ① into locus heterosis effects;
[0015] ③ Test the statistical significance of the heterosis effect of the loci identified in step ② and count the number of loci with significant positive heterosis effect on ear weight in each hybrid.
[0016] Wherein, in said step (2), a prediction model for the number of loci with significant positive heterosis effect on ear weight of maize hybrid genotypes is constructed by integrating multiple genetic effects using the EGBLUP method.
[0017] Wherein, in said step (2), the construction of the ear weight heterosis prediction model is to construct a nonlinear relationship using the number of significant positive ear weight heterosis effect sites identified in step (1) and the heterosis of the middle parent of ear weight.
[0018] Wherein, in said step (4), the prediction accuracy is determined by the mean of the Pearson correlation coefficients of the predicted mid-parental heterosis of ear weight and the measured mid-parental heterosis of 10 randomly selected hybrids to be tested.
[0019] Compared with the prior art, the present invention has the following beneficial effects:
[0020] The method of the present invention for predicting heterosis of corn ear weight by utilizing the number of heterosis effect sites has a prediction accuracy significantly higher than other methods for predicting heterosis by utilizing heterosis effects, and can be used for predicting heterosis of ear weight during corn variety breeding, thereby improving hybrid screening efficiency and reducing variety development costs. DETAILED DESCRIPTION
[0021] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.
[0022] A method for predicting heterosis of corn ear weight using the number of heterosis effect loci comprises the following steps:
[0023] 1. Obtaining the number of significant positive heterotic effect loci for ear weight in maize hybrids
[0024] (1) Identification of significant heterotic loci for ear weight in maize hybrids
[0025] The maize hybrid population used was the CUBIC (Complete-diallel plus Unbalanced Breeding-derived Inter-Cross) population (Xiao et al., Genome Biology, 2021; 22: 148), which included 30 hybrid male parents, 206 hybrid female parents, and 5,706 corresponding hybrids. Genotype data were obtained from 40,448 high-quality parental genomic SNPs, and the hybrid genotypes were derived from the corresponding parental genotypes. The magnitude of heterosis in ear weight was calculated by BLUP of ear weight data from five environments to calculate the heterosis of the middle parent, i.e., (hybrid - parent mean) / parent mean. The location of the genetic loci for the heterosis of hybrid ear weight was carried out by an improved genome-wide association analysis method (Jiang et al., Nature Genetics, 2017; 49(12): 1741). According to the identification criterion of q < 0.05 (Benjamini-Hochberg, 1995), a total of 16,889 significant loci of heterosis-plus-plus interaction effect for ear weight and 343 significant loci of heterosis-significant interaction effect for ear weight were detected.
[0026] (2) Calculation and statistical significance analysis of heterosis effect of ear weight at each locus in maize
[0027] The heterotic effect of ear weight at each locus in maize is obtained by integrating the significant heterotic genetic effects of each locus. For specific methods, refer to the calculation formula published by Jiang et al. (2017):
[0028]
[0029] After determining the heterotic effects for ear weight at each locus, Pearson correlation analysis was performed between the heterotic effects for each locus and the heterotic effect for ear weight in the hybrid population. Statistical analysis of heterotic effects was performed using the q < 0.05 criterion (Benjamini-Hochberg, 1995). A total of 5,996 heterotic effects for ear weight were statistically significant.
[0030] (3) Obtaining the number of significant positive heterotic effect loci for ear weight in maize hybrids
[0031] Because the heterotic effects of ear weight on the 5,996 significant loci identified varied across maize hybrids, including positive, negative, or no contribution (statistically insignificant), we screened and counted the positively contributing loci for each hybrid. Of these, 5,363 loci showed a positive contribution in at least one hybrid. The hybrid with the fewest significant positive-effect loci contained only two, while the hybrid with the most contained 2,831 significant positive-effect loci.
[0032] 2. Construction of a prediction model for the number of loci with significant positive heterotic effect on ear weight in maize hybrids
[0033] Using the EGBLUP method (Jiang et al., 2017, formula 7: A model for predicting the number of sites with significant positive heterosis effect on ear weight in hybrids was constructed. Modified to a vector of loci with significant positive heterotic effects for ear weight in hybrids. This model establishes relationships between the number of loci with significant positive heterotic effects for ear weight in 5,706 hybrids and the integrated genetic effects of 40,448 SNPs: dominance (D), additive-additive interaction (AA), additive-dominant interaction (AD), and dominant-dominant interaction (DD). The training and validation sets consisted of 80% and 20% of the hybrids, respectively, with 100 cross-validations. The mean Pearson correlation coefficient between the predicted and measured values in the validation set was used as the prediction accuracy.
[0034] The model performed well in predicting the number of significant positive heterotic effect loci for ear weight in hybrids, with a prediction accuracy of 0.96-0.98 (D: 0.97, AA: 0.98, D+AA: 0.98, D+AA+AD: 0.98, D+AA+AD+DD: 0.98).
[0035] 3. Number of significant positive heterotic effect loci for ear weight in hybrids and construction of heterotic effect prediction model for ear weight
[0036] A prediction model was constructed using the number of loci with significant positive heterotic effects for ear weight and mid-parent heterosis for ear weight from 80% of randomly sampled hybrids (4,565 accessions). The model was constructed using the quadratic polynomial function lm in R. The model was lm(mid-parent heterosis for ear weight ~ poly(number of loci with significant positive heterotic effects for ear weight in hybrids, 2)), where y = ax 2 +b. Where y is the heterosis of ear weight, x is the number of sites with significant positive heterosis effect on ear weight in hybrids, a is the coefficient, and b is the intercept. The model was established by 10 samplings, and R 2 The average value is 0.33. The specific indicators of the 10-order model are shown in Table 1.
[0037] Table 110 Model Indicators
[0038]
[0039]
[0040] 4. Prediction of the number of significant positive heterotic effect loci for ear weight in the hybrids to be tested
[0041] 20% of the hybrids were used as test hybrids (1,141 hybrids). The number of significant positive heterotic effect loci for ear weight in the test hybrids was predicted using the genotypes of 40,448 SNPs in the 1,141 hybrids and the model constructed in step 2.
[0042] 5. Prediction of heterosis of ear weight of tested hybrids
[0043] The number of loci with significant positive heterotic effects for ear weight obtained in step 4 and the heterotic vigor prediction model for ear weight constructed in step 3 were used to predict heterotic vigor for ear weight in the test hybrids. Prediction accuracy was determined by averaging the Pearson correlation coefficients between the predicted and measured heterotic effects for ear weight in 10 randomly selected test hybrids. The accuracy of predicting heterotic vigor for ear weight using the number of loci with significant positive heterotic effects reached a high of 0.51, making it the most accurate method known for predicting heterotic vigor for ear weight in maize using heterotic effects. This result also demonstrates that the number of loci with significant positive heterotic effects is an important predictor of heterotic vigor.
[0044] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.
Claims
1. A method for predicting heterosis of corn ear weight using the number of heterosis effect loci, characterized by: The following steps are involved: (1) Using maize hybrid populations and molecular markers to determine the number of significant positive heterotic effect loci for ear weight in each hybrid; (2) Using maize hybrid populations, we constructed a prediction model for the number of significant positive heterotic effect loci for ear weight and a prediction model for heterotic effect for ear weight; (3) Using the constructed prediction model for the number of significant positive ear weight heterosis effect loci, the number of significant positive ear weight heterosis effect loci in the tested hybrids was predicted; (4) The heterosis effect of ear weight of the tested hybrids was predicted based on the number of significant positive heterosis effect sites of ear weight and the established heterosis prediction model of ear weight.
2. The method for predicting heterosis of corn ear weight using the number of heterosis effect loci according to claim 1, characterized in that: In step (1), the method for obtaining the number of significant positive ear weight heterosis effect sites in hybrids comprises the following steps: ① Using molecular markers in maize hybrid populations and mid-parent heterosis in ear weight to identify significant heterosis loci for ear weight; ② Further integrate the significant heterosis genetic effects identified in step ① into locus heterosis effects; ③ Test the statistical significance of the heterosis effect of the loci identified in step ② and count the number of loci with significant positive heterosis effect on ear weight in each hybrid.
3. The method for predicting heterosis of corn ear weight using the number of heterosis effect loci according to claim 1, characterized in that: In the step (2), a prediction model for the number of loci with significant positive heterosis effect on ear weight of maize hybrid genotypes is constructed by integrating multiple genetic effects using the EGBLUP method.
4. The method for predicting heterosis of corn ear weight using the number of heterosis effect loci according to claim 1, characterized in that: In the step (2), the construction of the ear weight heterosis prediction model is to construct a nonlinear relationship using the number of significant positive ear weight heterosis effect sites identified in step (1) and the heterosis of the middle parent of ear weight.
5. The method for predicting heterosis of corn ear weight using the number of heterosis effect loci according to claim 1, characterized in that: In the step (4), the prediction accuracy is determined by the mean of the Pearson correlation coefficients of the predicted mid-parental heterosis of ear weight and the measured mid-parental heterosis of 10 randomly selected hybrids to be tested.