Gene localization method based on multi-parent nested association mapping population

Abstract

The invention discloses a gene localization method (NAM-BSA) based on a multi-parent nested association mapping (NAM) population, and belongs to the technical field of plant genetic breeding. The method comprises the following steps: hybridizing, backcrossing and selfing a common parent and at least three donor parents with different genetic backgrounds to construct a BC4F8 population; based on target character extreme phenotype and functional marker genotype analysis, screening extreme individuals from each subpopulation, and equivalently mixing DNA to construct a high-value and low-value mixing pool; performing whole genome re-sequencing on the mixed pool, and performing strict bioinformatics filtering to obtain a high-quality SNP data set; by calculating delta (SNP-index) and combining with a 99% confidence interval threshold, a genome region significantly associated with the character is determined. The method is successfully applied to positioning the plant height gene PH8 of the 8th chromosome of rice in the 4, 600,000-5,760,000 bp interval, overcomes the defect that the genetic diversity of the traditional BSA technology is limited, remarkably improves the gene positioning efficiency and precision of complex quantitative traits, and has wide application value in crop functional genome research and molecular breeding.

Description

Technical Field

[0001] This invention belongs to the field of plant genetics and breeding technology, and specifically relates to a gene localization method based on a multi-parent nested association mapping population. Background Technology

[0002] With the development of molecular marker technology, BSA technology has undergone significant evolution. Early studies mainly relied on molecular markers such as RAPD, AFLP, and SSR. While these markers improved mapping efficiency to some extent, their low throughput and limited genome coverage restricted the accuracy and resolution of mapping. In the era of high-throughput sequencing, BSA technology was combined with whole-genome sequencing, giving rise to a series of powerful methods such as QTL-seq and MutMap. These methods achieve higher throughput and more accurate gene mapping by performing whole-genome resequencing on extreme pools and using SNP-based genotype frequencies for genome-wide association analysis. In recent years, emerging technologies such as PCAMP, GradePool-seq, and DeepBSA have further expanded the research capabilities of BSA from the perspectives of algorithms and analysis. The introduction of artificial intelligence and machine learning technologies has significantly enhanced the ability to analyze complex traits regulated by multiple gene interactions. Currently, BSA technology has successfully cloned multiple key genes controlling yield, stress resistance, and quality traits in important crops such as rice, maize, and wheat, demonstrating its enormous application potential in molecular breeding and functional genomics research.

[0003] However, despite significant advancements in BSA technology, it suffers from a fundamental limitation: all existing BSA and derivative technologies rely on traditional two-parent hybrid populations (such as F2, RIL, DH, etc.). This population structure contains only allelic variations from two parents, resulting in limited genetic diversity. When the target trait is controlled by multiple minor QTLs or exhibits a dominant effect, there may be a lack of sufficient polymorphic markers in the parental population, leading to reduced detection power, insufficient localization accuracy, or even failure to detect certain loci. This severely restricts in-depth analysis of complex quantitative traits.

[0004] Nested Association Mapping (NAM) populations are a type of advanced multiparental genetic population developed in recent years. They are constructed by crossing a common reference parent with multiple different donor parents and then backcrossing them. NAM populations cleverly combine the advantages of biparental populations (simple genetic structure) and association analysis populations (high genetic diversity). Compared with biparental populations, NAM populations have the following significant advantages: (1) extremely rich genetic diversity, while integrating allelic variations from multiple parents; (2) higher mapping accuracy and resolution, because more recombination events and denser genetic markers can effectively narrow the confidence interval of QTLs; (3) wider application range, especially suitable for analyzing complex quantitative and qualitative traits controlled by multiple genes.

[0005] Despite the significant advantages of the NAM population, how to leverage its complex multi-parental genetic structure and develop efficient gene mapping methods to match it remains a pressing technical challenge in the field of plant genetics and breeding. Summary of the Invention

[0006] In view of the above, it is necessary to provide a gene localization method based on multi-parent nested association mapping populations, which fills the technical gap of lacking an efficient BSA analysis scheme for multi-parent populations.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A gene localization method based on multi-parent nested association mapping (NAM) populations includes the following steps:

[0009] (a) Provide a multiparental NAM population, which is a BC4F8 population obtained by crossing a common parent with at least three donor parents with different genetic backgrounds, backcrossing for at least four generations, and self-crossing for at least six generations;

[0010] (b) Individuals with extreme phenotypes are screened from each subpopulation of the NAM population based on phenotypic measurements of the target trait and genotypic analysis of known functional markers;

[0011] (c) Mix the DNA of individuals with extremely high phenotypes selected in step (b) in equal amounts to construct a high-value pool, and mix the DNA of individuals with extremely low phenotypes in equal amounts to construct a low-value pool, wherein each pool contains 15-25 individuals;

[0012] (d) Perform whole-genome resequencing on the high-value pool and the low-value pool to detect single nucleotide polymorphism (SNP) sites;

[0013] (e) Quality filtering of the detected SNP sites, the filtering including:

[0014] (i) Only biselenogene loci are retained;

[0015] (ii) Remove sites with a read support count less than 4 from any pool;

[0016] (iii) Remove loci that are homozygous and have the same genotype across all pools;

[0017] (f) Based on the filtered high-quality SNP sites, calculate the Δ(SNP-index) value of each SNP site between the high and low mixed pools, and use the 99% confidence interval of the fitted curve as the threshold to determine the genomic regions that are significantly associated with the target trait.

[0018] In this invention, the common parent is a cultivated rice variety, and the donor parent is a wild rice variety.

[0019] In this invention, further, each subpopulation of the BC4F8 population described in step (a) contains 297-314 strains.

[0020] In this invention, further, the functional marker in step (b) is primer XianSd1, the sequence of which is:

[0021] Upstream primer: AGCTGGACATGCCCGTGGTC (SEQ ID NO: 1);

[0022] Downstream primer: TTGAGCTGCTGTCCGCGAAG (SEQ ID NO: 2).

[0023] In this invention, the primer XianSd1 is further used to identify loss-of-function alleles of the rice Sd1 gene, wherein a 223 bp amplification product corresponds to a dwarf phenotype, and a 606 bp amplification product corresponds to a tall phenotype.

[0024] In this invention, further, in step (b) when screening individuals with extreme phenotypes, a high-value mixed pool is constructed for individuals with a loss-of-function Sd1 genotype but a tall plant height phenotype, and a low-value mixed pool is constructed for individuals with both a short Sd1 genotype and a short plant height phenotype.

[0025] In this invention, further, the high-value pool and low-value pool described in step (c) are both composed of individual DNA from different subpopulations, with each subpopulation contributing 2-4 individuals.

[0026] In this invention, the number of high-quality SNP sites obtained after filtering in step (e) is between 500,000 and 2,000,000.

[0027] In this invention, the significantly associated genomic regions described in step (f) are determined by a Δ(SNP-index) value greater than the 99% confidence interval threshold.

[0028] The present invention also provides an application of the method in locating rice plant height genes, wherein the gene is located in the interval of 4,600,000 bp to 5,760,000 bp on rice chromosome 8.

[0029] Compared with the prior art, the present invention has at least the following beneficial effects:

[0030] (1) This invention pioneered the combination of Nested Association Mapping (NAM) populations with Pool Segregation Analysis (BSA) technology, creating a new NAM-BSA method. Compared with the traditional biparental BSA technology, this method uses a common parent and at least three donor parents with different genetic backgrounds (six wild rice materials in the example) to construct a multi-parent population, integrating the allelic variations of multiple parents at once, thus significantly improving genetic diversity. As the phenotypic identification results show, the plant height variation range of the six BC4F8 populations all exceeded 90 cm (e.g., 84-195 cm), far exceeding the performance of the recipient parent 'Youzhan 8' (approximately 125 cm), providing unprecedentedly rich genetic material and phenotypic differences for the construction of extreme pools and subsequent efficient localization.

[0031] (2) This invention effectively improves the capability and resolution of QTL detection through multi-parental joint analysis. In the examples, by performing Δ(SNP-index) analysis on 1,126,885 high-quality SNPs (obtained through rigorous bioinformatics filtering), the target gene was successfully located within the range of 4.60-5.76 Mb (approximately 1.16 Mb) on rice chromosome 8. The accuracy of this range is due to the extremely high density of polymorphic markers provided by the multi-parental population. At the same time, this method innovatively combines functional marker (XianSd1) pre-screening with extreme phenotypic selection to construct a mixed pool, effectively eliminating background interference from known major genes (Sd1), directly focusing on new unknown sites, greatly enhancing the accuracy and specificity of the localization, and avoiding the false positive problem of traditional BSA in the localization of complex traits.

[0032] (3) While maintaining the advantages of low cost and high throughput of BSA technology, this invention solves the core problem that it cannot utilize the genetic information of multiple parents. In the example, only 20 extreme individuals need to be selected for each pool, and the target locus is successfully identified from the multiple parental background by resequencing and bioinformatics analysis of a total of 40 samples. Compared with the traditional QTL mapping method that requires genotyping of a large number of individuals, or the conventional BSA that can only analyze parental variation, this invention greatly saves time and economic costs, and is particularly suitable for the genetic analysis of complex quantitative traits (such as plant height, yield, quality, etc.) that are controlled by multiple genes and are easily affected by genetic background.

[0033] (4) This invention provides a complete and reproducible workflow from population construction, phenotypic and genotypic identification, pooling construction to bioinformatics analysis. The SNP filtering criteria (e.g., read support count ≥ 4) reference authoritative industry standards, ensuring the rigor and reproducibility of the analysis process. This method has not only been successfully applied to rice plant height gene mapping, but its core principle—using multi-parental NAM populations for BSA analysis—can be directly extended to other cereal crops (such as maize and wheat) and other plants and animals with similar genetic population construction capabilities. It can be used to clone genes for important agronomic traits and has enormous application potential in functional genomics research and marker-assisted breeding. Attached Figure Description

[0034] Figure 1 A schematic diagram illustrating the construction process of the NAM population; the diagram shows the NAM population with shared parental lineage Oil 8.

[0035] Figure 2 Box plots showing the plant height distribution of the six BC4F8 subpopulations;

[0036] Figure 3 This is a map showing the Δ(SNP-index) mapping of the whole genome of rice. Detailed Implementation

[0037] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0038] Example: A method for locating the rice plant height gene PH8 using NAM-BSA technology

[0039] (1) Construction of NAM population

[0040] This invention uses the superior indica rice cultivar 'Youzhan 8' (the common parent of the recipient) as the genetic background and crosses it with six wild rice (Oryza rufipogon) materials with broad genetic diversity (referred to as donor parents 1 to 6) to construct multi-parental NAM populations. The acquisition and use of all genetic materials comply with relevant national laws and regulations.

[0041] The specific process is as follows (see Figure 1 ):

[0042] Early 2012 season: Using 'Youzhan 8' as the female parent, it was hybridized with 6 donor wild rice varieties to obtain 6 F1 generation hybrids.

[0043] From 2013 to 2014: Backcrossing was carried out for four consecutive generations using 'Youzhan 8' as the recurrent parent. In each generation, single plants with significant wild rice traits (such as long awns, red lemma tips, and grain length) were selected for backcrossing.

[0044] Late 2014: 320 individual plants were selected from 160 BC4F1 individual plants based on phenotypic differences.

[0045] From 2015 to 2017: The selected individual plants were self-crossed for six consecutive generations to purify the strain.

[0046] 2018: Six stable BC4F8 populations were finally obtained, each containing 297-314 strains. The entire process was managed through strict tagging and numbering, enabling pedigree tracking of all materials.

[0047] (2) Phenotypic identification of plant height in NAM population

[0048] At the rice maturity stage, the plant height (vertical height from ground level to panicle tip) of all individual plants in the six BC4F8 populations was measured. Figure 2 The box plots show that the plant height of each population exhibits extensive and continuous variation, with the specific distribution range as follows:

[0049] The population of Guangxi Wild Rice No. 1 × Youzhan No. 8: plant height ranged from 89 to 195 cm, with an average of 116.7 cm;

[0050] The population of Guangxi wild rice No. 2 × Youzhan No. 8: plant height ranged from 87 to 184 cm, with an average of 113.3 cm;

[0051] Yuanjiang wild rice × Youzhan 8 population: plant height range 91-193 cm, average 116.2 cm;

[0052] Chaling wild rice × Youzhan 8 population: plant height range 84-176 cm, average 112.5 cm;

[0053] Dongxiang wild rice × Youzhan 8 population: plant height range 85-179 cm, average 113.1 cm;

[0054] Cambodian wild rice × Youzhan 8 population: plant height range 92-182 cm, average 114.4 cm.

[0055] Figure 2 In the test chamber, the interquartile range of plant height for each population was displayed as 25%–75%, with the whisker lines extending to the minimum and maximum values. The range of plant height variation for all populations covered >90 cm (e.g., 84–179 cm for the Dongxiang population), indicating that the genetic introgression of wild rice donor parents significantly expanded the phenotypic diversity of the cultivated rice background (compared to approximately 125 cm in plant height for the recipient parent, Oilseed No. 8).

[0056] (3) Genotyping based on functional markers

[0057] We designed a functional marker primer, XianSd1, for the known major dwarfing gene Sd1 in rice.

[0058] Primer sequences:

[0059] Upstream primer: 5'-AGCTGGACATGCCCGTGGTC-3' (SEQ ID NO: 1);

[0060] Downstream primer: 5'-TTGAGCTGCTGTCCGCGAAG-3' (SEQ ID NO: 2);

[0061] PCR identification principle: This primer can amplify a specific region of the Sd1 gene. If the amplification product is 606 bp, it indicates that the Sd1 gene is functioning normally, corresponding to the tall phenotype; if the amplification product is 223 bp, it indicates that there is a fragment deletion in the Sd1 gene, which is a loss-of-function allele, corresponding to the dwarf phenotype.

[0062] Genotyping was performed on individual plants from six BC4F8 populations using this primer. A small number of anomalous plants with a dwarf Sd1 genotype but a tall phenotype were found in each population, suggesting the possible existence of a new plant height gene locus masking the dwarfing effect of Sd1.

[0063] (4) Construction of extreme phenotypic mixed pool (High / Low Bulk)

[0064] Based on phenotypic and genotypic analysis, we selected extreme individuals from six BC4F8 populations to construct mixed pools:

[0065] High Bulk: Twenty aberrant plants with the Sd1 genotype of dwarf but the plant height phenotype were selected from each population. Genomic DNA was extracted from each plant and mixed in equal amounts to form the high-value pool.

[0066] Low Bulk: Twenty typical plants with both Sd1 genotype and short stature phenotype were selected from each population. DNA was extracted from each plant and mixed in equal amounts to form the low bulk.

[0067] Note: Statistical considerations for pool construction: In BSA analysis, the size of the pool needs to be determined by balancing sequencing cost and statistical power. In this study, 20 extreme individuals were selected for each pool. The study shows that, under conditions of high heritability of the target trait, pools of this size can effectively enrich the target alleles and provide sufficient statistical power for initial screening and mapping of QTLs. This practice is already well-established in the field. Subsequent significance testing can effectively control the risk of false positives.

[0068] Meanwhile, DNA was extracted from the recurrent parent 'Youzhan 8' as a control.

[0069] (5) Whole genome resequencing and SNP calling

[0070] DNA samples from 'Oil No. 8' and two mixed pools (high and low read lengths) were sequenced (150 bp). The raw sequencing data were filtered for quality and then compared with the Nipponbare reference genome (IRGSP-1.0) to detect single nucleotide polymorphisms (SNPs) across the entire genome.

[0071] (6) SNP filtering and acquisition of high-quality SNP datasets

[0072] To obtain reliable analytical results, we performed rigorous quality filtering on the initially detected SNPs, as follows:

[0073] (i) Only retain SNP sites that are dialleles in all samples, and remove multiallele sites;

[0074] (ii) Remove SNP sites with fewer than 4 sequencing read support in any pool. (This filtering criterion is a widely adopted threshold in the industry to ensure the accuracy of genotyping.)

[0075] (iii) Remove SNP loci that are homozygous and have the same genotype between the high and low pools.

[0076] After the above filtering, a total of 1,126,885 high-quality SNP sites that can be used for association analysis were finally obtained.

[0077] (7) Association analysis based on Δ(SNP-index) and PH8 gene localization

[0078] Calculate the SNP-index of each high-quality SNP locus in the high bulk and low bulk (denoted as SNP-index-H and SNP-index-L, respectively).

[0079] As mentioned above, SNP-index = total number of reads / number of minor allele reads / total number of reads; (total number of reads ≥ 4).

[0080] And calculate Δ(SNP-index) = |SNP-index-H - SNP-index-L|.

[0081] We used the sliding window method to calculate the genome-wide Δ(SNP-index) value and used the 99% confidence interval of the fitted curve as the significance threshold. The analysis revealed a highly significant peak region on rice chromosome 8, between 4.60 Mb and 5.76 Mb (physical location: 4,600,000 bp - 5,760,000 bp). This region was identified as a novel major-effect QTL controlling plant height and named PH8. Figure 3 As shown.

[0082] The successful localization of the PH8 region using this NAM-BSA method demonstrates its effectiveness. Full functional validation of the candidate gene (e.g., knockout or overexpression experiments using CRISPR / Cas9 gene editing technology) is the gold standard for confirming its biological function, and this will be pursued as a separate follow-up study. The core of this patent lies in providing a novel and efficient localization method, the effectiveness and value of which have been fully demonstrated by the successful localization of PH8 in this specific embodiment.

[0083] The above embodiments are merely examples of several implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention.

Claims

1. A method for gene positioning based on a multiple-parent nested association mapping population, characterized in that, The method comprises the following steps: (a) providing a multi-parental NAM population, which is a BC4F8 population obtained by crossing a common parent with at least three donor parents with different genetic backgrounds, at least four rounds of backcrossing and at least six rounds of selfing; (b) screening individuals with extreme phenotypes from each subpopulation of the NAM population based on phenotypic measurement of a target trait and genotypic analysis of known functional markers; (c) mixing equal amounts of DNA of individuals screened in step (b) to construct a high-value pool and mixing equal amounts of DNA of individuals screened in step (b) to construct a low-value pool, wherein each pool comprises 15-25 individuals; (d) performing whole-genome resequencing on the high-value pool and the low-value pool to detect single nucleotide polymorphism (SNP) sites; (e) performing quality filtering on the detected SNP sites, which comprises: (i) retaining only di-allelic sites; (ii) removing sites with read support less than 4 in any pool; (iii) removing sites with consistent genotypes among all pools and being homozygous; (f) calculating Δ(SNP-index) values of each SNP site between the high-value pool and the low-value pool based on the high-quality SNP sites after filtering, and determining a genomic region significantly associated with the target trait by taking the 99% confidence interval of the fitted curve as a threshold.

2. The method of claim 1, wherein, The common parent is a cultivated rice variety, and the donor parent is a wild rice variety.

3. The method of claim 1, wherein, Each subpopulation of the BC4F8 population in step (a) comprises 297-314 lines.

4. The method of claim 1, wherein, The functional marker in step (b) is primer XianSd1, and the sequence is: upstream primer: AGCTGGACATGCCCGTGGTC (SEQ ID NO: 1); downstream primer: TTGAGCTGCTGTCCGCGAAG (SEQ ID NO: 2).

5. The method of claim 4, wherein, The primer XianSd1 is used to identify a functional loss allele of the Sd1 gene in rice, wherein an amplification product of 223 bp corresponds to a dwarf phenotype, and an amplification product of 606 bp corresponds to a tall phenotype.

6. The method of claim 1, wherein, In step (b), individuals with a functional loss genotype of the Sd1 gene but a tall phenotype of plant height are used to construct the high-value pool, and individuals with both a functional loss genotype of the Sd1 gene and a dwarf phenotype of plant height are used to construct the low-value pool.

7. The method of claim 1, wherein, In step (c), the high-value pool and the low-value pool are each composed of DNA from individuals in different subpopulations, and each subpopulation contributes 2-4 individuals.

8. The method of claim 1, wherein, The number of high-quality SNP sites obtained after filtering in step (e) is 500,000-2,000,000.

9. The method of claim 1, wherein, In step (f), the significantly associated genomic region is determined by Δ(SNP-index) values greater than the 99% confidence interval threshold.

10. Use of the method according to any one of claims 1 to 9 for mapping a gene for plant height in rice, characterized in that, The gene is located in the interval of 4,600,000 bp to 5,760,000 bp on chromosome 8 of rice.

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