Method for rapidly and accurately discovering genetic interactions of quantitative trait genes in rice

Abstract

A method for rapidly and accurately discovering the genetic interactions of quantitative trait genes in rice. The method of the present invention comprises analyzing the phenotypic difference P values of four QTL allele combinations from any two bins in the subpopulation genome of a rice NAM population; selecting QTL loci that can be simultaneously mapped as candidate QTLs; performing P value analysis of bins for the QTL loci and the regions within 1 Mb upstream and downstream thereof and merging the Pbin values; performing FDR analysis on PM values of all the QTLs and the upstream and downstream regions thereof, and determining the genetic interactions of the QTL loci on the basis of a FDR value <0.01; merging the QTL loci on the basis of LOD values and P values; and discovering key quantitative trait genes from the rice QTL loci with genetic interactions, thereby determining the genetic interaction relationship among the quantitative trait genes in rice. The method can improve the accuracy in discovering the genetic interactions of quantitative trait genes in rice.

Description

A method for rapidly and accurately identifying genetic interactions of quantitative traits in rice Technical Field

[0001] This invention relates to the field of biotechnology, specifically to a method for rapidly and accurately identifying genetic interactions of quantitative traits in rice. Background Technology

[0002] Rice is an important food crop, and developing high-quality, high-yielding, and disease-resistant rice varieties is a key goal of rice breeding. Yield, quality, and resistance are complex quantitative traits, controlled by a large number of quantitative trait genes. Over the past thirty years, through the collaborative efforts of rice geneticists worldwide, using methods such as association and linkage analysis for quantitative trait gene mapping, combined with gene function verification methods such as transgenic, near-isogenic lines, and gene editing, approximately 400 quantitative trait genes have been cloned from cultivated and wild rice. These genes cover dozens of traits, including yield, quality, disease resistance, insect resistance, salt tolerance, alkali tolerance, heat tolerance, cold tolerance, drought tolerance, flood tolerance, plant architecture, grain type, heading date, nutrient absorption efficiency, and heavy metal absorption efficiency. This has provided a clearer understanding of the genetic basis of rice's complex traits and a wealth of usable genetic resources for rice breeding.

[0003] The genetic effects of quantitative trait genes contribute differently to the phenotype. For example, the "Green Revolution" gene Sd1 has a significant impact on rice plant height and is the major gene controlling plant height. Mutations in the Sd1 gene can typically reduce rice plant height by more than 20 centimeters. The genetic effects of quantitative trait genes include additive effects, epistatic effects, and dominant effects. Among them, additive effects are the sum of the genotype values ​​of multiple genes affecting the quantitative trait; dominant effects refer to the effects produced by the interaction between alleles; and epistatic effects refer to the effects caused by the interaction of different genes. Since dominant effects only appear when the gene is heterozygous, for homozygous materials, their narrow heritability is equal to the sum of additive and epistatic effects.

[0004] Interactions between quantitative trait genes are ubiquitous in the biological world. Genetic interactions of quantitative trait genes have been reported extensively in the genomes of yeast, rice, maize, mice, and humans, with epistatic effects having a significant impact on phenotypes in different species. Molecular design breeding of rice requires a thorough understanding of the additive, dominant, and epistatic effects of rice quantitative trait genes. However, to date, the epistatic aspect of rice quantitative trait genes lacks systematic exploration; which genes exhibit epistatic relationships and their epistatic effects are unclear. For example, GW6a is a gene with a moderate effect controlling rice plant height, and its mutation typically results in a change of about ten centimeters in plant height. Simultaneous mutations of the Sd1 and GW6a genes result in plant height changes exceeding 30 centimeters, and the magnitude of the height change is approximately equal to the sum of the effects of the two genes, indicating that there is no significant genetic interaction between Sd1 and GW6a. Hd1 and Ghd8 are both major genes controlling the heading date in rice. Reports have shown that the simultaneous presence of both genes leads to a significant delay in heading, far exceeding the delay caused by the individual genes, indicating a significant genetic interaction between Hd1 and Ghd8. Constructing rice materials containing any two quantitative trait genes through gene editing, near-isogenic lines, and transgenic methods, and analyzing the phenotypes of mutants, can test for the existence of genetic interactions between the two genes. However, verifying arbitrary pairwise interactions of the 400 cloned rice quantitative trait genes through gene editing is an extremely large undertaking, difficult to complete under current technological conditions. Furthermore, molecular design breeding of rice requires understanding the interactions between quantitative trait genes to simultaneously improve multiple genes. Therefore, there is an urgent need to develop a simple and rapid method for comprehensively exploring and analyzing the genetic interactions between rice quantitative trait genes.

[0005] Population genetics and quantitative genetics analyses can be used to analyze additive effects. Combining large populations to improve resolution holds promise for solving the problem of gene interaction discovery. Therefore, this invention aims to use two-way ANOVA and FDR analysis to discover the genetic interactions of quantitative trait genes in rice within a NAM population, thereby providing theoretical and technical support for molecular design breeding of rice. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for rapidly and accurately identifying genetic interactions of quantitative traits in rice. This invention utilizes ANOVA to analyze the p-values ​​of phenotypic differences between four QTL allele combinations in any two bins of a rice NAM population; simultaneously, linkage analysis of the subpopulation and association analysis of the entire population are performed, selecting QTL loci that can be simultaneously located as candidate QTLs; and p-value analysis of bins and p-values ​​within a 1Mb range upstream and downstream of the QTL loci is conducted. bin Merging; P for all QTLs and their upstream and downstream intervalsM FDR analysis was performed on the QTLs, and genetic interactions were determined based on q values ​​< 0.01. QTLs were merged within a certain range based on LOD and P values. Key quantitative trait genes were identified from the QTLs exhibiting genetic interactions, thus determining the genetic interactions between rice quantitative trait genes. The ability to identify genetic interactions between rice quantitative trait genes can significantly reduce false positives in this process.

[0007] Therefore, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides, in optional embodiments, a method for rapidly and accurately identifying genetic interactions of quantitative traits in rice, comprising the following steps:

[0009] S1: Sequencing and phenotypic identification of the rice NAM population at multiple environmental sites were performed. The chromosomal genotype of each material was analyzed. Each subpopulation was divided into four groups based on the genotypes of any two bins, with 100kb as one bin. The p-values ​​for specific phenotypes of the four groups were calculated using two-way ANOVA and denoted as P. bin ;

[0010] S2: Based on the genotype and phenotype of the rice NAM population, perform genome-wide association analysis (GWIA) for the entire population and linkage analysis for each subpopulation to identify quantitative trait loci significantly associated with the quantitative trait phenotype, denoted as associated QTL loci; and perform linkage analysis for each subpopulation of the mapping population to identify quantitative trait loci linked to the quantitative trait phenotype in each subpopulation, denoted as linked QTL loci; compare the positions of associated QTL loci with those linked QTL loci in all subpopulations, and denote QTL loci with close positions as candidate QTL loci;

[0011] S3: In each subpopulation, retrieve bins within 500kb before and after any two candidate QTL loci. Take the pair of bins with the smallest P-value among all bins as the genetic interaction P-value between the two candidate QTL loci. Then, in each subpopulation, retrieve bins within 1Mb before and after any two candidate QTL loci. Take the pair of bins with the smallest P-value among all bins as the genetic interaction P-value between the two candidate QTL loci. M value;

[0012] S4: Summarize the genetic interaction P-values ​​and P-values ​​of QTL loci in all subpopulations. MThe FDR value of all interaction pairs is calculated, and interaction pairs with an FDR value less than 0.01 are selected. Further, interaction pairs are merged in ranges of 3Mb or 5Mb according to chromosome location. The two regions with the smallest FDR values ​​are selected as candidate intervals. The candidate QTL loci with the largest LOD value of the linked locus or the smallest P value of the associated locus within the two candidate intervals are considered to be rice QTL loci with genetic interaction.

[0013] S5: Discover key quantitative trait genes from rice QTL loci where genetic interaction exists. The two genes in a pair of genetic interaction QTLs are the quantitative trait genes in rice where genetic interaction exists.

[0014] In this invention, the inventors of this application, after conducting extensive research, discovered that, for gene-gene interactions, by performing ANOVA-based P-value analysis on the bin, merging the P-values ​​of the 1Mb interval where the bin is located and the 1Mb intervals upstream and downstream of it, performing FDR analysis on the merged P-values, merging QTLs within a certain range according to the signal intensity of QTLs, and screening out key genes within the QTLs, it is possible to screen out quantitative trait genes that are likely to have genetic interactions.

[0015] In step S1, any two bins do not include bins with a physical distance of less than 10 Mb on the same chromosome. Each bin corresponds to two parental alleles, such as Aa, Bb, Cc, or Dd. Taking one bin as an example where the parental allele is Aa and the other bin is Bb, the combinations of parental alleles corresponding to these two bins are AB, Ab, aB, and ab. The offspring population will contain one of the four genes: AB, Ab, aB, or ab. The offspring population is divided into these four groups according to the genes they contain. For example, if an offspring population contains the gene AB, it is divided into the AB group; if an offspring population contains the gene Ab, it is divided into the Ab group.

[0016] Preferably, step S1 includes: S11: Selecting multiple rice materials with significant differences in target quantitative traits as parents, wherein one rice variety is used as a common parent and is hybridized with other materials for several generations to construct a rice nested inbred line (NAM) population containing multiple sets of recombinant inbred lines;

[0017] S12: Genotyping of the parents and all lines of the NAM population, phenotyping of all lines of the NAM population at multiple environmental sites, and obtaining parental genotypes, variation sites of all lines, and phenotypic data under multiple environments.

[0018] S13: Within each subpopulation, bins are constructed in each line within a 100kb interval based on the genotypes of the parents and offspring lines;

[0019] S14: Divide each subpopulation into four groups based on the genotypes of any two bins, and calculate the P-value for a specific phenotype in each of the four groups using ANOVA, denoted as P0. bin .

[0020] In this invention, the parental genomes were sequenced using Nanopore and Illumina sequencing platforms, with average sequencing depths of 69× and 100×, respectively. Genome assembly was performed using NextDenovo and NextPolish. The assembled parental genomes were compared with the Nipponbare genome (MSUv7) using Mummer and BLAST to obtain parental SNP loci. Progeny lines underwent Illumina whole-genome sequencing at a depth of 0.3×. The progeny lines were compared with the Nipponbare genome (MSUv7) using SMALT. The comparison results were used to obtain bin genotypes for each line in 100kb intervals using the SEG-Map program. Combined with the parental SNP variation information, progeny SNP loci were obtained. The two-factor analysis method uses the aov function in R language to calculate the p-value. Taking plant height as an example, the four groups of lines are AB group, Ab group, aB group and ab group, with 1000 offspring plants, which are divided into AB group, Ab group, aB group and ab group respectively. The number of offspring in each group is x1, x2, x3 and (1000-x1-x2-x3) respectively. The plant height data of each offspring is input into the aov function in R language (or R software), and the corresponding p-value of each of the four phenotypes (AB group, Ab group, aB group and ab group) can be calculated.

[0021] Preferably, step S2 includes: S21: performing genome-wide association analysis on the entire NAM population to identify QTL loci that are significantly associated with the target trait;

[0022] S22: Perform linkage analysis on each recombinant inbred line population in the NAM population to identify QTL sites that are significantly linked to the target trait;

[0023] S23: Based on the location of significantly associated and linked QTL sites, select QTL sites that are in the same or similar location in both types of sites as candidate QTL sites.

[0024] In this invention, FarmCPU is used to perform genome-wide association analysis on the entire NAM population to discover QTL sites that are significantly associated with the target trait, and WinQTLCart is used to perform linkage analysis on each recombinant inbred line population in the NAM population.

[0025] Preferably, step S3 includes: S31: taking 10 bins within 500kb before and after the candidate QTL site, 10 bins within the upstream 1Mb interval, and 10 bins within the downstream 1Mb interval, and respectively denoted as candidate QTL bin, candidate QTL upstream bin, and candidate QTL downstream bin;

[0026] S32: Interact the 10 upstream bins, 10 candidate QTL bins, and 10 downstream bins of any two candidate QTLs. Specifically: the upstream bin of one candidate QTL is represented as B1, which contains 10 bins, namely b101, b102...b109 and b110; the candidate QTL bin is represented as B2, which contains 10 bins; and the downstream bin is represented as B3, which contains 10 bins. The upstream bin of the other candidate QTL is represented as Ba, which contains 10 bins, namely ba01, ba02...ba09 and ba10; the candidate QTL bin is represented as Bb, which contains 10 bins; and the downstream bin is represented as Bc, which contains 10 bins. The specific interaction method is as follows: B1 and Ba form a bin pair. B101, B102...b109, and B110 in B1 are combined with ba01, ba02...ba09, and ba10 respectively to form 100 bin pairs. Similarly, B1 and Bb form a bin pair containing 100 bin pairs, ... B3 and Bb form a bin pair containing 100 bin pairs, and B3 and Bc form a bin pair containing 100 bin pairs. A total of 9 bin pairs are formed, each containing 100 bin pairs.

[0027] S33: Take the minimum P for each bin pair. bin The value is denoted as the interaction P of the group. M The value is used as the genetic interaction P-value for candidate QTLs.

[0028] Preferably, step S4 includes: S41: merging the 9 genetic interactions P of all candidate QTL pairs. M The values ​​are merged, and the corresponding FDR value is calculated;

[0029] S42: Select interaction pairs with FDR values ​​less than 0.01 as candidate genetic interaction sites;

[0030] S43: Based on the FDR value, rice quantitative trait loci are merged on all rice chromosomes in a range of 3Mb or 5Mb. Within the range, the locus with the smallest FDR value is selected as the interval where the rice genetic interaction QTL is located.

[0031] S44: Compare the LOD value of the linked loci obtained in each subpopulation linkage analysis or the P value of the associated loci obtained in the whole population GWAS analysis within the interval. Select the QTL locus with the largest LOD value or the smallest P value as the final rice QTL locus with genetic interaction.

[0032] In this invention, the LOD value of linkage sites can be obtained using WinQTLCart when performing linkage analysis on each recombinant inbred line population in the NAM population. The P-value of associated sites can be obtained using FarmCPU when performing association analysis on the NAM population. The FDR value (q-value) is calculated using the p.adjust function in R software; by inputting the P-value, the p.adjust function in R software can calculate the FDR value (q-value).

[0033] Preferably, step S5 includes: S51: analyzing candidate genes within the rice QTL locus and selecting the three genes with the highest scores as candidate genes;

[0034] S52: Based on the function of the gene and the traits involved in its regulation, the final candidate genes are determined, and the two candidate genes in the genetic interaction sites determined in step 44 are considered to be gene pairs with genetic interaction.

[0035] In this invention, the methods used in step S5, "discovering key quantitative trait genes from rice QTL loci with genetic interactions", and step S51 are both methods disclosed in the patent "Patent Application No.: 202310058245.9, Patent Name: A Method for Rapid Discovery of Rice Quantitative Trait Genes".

[0036] Preferably, in step S5, when identifying key quantitative trait genes from rice QTL loci with genetic interactions, the reference genome is the genome of the rice variety "Nipponbare"; and / or,

[0037] The variant site is a single nucleotide variant site.

[0038] Secondly, in an optional embodiment, the present invention provides an application of the above method, wherein the application is to mine the genetic interactions of quantitative trait genes in a rice NAM population.

[0039] Furthermore, the above methods can be applied to various crops that have reference genome sequences and can construct artificial populations.

[0040] Compared with the prior art, the present invention has one of the following beneficial effects:

[0041] 1. The method provided by this invention, through P-value and FDR value analysis, can find quantitative trait loci and genes with genetic interactions in plants, especially rice, thereby reducing the proportion of false positives in genetic interactions and improving the accuracy of discovering genetic interactions of quantitative trait genes in rice.

[0042] 2. Prior to the method of this invention, the discovery of genetic interactions of quantitative trait genes in rice mainly relied on two-way variance analysis of molecular markers, which resulted in a high false positive rate in the discovery of genetic interactions of quantitative trait loci. However, the method of this invention can achieve more accurate discovery of genetic interactions of quantitative trait loci in rice.

[0043] 3. The method of this invention has the advantage of precise genome-wide discovery. Compared with published work on the identification of genetic interactions of quantitative traits in rice, the method of this invention improves the efficiency and accuracy of discovery and reduces false positives. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 is a flowchart illustrating the method for discovering the genetic interactions of quantitative trait genes in rice according to Embodiment 1 of the present invention.

[0046] Figure 2 is a schematic diagram of the genetic interaction relationship of QTL genes during the heading stage of rice in Example 1 of the present invention;

[0047] Figure 3 shows the verification results of the genetic interaction between rice Ghd7 and Ghd7.1 in Example 2 of this invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0050] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are merely for explaining the present invention and are not limited to the present invention.

[0052] The invention will now be described using the discovery of genetic interactions among genes at the heading stage of rice NAM populations as an example.

[0053] Example 1

[0054] Discovery of genetic interactions of heading stage genes in rice NAM population

[0055] This embodiment describes the development process of a technique for rapidly identifying quantitative trait gene interactions using rice NAM populations. In summary, referring to Figure 1, the genotypes and heading dates of the parents and offspring lines in the rice NAM population are first identified to obtain the genotypes of the subpopulations. A two-way ANOVA is then performed on the phenotypes corresponding to any two bins within the subpopulations. Subsequently, linkage and association analyses are performed at the heading date to obtain linkage loci in the subpopulations and association loci in the entire population. Association loci at the same location as linkage loci are selected as candidate QTL loci. The minimum P-value of all bins within a 1Mb interval upstream and downstream of any two QTL loci is taken. The P-values ​​of all QTL loci in all subpopulations are then combined for FDR analysis, and QTL loci with q less than 0.01 are selected as candidate QTL pairs with genetic interaction. Based on the location of the candidate interacting QTL loci, they are merged within a certain interval according to the signal intensity of their linkage and association loci. The QTLs with the strongest signal intensity in each of the two intervals are selected as QTL pairs with genetic interaction. Two key genes were further identified from the two QTL loci as genes with genetic interactions. Preferably, the range of QTL merging can be adjusted based on the population size and the range of the QTL itself.

[0056] Specifically, the discovery of genetic interactions of genes at the heading stage in rice NAM populations was achieved through the following steps:

[0057] Step 1.1: Select 16 rice materials with significant differences in heading period as parents. Among them, one rice variety "Huang Huazhan" is used as a common parent and is hybridized with other materials. After several generations of self-pollination, a rice nested composite diagram (NAM) population containing 15 sets of recombinant inbred lines is constructed.

[0058] Step 1.2: The genomes of 16 parents were sequenced using Nanopore and Illumina sequencing platforms (average sequencing depths of 69× and 100×, respectively). Genome assembly was performed using NextDenovo and NextPolish. The assembled parental genomes were aligned with the Nipponbare genome (MSUv7) using Mummer and BLAST to obtain parental SNP loci. Illumina whole-genome sequencing (0.3× depth) was performed on over 15,000 progeny lines. The progeny sequences were aligned with the Nipponbare genome (MSUv7) using SMALT. The alignment results were used to obtain bin genotypes for each line in 100kb intervals using the SEG-Map program. Combined with parental SNP variation information, progeny SNP loci were obtained. Heading dates were statistically analyzed for all lines in the NAM population in Shanghai, Hangzhou, and Sanya, obtaining heading date data under multiple environmental conditions.

[0059] Step 1.3: Within each subpopulation, divide each subpopulation into four groups based on the genotypes of any two bins. Perform ANOVA analysis using the aov function in R language to calculate the p-value (P0) of the phenotypic ratios of the four groups of strains. bin ).

[0060] Step 1.4: Perform genome-wide association analysis (GWAS) on the entire NAM population using FarmCPU to identify QTL loci significantly associated with the target trait. Use WinQTLCart to perform linkage analysis on each recombinant inbred line population within the NAM population to identify QTL loci significantly linked to the target trait. Based on the location of significantly associated and linked QTL loci, select QTL loci that are at the same or similar locations in both types of loci as candidate QTL loci.

[0061] Step 1.5: Take 10 bins within 500kb before and after the candidate QTL site, 10 bins within a 1Mb upstream interval, and 10 bins within a 1Mb downstream interval, and denote them as the candidate QTL bin, the candidate QTL upstream bin, and the candidate QTL downstream bin, respectively. Interact and combine the upstream bin, candidate QTL bin, and downstream bin of any two candidate QTLs to form 9 bin pairs, each containing 100 bin pairs. Take the P of each bin pair. bin The value is recorded as the interaction PM value of the group and used as the genetic interaction P value of the candidate QTL.

[0062] Step 1.6: The nine genetic interaction P-values ​​of all candidate QTL pairs were merged, and the FDR values ​​(q-values) corresponding to these P-values ​​were calculated using the p.adjust function in R software. Interaction pairs with q-values ​​less than 0.01 were selected as candidate genetic interaction loci. Candidate genetic interaction loci were merged across all rice chromosomes within a 3Mb range. The LOD values ​​of linkage loci obtained during linkage analysis in each subpopulation or the P-values ​​of associated loci obtained during GWAS analysis of the entire population were compared. The QTL loci with the largest LOD value or the smallest P-value were selected as the final rice QTL loci exhibiting genetic interaction. Based on the above steps, 118, 74, and 72 pairs of heading-stage QTLs exhibiting genetic interaction were identified in Shanghai, Hangzhou, and Sanya, respectively.

[0063] Step 1.7: Using the method disclosed in patent application number 202310058245.9, entitled "A Method for Rapidly Discovering Quantitative Trait Genes in Rice," candidate genes within rice QTL loci were analyzed (the genome referenced during analysis was the genome of the rice variety "Nipponbare," and the variant sites were single nucleotide variant sites). The three genes with the highest scores were selected as candidate genes. The final candidate genes were determined based on gene function and the traits they regulate. Ultimately, 75 pairs of rice heading-stage genes with genetic interactions were discovered (see Figure 2).

[0064] Example 2

[0065] Verification of genetic interactions during the heading stage of rice

[0066] Step 2.1: Using the method in Example 1, genetic interaction analysis was performed on the heading period genes of the rice NAM population, and it was found that Ghd7 and Ghd7.1 have genetic interactions.

[0067] Step 2.2: Genotypic analysis was performed on the rice variety “Huang Huazhan”, and it was found that its Ghd7 and Ghd7.1 are wild type.

[0068] Step 2.3: Design CRISPR / Cas9 targets for the coding regions of Huang Huazhan's Ghd7 and Ghd7.1 genes, construct Ghd7 and Ghd7.1 gene editing vectors respectively, and transform them into "Huang Huazhan" through Agrobacterium rhizogenes to knock out the Ghd7 and Ghd7.1 genes respectively.

[0069] Step 2.4: Select homozygous 1bp insertion genotypes from the T0 generation lines of the two genes, plant the materials for two consecutive generations, and select vector-free homozygous mutant materials in each generation.

[0070] Step 2.5: Cross the T2 generation of Ghd7 and Ghd7.1 gene-edited genes, then self-pollinate, and screen out materials from the self-pollinated offspring that are homozygous mutants of both genes.

[0071] Step 2.6: Plant Ghd7 single mutant, Ghd7.1 single mutant, Ghd7 and Ghd7.1 double mutant, and wild-type Huang Huazhan, with at least 60 seedlings per line. Record the heading date, plant height, and yield of all individual plants (see Figure 3). It was found that the heading date of the double mutant material was significantly earlier than the sum of the heading dates of the single mutants, indicating a significant interaction between Ghd7 and Ghd7.1 (P = 1.89 × 10⁻⁶). -29 Moreover, this interaction also causes significant differences in plant height and heading time.

[0072] In summary, by exploring genetic interactions at the heading stage in rice, 75 pairs of genes exhibiting genetic interactions at the heading stage were identified across three environmental sites. Furthermore, for the first time, gene editing was used to verify a significant genetic interaction between the Ghd7 and Ghd7.1 genes. These results demonstrate that the method for identifying genetic interactions of quantitative trait genes in rice can rapidly and accurately uncover interactions among QTL genes at the heading stage.

[0073] Although the principles of the present invention have been described in detail above with reference to preferred embodiments, those skilled in the art should understand that the above embodiments are merely illustrative explanations of the implementation of the present invention and are not intended to limit the scope of the present invention. The details in the embodiments do not constitute a limitation on the scope of the present invention. Any obvious changes, such as equivalent transformations or simple substitutions, based on the technical solutions of the present invention without departing from the spirit and scope of the present invention fall within the protection scope of the present invention.

Claims

1. A method for rapidly and accurately identifying gene interactions in quantitative traits of rice, characterized in that, Includes the following steps: S1: Sequencing and phenotypic identification of the rice NAM population at multiple environmental sites were performed. The chromosome genotype of each material was analyzed. Each subpopulation was divided into four groups based on genotype combinations of any two bins, with 100kb as one bin. The p-values ​​for specific phenotypes of the four groups were calculated using two-way ANOVA and denoted as P. bin ; S2: Based on the genotype and phenotype of the rice NAM population, perform genome-wide association analysis for the entire population and linkage analysis for each subpopulation to identify quantitative trait loci that are significantly associated with the quantitative trait phenotype, denoted as associated QTL loci; and perform linkage analysis for each subpopulation of the mapping population to identify quantitative trait loci linked to the quantitative trait phenotype in each subpopulation, denoted as linked QTL loci. Compare the locations of associated QTL sites with those linked QTL sites in all subpopulations, and denote QTL sites with close locations as candidate QTL sites. S3: In each subpopulation, retrieve bins within 500kb before and after any two candidate QTL loci. Take the pair of bins with the smallest P-value among all bins as the genetic interaction P-value between the two candidate QTL loci. Then, in each subpopulation, retrieve bins within 1Mb before and after any two candidate QTL loci. Take the pair of bins with the smallest P-value among all bins as the genetic interaction P-value between the two candidate QTL loci. M value; S4: Summarize the genetic interaction P-values ​​and P-values ​​of QTL loci in all subpopulations. M The FDR value of all interaction pairs is calculated, and interaction pairs with an FDR value less than 0.01 are selected. Further, interaction pairs are merged in a range of 3Mb or 5Mb according to chromosome location. The two regions with the smallest FDR values ​​are selected as candidate intervals. The candidate QTL locus with the largest LOD value of the linked locus or the smallest P value of the associated locus within the two candidate intervals is considered to be the rice QTL locus with genetic interaction. S5: Discover key quantitative trait genes from rice QTL loci where genetic interaction exists. The two genes in a pair of genetic interaction QTLs are the quantitative trait genes in rice where genetic interaction exists.

2. The method for rapidly and accurately identifying genetic interactions of quantitative traits in rice according to claim 1, characterized in that, Step S1 includes: S11: Select multiple rice materials with significant differences in the target quantitative traits as parents. One rice variety is used as a common parent and is hybridized with other materials. After several generations of continuous self-pollination, a rice nested composite diagram (NAM) population containing multiple sets of recombinant inbred lines is constructed. S12: Genotyping of the parents and all lines of the NAM population, phenotyping of all lines of the NAM population at multiple environmental sites, and obtaining parental genotypes, variation sites of all lines, and phenotypic data under multiple environments. S13: Within each subpopulation, bins are constructed in each line within a 100kb interval based on the genotypes of the parents and offspring lines; S14: Divide each subpopulation into four groups based on the genotypes of any two bins, and calculate the P-value for a specific phenotype in each of the four groups using ANOVA, denoted as P0. bin .

3. The method for rapidly and accurately identifying genetic interactions of quantitative traits in rice according to claim 1, characterized in that, Step S2 includes: S21: Perform genome-wide association analysis on the entire NAM population to identify QTL loci that are significantly associated with the target trait; S22: Perform linkage analysis on each recombinant inbred line population in the NAM population to identify QTL sites that are significantly linked to the target trait; S23: Based on the location of significantly associated and linked QTL sites, select QTL sites that are in the same or similar location in both types of sites as candidate QTL sites.

4. The method for rapidly and accurately identifying genetic interactions of quantitative traits in rice according to claim 1, characterized in that, Step S3 includes: S31: Take 10 bins within 500kb before and after the candidate QTL site, 10 bins within the upstream 1Mb interval, and 10 bins within the downstream 1Mb interval, and denot them as candidate QTL bin, candidate QTL upstream bin, and candidate QTL downstream bin, respectively. S32: Interact the 10 upstream bins, 10 candidate QTL bins, and 10 downstream bins of any two candidate QTLs to form a total of 9 bin pairs, each containing 100 bin pairs; S33: Take the minimum P for each bin pair. bin The value is denoted as the interaction P of the group. M The value is used as the genetic interaction P-value for candidate QTLs.

5. The method for rapidly and accurately identifying genetic interactions of quantitative traits in rice according to claim 1, characterized in that, Step S4 includes: S41: Genetic interactions P of all 9 candidate QTL pairs M The values ​​are merged, and the corresponding FDR value is calculated; S42: Select interaction pairs with FDR values ​​less than 0.01 as candidate genetic interaction sites; S43: Based on the FDR value, rice quantitative trait loci are merged on all rice chromosomes in a range of 3Mb or 5Mb. Within the range, the locus with the smallest FDR value is selected as the interval where the rice genetic interaction QTL is located. S44: Compare the LOD value of the linked loci obtained in each subpopulation linkage analysis or the P value of the associated loci obtained in the whole population GWAS analysis within this interval, and select the QTL locus with the largest LOD value or the smallest P value as the final rice QTL locus with genetic interaction.

6. The method for rapidly and accurately identifying genetic interactions of quantitative traits in rice according to claim 5, characterized in that, Step S5 includes: S51: Analyze the candidate genes within the QTL loci of rice and select the three genes with the highest scores as candidate genes. S52: Based on the function of the gene and the traits involved in its regulation, the final candidate genes are determined, and the two candidate genes in the genetic interaction sites determined in step 44 are considered to be gene pairs with genetic interaction.

7. The method for rapidly and accurately discovering quantitative trait gene interactions in rice according to claim 1, characterized in that, in step S5, when discovering key quantitative trait genes from rice QTL loci where genetic interactions exist, the reference genome is the genome of the rice variety "Nipponbare"; and / or, The variant site is a single nucleotide variant site.

8. The application of the method according to any one of claims 1-7, characterized in that, The application is to mine the genetic interactions of quantitative trait genes in rice NAM populations.

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