A method for screening genetic map-based chip markers

Abstract

The application provides a method for screening chip markers based on a genetic map. First, a basic SNP marker set is obtained, including a plurality of non-uniformly distributed basic SNP markers and physical positions. Then, a plurality of mandatory markers are obtained by comparing known functional sites with a reference genome. Then, the basic SNP marker set is converted into a genetic SNP marker set according to a genetic map, and the genetic SNP marker set at least includes the basic SNP markers and corresponding genetic positions of the basic SNP markers. Then, the genetic SNP marker set is divided according to a set genetic distance, and a plurality of marker intervals are obtained. Then, it is determined whether the mandatory markers exist in each marker interval. If not, the marker interval is supplemented with markers, and the chip markers are obtained. The genetic position is divided, genetic analysis can be more targeted, and the genetic analysis is more uniform.

Description

Technical Field

[0001] This application relates to the field of gene technology, specifically to a method for screening chip markers based on genetic maps. Background Technology

[0002] Breeding is an important means to increase crop yield. Conventional breeding methods mainly utilize trait records, pedigree calculations of inter-individual kinship, and comprehensively consider trait differences to select samples.

[0003] Using genetic markers for selection can save breeding costs and shorten the breeding cycle. Using genetic markers across the entire genome for selection is effective, but often expensive, especially for species with large gene pools like wheat. Breeding chips, by aggregating genetic information of representative traits across the entire genome, can significantly reduce costs while identifying genetically superior male and female rice parents in advance, and predicting and designing the performance of their offspring. Through selective breeding, the entire breeding cycle can be significantly shortened.

[0004] Microarray markers require screening for representative loci. Due to marker linkage, selecting a few markers within each linked region is sufficient to represent the genetic information of that region. Since linkage weakens with increasing distance, marker screening needs to be uniform to avoid excessive redundancy. Typically, the genome is divided into multiple marker regions, and SNP markers within these regions are screened to obtain microarray markers. However, if the genome is divided according to physical location, within a single region containing areas with varying recombination frequencies, some SNP markers may exhibit high linkage while others show weak linkage. This complicates subsequent steps (such as identifying trait-related gene markers) because the uneven linkage disequilibrium interferes with accurate determination of gene-trait associations. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, this application aims to provide a method for screening microarray markers based on genetic maps, comprising the following steps:

[0006] Obtain a set of basic SNP markers, which includes at least a plurality of non-uniformly distributed basic SNP markers and the physical locations corresponding to each basic SNP marker; wherein, the basic SNP markers represent single nucleotide variations at specific sites;

[0007] The known functional sites are compared with the reference genome to obtain a set of mandatory markers. The set of mandatory markers includes multiple mandatory markers, which are the sites of the functional sites on the reference genome.

[0008] The basic SNP marker set is converted into a genetic SNP marker set based on the genetic map. The genetic SNP marker set includes at least the basic SNP markers and the genetic locations corresponding to the basic SNP markers.

[0009] The genetic SNP marker set is divided according to a set genetic distance to obtain multiple marker intervals;

[0010] Determine whether there is a mandatory marker in each of the marked intervals. If not, supplement the marked interval with additional markers to obtain the final SNP marker set, which is the chip marker.

[0011] The technical solution provided in the embodiments of this application further includes the following steps:

[0012] The basic SNP marker set is annotated to obtain priority features corresponding to each basic SNP marker. The priority features include: intron region markers, exon synonymous mutation markers, and exon non-synonymous mutation markers.

[0013] According to the technical solution provided in the embodiments of this application, after dividing the genetic SNP marker set according to the set genetic distance to obtain multiple marker intervals, the following steps are further included:

[0014] The number of required markers within each of the marked intervals is obtained according to the search strategy and recorded as the number of required markers.

[0015] If the number of required selections is greater than or equal to 1, then all required selection markers are retained within the marker range.

[0016] The technical solution provided in the embodiments of this application further includes the following steps:

[0017] A contribution set is obtained based on the basic SNP tag set. The contribution set includes at least the basic SNP tag and its corresponding contribution, wherein the contribution represents the magnitude of the influence of the basic SNP tag on the phenotype.

[0018] By filtering the basic SNP tags, a set of non-redundant large-effect SNP tags is obtained, which includes multiple non-redundant large-effect SNP tags.

[0019] The process of supplementing the marked interval with additional markings includes the following steps:

[0020] When it is determined that the number of required selections is less than 1 and the number of non-redundant large-effect SNP tags is greater than or equal to 1, the contribution of the non-redundant large-effect SNP tags is obtained, and the non-redundant large-effect SNP tags with higher contribution are retained in the tag interval corresponding to them.

[0021] According to the technical solution provided in the embodiments of this application, the step of filtering the basic SNP tags to obtain a set of non-redundant large-effect SNP tags includes the following steps:

[0022] A set of high-confidence large-effect SNP markers is obtained based on the basic SNP markers. The set of high-confidence large-effect SNP markers includes multiple high-confidence large-effect SNP markers, and the high-confidence large-effect SNP markers are the basic SNP markers that are strongly correlated with the phenotype.

[0023] The set of high-confidence large-effect SNP tags is filtered to obtain the set of non-redundant large-effect SNP tags.

[0024] According to the technical solution provided in the embodiments of this application, obtaining a set of high-confidence, large-effect SNP tags based on the basic SNP tags includes the following steps:

[0025] Genome-wide association analysis was performed on the basic SNP markers and phenotypic data to obtain the phenotypic association value of each basic SNP marker. The larger the phenotypic association value, the higher the association with the phenotype.

[0026] Obtain the basic SNP tags corresponding to the correlation values ​​that are greater than or equal to the first preset threshold to obtain the initial high-confidence large-effect SNP tags;

[0027] QTL sites are obtained, which represent DAN regions that influence quantitative traits;

[0028] All the initial high-confidence large-effect SNP markers and the QTL sites are combined to form the high-confidence large-effect SNP set.

[0029] According to the technical solution provided in the embodiments of this application, the step of filtering the high-confidence large-effect SNP marker set to obtain the non-redundant large-effect SNP marker set includes the following steps:

[0030] Obtain the correlation between any two of the aforementioned high-confidence large-effect SNP markers;

[0031] Two highly reliable large-effect SNPs with a relevance greater than a preset relevance threshold are identified as redundant markers.

[0032] Remove the high-confidence large-effect SNP tags with low contribution from the redundant tags to obtain the set of non-redundant large-effect SNP tags.

[0033] According to the technical solution provided in the embodiments of this application, the PLINK software is used to calculate the correlation between any two of the high-confidence large-effect SNP tags.

[0034] The technical solution provided in the embodiments of this application further includes the following steps:

[0035] Obtain the basic SNP tags corresponding to correlation values ​​that are less than the first preset threshold and greater than the second preset threshold to obtain untrusted SNP tags;

[0036] The process of supplementing the marking of the marked interval also includes the following steps:

[0037] If the number of required selections is less than 1, the number of non-redundant large-effect SNP markers is less than 1, and the number of untrusted SNP markers is greater than or equal to 1, then the contribution of all untrusted SNP markers is obtained to obtain the first contribution set.

[0038] Determine the number of maximum contributions in the first set of contributions. If the number is 1, retain the non-redundant large effect SNP corresponding to the maximum contribution in the corresponding labeling interval.

[0039] If the number is greater than 1, obtain the highest priority non-redundant large effect SNP tag among at least one of the non-redundant large effect SNP tags corresponding to the maximum contribution, and retain it in the tag space.

[0040] According to the technical solution provided in the embodiments of this application, the step of supplementing the marking interval includes the following steps:

[0041] When the number of required selections is less than 1, the number of non-redundant large-effect SNP markers is less than 1, and the number of untrusted SNP markers is less than 1, the contribution of the basic SNP markers within the marker interval is obtained to obtain a second contribution set.

[0042] Determine the number of maximum contributions in the second contribution set. If the number is 1, retain the non-redundant large effect SNP label corresponding to the maximum contribution in the label interval corresponding to it.

[0043] If the number is greater than 1, obtain the highest priority basic SNP tag among at least one basic SNP tag corresponding to the maximum contribution, and retain it in the tag space.

[0044] In summary, this application proposes a method for screening microarray markers based on genetic maps. First, a basic SNP marker set is obtained, which includes at least multiple non-uniformly distributed basic SNP markers and their corresponding physical locations. Then, known functional loci are aligned with a reference genome to obtain a set of mandatory markers, which includes multiple mandatory markers, each representing a location of the functional locus on the reference genome. Next, the basic SNP marker set is converted into a genetic SNP marker set based on the genetic map. This genetic SNP marker set includes at least the basic SNP markers and their corresponding genetic locations. Then, the genetic SNP marker set is divided according to a set genetic distance to obtain multiple marker intervals. Finally, it is determined whether each marker interval contains a mandatory marker. If not, the interval is supplemented with additional markers to obtain the final SNP marker set, which is the microarray marker. This application, through genetic location segmentation, can group SNP markers with similar linkage disequilibrium levels together, facilitating more targeted genetic analysis within each marker interval and resulting in a more genetically uniform distribution. In addition, this application ensures that each marker interval contains sites related to important biological functions, thus guaranteeing the effectiveness and reliability of the microarray markers. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating a method for screening chip markers based on genetic maps, provided in an embodiment of this application. Detailed Implementation

[0046] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0048] As mentioned in the background section, this application proposes a method for screening microarray markers based on genetic maps, comprising the following steps:

[0049] S100. Obtain a basic SNP marker set, wherein the basic SNP marker set includes at least a plurality of non-uniformly distributed basic SNP markers and the physical location corresponding to each basic SNP marker; wherein the basic SNP markers represent single nucleotide variations at specific sites;

[0050] The process involves whole-genome sequencing of all samples to obtain a test SNP set, which includes multiple SNP markers and their corresponding physical locations. The basic SNP marker set is obtained by sequentially using FASTP software for quality control, BWA software for alignment, and GTAK software for detection, and filtering based on depth, deletion rate, and minimum allele, which is an existing technique and will not be described in detail here.

[0051] After obtaining the basic SNP tag set, the following steps are included:

[0052] S200. Align the known functional sites with the reference genome to obtain a set of mandatory markers, wherein the set of mandatory markers includes multiple mandatory markers, and the mandatory markers are the sites of the functional sites on the reference genome.

[0053] Known functional sites refer to genomic regions or sites that are known to have specific functions in the physiological, developmental, or other biological processes of organisms. This information is usually derived from previous research findings, public databases, or experimentally validated gene functional regions. A reference genome is a standard sequence template of a species' genome, a relatively complete and accurate genome sequence obtained by sequencing, assembling, and annotating the genomes of one or more individuals of that species. This process is the same as step S100, also using BWA software for alignment. Therefore, this process yields mandatory markers, which are site markers with key functions in the genome. These sites are determined based on previous research findings or biological knowledge.

[0054] Furthermore, after obtaining the basic SNP tag set, the following steps are also included:

[0055] S210. Annotate the basic SNP marker set to obtain a priority set. The priority set includes each basic SNP marker and its corresponding priority feature. The priority feature includes: intron region markers, exon synonymous mutation markers, and exon non-synonymous mutation markers.

[0056] The basic SNP marker set was annotated using Annovar software to obtain priority features. The gene includes exon and intron regions. Basic SNP markers located in exon regions include synonymous mutation markers and non-synonymous mutation markers. Specifically, if the codons before and after the mutation encode the same amino acid, the basic SNP marker in the exon region is an exon synonymous mutation marker; if the codons before and after the mutation encode different amino acids, the basic SNP marker in the exon region is an exon non-synonymous mutation marker. Therefore, the priority features include: intron region markers, exon synonymous mutation markers, and exon non-synonymous mutation markers.

[0057] Furthermore, after obtaining the basic SNP tag set, the following steps are also included:

[0058] S220. Obtain a contribution set based on the basic SNP tag set, wherein the contribution set includes at least the basic SNP tag and its corresponding contribution, wherein the contribution represents the magnitude of the influence of the basic SNP tag on the phenotype.

[0059] The method of calculating the contribution of each basic SNP tag by setting an algorithm and model is existing technology and will not be described in detail here.

[0060] S230. Filter the basic SNP markers to obtain a set of non-redundant large-effect SNP markers, wherein the set of non-redundant large-effect SNPs includes multiple non-redundant large-effect SNP markers; including the following steps:

[0061] S231. Based on the basic SNP markers, obtain a set of high-confidence large-effect SNP markers, wherein the set of high-confidence large-effect SNP markers includes multiple high-confidence large-effect SNP markers, and the high-confidence large-effect SNP markers are the basic SNP markers that are strongly correlated with the phenotype; including the following steps:

[0062] S231-1. Perform genome-wide association analysis on the basic SNP markers and phenotypic data to obtain the phenotypic association value of each basic SNP marker, wherein the larger the phenotypic association value, the higher the association with the phenotype;

[0063] S231-2. Obtain the basic SNP tags corresponding to the correlation values ​​that are greater than or equal to the first preset threshold to obtain initial high-confidence large-effect SNP tags; obtain the basic SNP tags corresponding to the correlation values ​​that are less than the first preset threshold and greater than the second preset threshold to obtain unconfidence SNP tags.

[0064] By linking basic SNP marker phenotypic data, gene loci that significantly influence phenotypes can be identified. For example, in studying plant disease resistance phenotypes, genome-wide association analysis (GWAS) can help locate genomic regions containing genes associated with disease resistance. Initial high-confidence, high-effect SNP markers are more reflective of phenotypes than unconfidenced SNP markers.

[0065] S231-3. Obtain QTL sites, where the QTL sites represent DAN regions that influence quantitative traits;

[0066] Among them, QTL (Quantitative Trait Locus) loci are those that have been studied and validated;

[0067] S231-4. Combine all the initial high-confidence large SNP markers and the QTL sites to form a high-confidence large-effect SNP set; the high-confidence large-effect SNP marker set includes multiple high-confidence large-effect SNP markers, and the high-confidence large-effect SNP markers are the basic SNP markers that are strongly correlated with the phenotype;

[0068] In genome-wide association studies (GWAS), some truly phenotype-related loci may be missed. QTL loci can supplement GWAS results and improve the set of high-confidence, large-effect SNPs.

[0069] S232. Filtering the set of high-confidence large-effect SNP markers to obtain the set of non-redundant large-effect SNP markers includes the following steps:

[0070] S232-1. Obtain the correlation between any two of the aforementioned high-confidence large-effect SNP markers;

[0071] In a preferred embodiment, the PLINK software is used to calculate the correlation between any two of the high-confidence large-effect SNP markers in the set of SNP markers.

[0072] The correlation coefficient matrix can be obtained by using PLINK software. The correlation coefficient matrix can clearly show the correlation between each pair of high-confidence large-effect SNP markers. For example, the correlation between high-confidence large-effect SNP marker 1 and high-confidence large-effect SNP marker 2 is 0.8, which means that the two are strongly correlated.

[0073] S232-2. Determine that the two highly reliable large-effect SNPs with a relevance greater than a preset relevance threshold are marked as redundant markers;

[0074] S232-3. Remove the high-confidence large-effect SNP tags with low contribution from the redundant tags to obtain a set of non-redundant large-effect SNP tags, wherein the set of non-redundant large-effect SNP tags includes multiple non-redundant large-effect SNP tags.

[0075] In high-confidence, large-effect SNP marker sets, factors such as linkage disequilibrium may lead to some overlap in the information carried by two high-confidence, large-effect SNP markers. Filtering the high-confidence, large-effect SNP marker set by contribution can ensure that the final retained markers more accurately reflect phenotype-related genetic variations. Furthermore, reducing redundant markers can decrease the complexity of subsequent data analysis.

[0076] S300. Convert the basic SNP marker set into a genetic SNP marker set according to the genetic map, wherein the genetic SNP marker set includes at least the basic SNP markers and the genetic location information corresponding to the basic SNP markers;

[0077] Physical location refers to the actual position of a SNP marker on the genomic DNA sequence, usually measured in base pairs (bp), and is marked along a linear sequence of the chromosome. For example, an SNP marker might be located at position 100,000-100,001 bp on a chromosome. Genetic location, on the other hand, is measured based on genetic linkage and recombination events, measured in centimorgans (cM), and reflects the probability of recombination of a gene or marker during meiosis.

[0078] The physical location of a base SNP marker is converted into its genetic location using a physical location-genetic location conversion algorithm or software tool.

[0079] S400. Divide the set of genetic SNP markers according to the set genetic distance to obtain multiple marker intervals.

[0080] Optionally, if the genetic distance is set to 1 cM and the total length of the genetic SNP marker set is 10 cM, then there are 10 marker intervals after the division. Since the basic SNP markers in the basic SNP marker set are not uniformly distributed, the number of genetic SNP markers in each marker interval is different.

[0081] S500. Determine whether there is a mandatory marker in each of the marked intervals. If not, supplement the marked interval with markers to obtain the final SNP marker set, which is the chip marker.

[0082] If the basic SNP marker set is divided according to physical location, within a defined interval, due to the inclusion of regions with different recombination frequencies, some SNP markers may exhibit high linkage while others show weak linkage. This can complicate subsequent analyses (such as identifying trait-related gene markers) because the uneven linkage disequilibrium can interfere with the accurate determination of gene-trait associations. However, dividing by genetic location allows SNP markers with similar degrees of linkage disequilibrium to be clustered together, facilitating more targeted genetic analysis within each interval and resulting in a more genetically uniform dataset. Furthermore, this application ensures that each marker interval contains loci associated with important biological functions, guaranteeing the effectiveness and reliability of the microarray markers.

[0083] In a preferred embodiment, after dividing the genetic SNP marker set according to a set genetic distance to obtain multiple marker intervals, the method further includes the following steps:

[0084] S600. Obtain the number of required markers in each of the marked intervals according to the search strategy, and record it as the number of required markers;

[0085] The search strategy includes the following steps:

[0086] a. Take any corresponding site within each marked interval as the starting site;

[0087] b. Start from the initial location and search for the required markers by setting the search distance;

[0088] S700. If the number of required selections is greater than or equal to 1, then all required selection markers are retained within the marker range.

[0089] The starting point can be the beginning or end of the marker interval, or any genetic locus within the marker interval. The principle for setting the starting point for each marker interval is the same. For example, if the genetic distance is set to 1 cM, then the length of the marker interval is 1 cM. The starting point is 0.2 cM away from the starting point of each marker interval. The search distance is 0.1 cM. Then, the search is conducted 0.1 cM upstream and downstream of 0.2 cM. If a required marker is found, all required markers are retained within the marker interval.

[0090] In a preferred embodiment, supplementing the marking of the marked interval includes the following steps:

[0091] S510. When it is determined that the number of required selections is less than 1 and the number of non-redundant large-effect SNP tags is greater than or equal to 1, the contribution of the non-redundant large-effect SNP tags is obtained, and the non-redundant large-effect SNP tags with higher contribution are retained in the tag interval corresponding to them.

[0092] That is, firstly, it is determined whether there are any mandatory labels within the labeling interval. If so, all mandatory labels are retained within the labeling interval. If not, it is determined whether there are any non-redundant large-effect SNP labels within the labeling interval. If so, the labels with higher contributions are retained within the labeling interval. The contribution of the non-redundant large-effect SNP labels is obtained from step S220.

[0093] In a preferred embodiment, the step of supplementing the marking of the marked interval further includes the following steps:

[0094] S520. When the number of required data is less than 1, the number of non-redundant large-effect SNP tags is less than 1, and the number of untrusted SNP tags is greater than or equal to 1, obtain the contribution of all untrusted SNP tags to obtain the first contribution set.

[0095] S521. Determine the number of maximum contributions in the first contribution set. If the number is 1, retain the non-redundant large effect SNP corresponding to the maximum contribution in the corresponding labeling interval.

[0096] S522. If the number is greater than 1, obtain the highest priority non-redundant large effect SNP tag among at least one of the non-redundant large effect SNP tags corresponding to the maximum contribution, and retain it in the tag space.

[0097] When there are no mandatory markers in the marking interval and no non-redundant large-effect SNP markers, it is determined whether there are untrusted SNP markers in the marking interval. If there are, the untrusted SNP marker with the largest contribution is selected and retained in the marking interval. If there are multiple untrusted SNP markers with the largest contribution, the one with the highest priority is selected from the multiple untrusted SNP markers corresponding to the largest contribution and retained in the marking space.

[0098] In step S210, the priority characteristics of each basic SNP marker are obtained. A priority determination strategy is then used to determine the priority of the corresponding untrusted SNP markers. The priority determination strategy is as follows: exon non-synonymous mutation markers are ranked higher than exon synonymous mutation markers, which are ranked higher than intron region markers. If all basic SNP markers within a marker interval have the same priority characteristics, then any untrusted SNP marker can be retained. The priority determination strategy is implemented by executing a self-written script.

[0099] In a preferred embodiment, supplementing the marking of the marked interval includes the following steps:

[0100] S530. When it is determined that the number of required selections is less than 1, the number of non-redundant large-effect SNP tags is less than 1, and the number of untrusted SNP tags is less than 1, the contribution of the basic SNP tags within the tag interval is obtained to obtain a second contribution set.

[0101] S531. Determine the number of maximum contributions in the second contribution set. If the number is 1, retain the non-redundant large effect SNP corresponding to the maximum contribution in the corresponding labeling interval.

[0102] S532. If the number is greater than 1, obtain the highest priority basic SNP tag among at least one basic SNP tag corresponding to the maximum contribution, and retain it in the tag space.

[0103] The priority is determined according to the priority judgment strategy mentioned above. When there are no mandatory markers, no non-redundant large-effect SNP markers, and no unreliable SNP markers in the marking interval, the contribution of the basic SNP markers in the marking interval is judged, and the one with the largest contribution is retained in the marking space. If there are multiple markers with the largest contribution, the one with the highest priority is selected from the basic SNP markers corresponding to the largest contribution and retained in the marking space.

[0104] This application improves the uniformity of chip marking by supplementing the marking to ensure that each marking space retains a marking. Furthermore, this application provides multiple methods for supplementing marking by sequentially selecting SNP markings in a fallback manner after determining whether there are mandatory markings, whether there are non-redundant large-effect SNP markings, and whether there are unreliable large-effect SNP markings, thereby improving the flexibility of supplementing marking.

[0105] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for screening microarray markers based on genetic maps, characterized in that, Includes the following steps: Obtain a set of basic SNP markers, which includes at least a plurality of non-uniformly distributed basic SNP markers and the physical locations corresponding to each basic SNP marker; wherein, the basic SNP markers represent single nucleotide variations at specific sites; The known functional sites are compared with the reference genome to obtain a set of mandatory markers. The set of mandatory markers includes multiple mandatory markers, which are the sites of the functional sites on the reference genome. The basic SNP marker set is converted into a genetic SNP marker set based on the genetic map. The genetic SNP marker set includes at least the basic SNP markers and the genetic locations corresponding to the basic SNP markers. The genetic SNP marker set is divided according to a set genetic distance to obtain multiple marker intervals; Determine whether there is a mandatory marker in each of the marked intervals. If not, supplement the marked interval with additional markers to obtain the final SNP marker set, which is the chip marker. After dividing the genetic SNP marker set according to the set of genetic distances and obtaining multiple marker intervals, the following steps are also included: The number of required markers within each of the marked intervals is obtained according to the search strategy and recorded as the number of required markers. If the number of required selections is greater than or equal to 1, then all required selection markers are retained within the marker range. A contribution set is obtained based on the basic SNP tag set. The contribution set includes at least the basic SNP tag and its corresponding contribution, wherein the contribution represents the magnitude of the influence of the basic SNP tag on the phenotype. By filtering the basic SNP tags, a set of non-redundant large-effect SNP tags is obtained, which includes multiple non-redundant large-effect SNP tags. Supplementing the marking of the marked interval includes: When it is determined that the number of required selections is less than 1 and the number of non-redundant large-effect SNP tags is greater than or equal to 1, the contribution of the non-redundant large-effect SNP tags is obtained, and the non-redundant large-effect SNP tags with higher contribution are retained in the tag interval corresponding to them. By filtering the basic SNP markers, a set of non-redundant large-effect SNP markers is obtained, including: A set of high-confidence large-effect SNP markers is obtained based on the basic SNP markers. The set of high-confidence large-effect SNP markers includes multiple high-confidence large-effect SNP markers, and the high-confidence large-effect SNP markers are the basic SNP markers that are strongly correlated with the phenotype. The set of high-confidence large-effect SNP markers is filtered to obtain the set of non-redundant large-effect SNP markers; Based on the aforementioned basic SNP tags, the set of high-confidence, large-effect SNP tags includes: Genome-wide association analysis was performed on the basic SNP markers and phenotypic data to obtain the phenotypic association value of each basic SNP marker. The larger the phenotypic association value, the higher the association with the phenotype. Obtain the basic SNP tags corresponding to the correlation values ​​that are greater than or equal to the first preset threshold to obtain the initial high-confidence large-effect SNP tags; Identify QTL sites, which represent DNA regions that influence quantitative traits; All the initial high-confidence large-effect SNP markers and the QTL sites are combined to form the high-confidence large-effect SNP marker set; The set of high-confidence large-effect SNP markers, filtered to obtain the set of non-redundant large-effect SNP markers, includes: Obtain the correlation between any two of the aforementioned high-confidence large-effect SNP markers; Two highly reliable large-effect SNPs with a relevance greater than a preset relevance threshold are identified as redundant markers. Remove the high-confidence large-effect SNP tags with low contribution from the redundant tags to obtain the set of non-redundant large-effect SNP tags.

2. The method for screening chip markers based on genetic maps according to claim 1, characterized in that, It also includes the following steps: The basic SNP marker set is annotated to obtain priority features corresponding to each basic SNP marker. The priority features include: intron region markers, exon synonymous mutation markers, and exon non-synonymous mutation markers.

3. The method for screening chip markers based on genetic maps according to claim 1, characterized in that, The correlation between any two high-confidence large-effect SNP markers can be calculated using PLINK software.

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