Forest tree cross parent accurate matching method based on multi-omics analysis

Abstract

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of forest tree hybridization, in particular to a forest tree hybridization parent precise matching method based on multi-omics analysis. BACKGROUND

[0002] As a key approach to cultivating high-quality forest tree varieties, forest tree hybridization breeding can organically integrate the excellent traits of both parents, thereby cultivating new varieties with more advantages. In the past, the matching of forest tree hybridization parents mainly relied on the observation of phenotypic traits and the experience of breeders. However, phenotypes are easily disturbed by environmental factors, and the genetic mechanisms of numerous excellent traits are complex. Therefore, it is of great limitation to screen parents with excellent genetic combinations only by phenotypes, which undoubtedly leads to low efficiency and long breeding cycle of hybridization breeding.

[0003] Nowadays, with the vigorous development of biotechnology, multi-omics technologies such as genomics, transcriptomics, proteomics and metabolomics provide powerful tools for in-depth exploration of forest tree genetic characteristics and precise analysis of gene function. The application of multi-omics analysis in the matching of forest tree hybridization parents is expected to realize precise breeding, greatly improve breeding efficiency and success rate, and inject new vitality into the high-quality development of forestry.

[0004] Therefore, we propose a forest tree hybridization parent precise matching method based on multi-omics analysis to solve the problem. SUMMARY

[0005] (I) Technical problems solved In view of the shortcomings of the prior art, the present application provides a forest tree hybridization parent precise matching method based on multi-omics analysis, which solves the problems in the background art.

[0006] (II) Technical solutions To achieve the above purpose, the present application provides the following technical solutions: a forest tree hybridization parent precise matching method based on multi-omics analysis, comprising the following steps: S1: Multi-omics data acquisition: genome sequencing, transcriptome analysis, proteomics analysis and metabolomics analysis are performed on forest tree population individuals to obtain multi-omics data such as genetic variation sites, gene expression, protein expression abundance and metabolite spectrum; S2: Multi-omics data analysis and integration: the obtained multi-omics data are preprocessed and analyzed, a comprehensive genetic model is constructed, and the genetic regulation network of forest tree trait formation is analyzed; S3: Hybridization parent precise matching: according to the comprehensive genetic model, the genetic potential of forest tree individuals in target traits is evaluated, and complementary matching, similarity matching and hybrid vigor prediction strategies are used to determine the hybridization parent combination; S4: Verification and optimization: verify the accuracy of the parent selection method through hybridization experiments, and optimize the integrated genetic model and heterosis prediction model according to the verification results.

[0007] Preferably, the specific method of genomic sequencing is to use new generation high-throughput sequencing technology to sequence the DNA of forest tree genes, and after quality control, the data is compared to the reference genome to identify SNP, InDel and other genetic variation sites, and a genomic genetic map is constructed.

[0008] Preferably, the specific method of transcriptome analysis is to collect samples at specific stages of forest trees or under environmental stress, extract total RNA and reverse transcribe into cDNA, use RNA-seq sequencing to analyze gene expression, screen differential expression genes, and study their functions and association patterns with target traits.

[0009] Preferably, the specific method of proteomics analysis is to separate and identify proteins in forest tree tissue samples using two-dimensional gel electrophoresis or liquid chromatography-mass spectrometry, and after quantitative analysis, to study their interaction network and determine the key proteins and regulatory mechanisms related to important traits.

[0010] Preferably, the specific method of metabolomics analysis is to use gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry and other technologies to analyze forest tree tissue or cell metabolites, identify differential metabolites, construct metabolite profiles, and explore their association with forest tree phenotypes and genetic backgrounds.

[0011] Preferably, the specific method of multi-omics data analysis and integration is to preprocess multi-omics raw data, use standardization and normalization methods, use bioinformatics and statistical methods for correlation analysis, and use related software to construct an integrated genetic regulation network model.

[0012] Preferably, the specific method of precise selection of hybrid parents is to calculate the genetic score of individual target traits from the perspectives of genes, proteins and metabolites based on the integrated genetic model, and to evaluate their genetic advantages. The parent selection strategy is as follows: complementary selection selects individuals with complementary genetic factors; similarity selection selects individuals with similar multi-omics characteristics and high integrated genetic score of target traits; heterosis prediction uses multi-omics data and machine learning algorithms to construct a model, and selects parent combinations with high heterosis potential according to the prediction results.

[0013] Preferably, the specific method of verification and optimization step is to carry out hybridization experiments according to the parent combinations determined by precise selection, observe the phenotypes of the hybrid offspring and perform multi-omics analysis, compare the actual performance of the offspring with the prediction results, and based on the verification results, adjust the parameters of the integrated genetic model and the heterosis prediction model, add data features or improve the algorithm to optimize the parent selection method.

[0014] (Three) beneficial effects Compared with the prior art, the present application provides a forest tree hybridization parent precise matching method based on multi-omics analysis, which has the following beneficial effects: 1. The forest tree hybridization parent precise matching method based on multi-omics analysis, by analyzing forest tree genetic characteristics from all aspects through multi-omics data, deeply fusing genomics, transcriptomics, proteomics and metabolomics data, accurately identifying genetic factors closely related to target traits, significantly improving the scientificity of parent matching in molecular level in forest tree hybrid breeding, abandoning the ambiguity and uncertainty of traditional phenotype and experience-based judgment from the root, greatly improving the accuracy of parent matching, and making the breeding work more targeted and efficient.

[0015] 2. The forest tree hybridization parent precise matching method based on multi-omics analysis, by the precise parent matching method constructed based on multi-omics analysis, can accurately locate parents with excellent genetic combination, greatly shorten the breeding cycle of excellent forest tree varieties, quickly cultivate new forest tree varieties with multiple excellent traits, and provide high-quality seedlings for forestry, lay a solid foundation for seedlings for sustainable development of forestry, and fundamentally guarantee the long-term stable and efficient development of forestry industry, and help the coordinated progress of ecological environment construction and economic development. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] A forest tree hybridization parent precise matching method based on multi-omics analysis, comprising the following steps: S1: Multi-omics data acquisition: performing genome sequencing, transcriptome analysis, proteomics analysis and metabolomics analysis on forest tree population individuals to obtain genetic variation sites, gene expression, protein expression abundance and metabolite spectrum and other multi-omics data; Here, the specific method of genome sequencing is to measure forest tree gene DNA by using new generation high-throughput sequencing technology, to align the data to the reference genome after quality control, to identify SNP, InDel and other genetic variation sites, and to construct a genetic map of the genome; The specific method of transcriptome analysis is to collect samples at a specific stage of the forest tree or under environmental stress, to extract total RNA and reverse transcribe it into cDNA, to use RNA-seq sequencing, to analyze gene expression, to screen differential expression genes, and to study their functions and association patterns with target traits; The proteomics analysis specifically uses two-dimensional gel electrophoresis or liquid chromatography-mass spectrometry to separate and identify proteins in the forest tree tissue sample, and then quantitatively analyzes the interaction network to determine the key proteins and regulatory mechanisms related to important traits. The metabolomics analysis specifically uses gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, or other techniques to analyze the metabolic products of forest tree tissues or cells, identify differential metabolites, construct a metabolite profile, and explore its association with forest tree phenotypes and genetic backgrounds.

[0018] S2: Multi-omics data analysis and integration: The obtained multi-omics data is preprocessed and correlation analyzed to construct a comprehensive genetic model and analyze the genetic regulatory network of forest tree trait formation. Here, the multi-omics data analysis and integration specifically involves preprocessing the multi-omics raw data, using standardization and normalization methods, correlation analysis using bioinformatics and statistical methods, and constructing a comprehensive genetic regulatory network model with the help of related software.

[0019] S3: Precise selection of hybrid parents: Based on the comprehensive genetic model, the genetic potential of forest tree individuals for target traits is evaluated, and strategies such as complementary selection, similarity selection, and hybrid vigor prediction are used to determine the hybrid parent combination. Here, the precise selection of hybrid parents specifically involves calculating the genetic score of individuals for target traits from the perspectives of genes, proteins, and metabolites based on the comprehensive genetic model, and evaluating their genetic advantages. The parent selection strategies are as follows: complementary selection selects individuals with complementary genetic factors; similarity selection selects individuals with similar multi-omics characteristics and high comprehensive genetic scores for target traits; and hybrid vigor prediction uses multi-omics data and machine learning algorithms to construct a model, and selects parent combinations with high hybrid vigor potential based on the prediction results.

[0020] S4: Verification and optimization: The accuracy of the parent selection method is verified through hybridization experiments, and the comprehensive genetic model and hybrid vigor prediction model are optimized based on the verification results. Here, the verification and optimization step specifically involves conducting hybridization experiments with the parent combinations determined by precise selection, observing the phenotypes of the hybrid offspring, and performing multi-omics analysis. The actual performance of the offspring is compared with the prediction results, and based on the verification results, the parameters of the comprehensive genetic model and the hybrid vigor prediction model are adjusted, additional data features are added, or the algorithm is improved to optimize the parent selection method.

[0021] In summary, the precise selection method and implementation based on multi-omics analysis can improve the scientific nature and accuracy of forest tree hybrid breeding parent selection, accelerate the cultivation of excellent varieties, and contribute to the sustainable development of forestry.

[0022] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for precise selection of forest tree hybridization parents based on multi-omics analysis, characterized in that, Comprise the following steps: S1: Multi-omics data acquisition: Genome sequencing, transcriptome analysis, proteomics analysis and metabolomics analysis are performed on individual trees in the population to obtain multi-omics data such as genetic variation sites, gene expression levels, protein expression abundance and metabolite profiles; S2: Multi-omics data analysis and integration: The obtained multi-omics data is preprocessed and correlation analyzed to construct a comprehensive genetic model and analyze the genetic regulation network of tree trait formation; S3: Precise selection of hybrid parents: According to the comprehensive genetic model, the genetic potential of tree individuals in the target trait is evaluated, and strategies such as complementary selection, similarity selection and heterosis prediction are used to determine the hybrid parent combination; S4: Verification and optimization: The accuracy of the parent selection method is verified through hybridization experiments, and the comprehensive genetic model and heterosis prediction model are optimized according to the verification results.

2. The method according to claim 1, wherein, The specific method of genome sequencing is to use next-generation high-throughput sequencing technology to measure the DNA of the tree genome, and after quality control, the data is compared to the reference genome to identify SNP, InDel and other genetic variation sites, and construct a genome genetic map.

3. The method according to claim 1, wherein, The specific method of transcriptome analysis is to collect samples at specific stages of the tree or under environmental stress, extract total RNA and reverse transcribe into cDNA, use RNA-seq sequencing to analyze gene expression levels, screen differential expression genes, and study their functions and association patterns with target traits.

4. The method according to claim 1, wherein, The specific method of proteomics analysis is to separate and identify proteins in tree tissue samples using two-dimensional gel electrophoresis or liquid chromatography-mass spectrometry, and after quantitative analysis, to study their interaction networks and determine the key proteins and regulatory mechanisms related to important traits.

5. The method according to claim 1, wherein, The specific method of metabolomics analysis is to analyze tree tissue or cell metabolites using gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry and other techniques, identify differential metabolites, construct metabolite profiles, and explore their association with tree phenotypes and genetic backgrounds.

6. The method according to claim 1, wherein, The specific method of multi-omics data analysis and integration is to preprocess the multi-omics raw data, use standardization and normalization methods, use bioinformatics and statistical methods for correlation analysis, and use related software to construct a comprehensive genetic regulation network model.

7. The method according to claim 1, wherein, The specific method of precise selection of hybrid parents is to calculate the genetic score of individual target traits from the genetic, protein and metabolite levels based on the comprehensive genetic model, and evaluate their genetic advantages. The parent selection strategies are as follows: complementary selection selects individuals with complementary genetic factors; similarity selection selects individuals with similar multi-omics characteristics and high comprehensive genetic score of target traits; heterosis prediction uses multi-omics data and machine learning algorithms to construct a model, and selects parent combinations with high heterosis potential based on the prediction results.

8. The method according to claim 1, wherein, The specific method of verification and optimization step is to carry out hybridization experiments according to the parent combinations determined by precise selection, observe the phenotypes of the hybrid offspring and perform multi-omics analysis, compare the actual performance of the offspring with the prediction results, and based on the verification results, adjust the parameters of the comprehensive genetic model and the heterosis prediction model, add data features or improve the algorithm to optimize the parent selection method.