Method and system for association analysis of genetic variants with phenotypic information

Abstract

The present application relates to a gene mutation and phenotype information association analysis method and system, belonging to the technical field of automatic medical analysis. The method comprises the following steps: S1: obtaining a high-frequency pathogenic gene mutation white list; S2: obtaining genotype data of the object to be analyzed, comparing with the gene mutation in the white list, taking the intersection, and obtaining the high-frequency pathogenic gene mutation; S3: obtaining the high-frequency pathogenic gene mutation and the phenotype information of the object to be analyzed, performing high-frequency pathogenic gene mutation association analysis, and obtaining the associated high-frequency pathogenic gene mutation set; S4: obtaining the genotype data and the phenotype information of the object to be analyzed, performing conventional association analysis, and obtaining the associated gene mutation set; S5: obtaining the associated high-frequency pathogenic gene mutation set and the associated gene mutation set, taking the union, and outputting the gene mutation list in the union, which is the candidate pathogenic gene mutation list. The method can solve the problem of false negatives in conventional analysis methods (software).

Description

Technical Field

[0001] This invention relates to the field of automated medical analysis technology, and in particular to a method and system for analyzing the correlation between gene variation and phenotypic information. Background Technology

[0002] In traditional genetic disease diagnosis and analysis based on high-throughput gene sequencing results, genetic analysts need to filter and screen hundreds or even thousands of gene variants, which is time-consuming and labor-intensive. However, with the continuous advancement of bioinformatics technology, phenotypic information-driven intelligent diagnostic technologies for genetic diseases have gradually matured, demonstrating great potential in assisting traditional genetic disease diagnosis and analysis. These intelligent diagnostic technologies use the patient's phenotypic and genotypic information as input. After an automated screening and filtering process, they output a sorted list of candidate pathogenic gene variants. Then, genetic analysts only need to further investigate a few dozen candidate gene variants in this list. Compared to the traditional, entirely manual analysis process, intelligent diagnostic technology provides analysts with a streamlined candidate list, reducing the scope of analysis and screening required, and saving labor costs for relevant companies or organizations.

[0003] However, current intelligent diagnostic strategies or software produce false negative results, which has caused difficulties for genetic analysts. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for analyzing the correlation between gene variation and phenotypic information to address the problem of false negative conclusions in the aforementioned intelligent diagnostic software, which can reduce the occurrence of false negatives.

[0005] A method for association analysis between gene variation and phenotypic information includes the following steps:

[0006] S1: Obtain a whitelist of high-frequency pathogenic gene mutations;

[0007] S2: Obtain the genotype data of the object to be analyzed, compare the gene variants therein with the gene variants in the whitelist above, take the intersection, and obtain the high-frequency pathogenic gene variants of the object to be analyzed.

[0008] S3: Obtain the high-frequency pathogenic gene variants and phenotypic information of the objects to be analyzed, perform association analysis of high-frequency pathogenic gene variants, and obtain the associated high-frequency pathogenic gene variants that are associated with the phenotype of the objects to be analyzed, which will be used as the associated high-frequency pathogenic variant set for later use.

[0009] S4: Obtain the genotype data and phenotypic information of the objects to be analyzed, perform routine association analysis, and obtain the associated gene variants as the associated gene variant set for later use;

[0010] S5: Obtain the associated high-frequency pathogenic gene variant set and the associated gene variant set, compare them, take the union, and output the gene variant list in the union, which is the candidate pathogenic gene variant list.

[0011] In their practical work, the inventors discovered that when using intelligent diagnostic software for genetic diseases such as Exomiser (https: / / github.com / exomiser / Exomiser, version 13.1.0), PhenIX (https: / / compbio.charite.de / PhenIX / , version 1.16), AMELIE (https: / / amelie.stanford.edu / , version 3.1.0), and LIRCIAL (https: / / github.com / TheJacksonLaboratory / LIRICAL, version 1.3.4) for data analysis, one reason for false negatives is as follows:

[0012] After inputting phenotypic and genotypic information, the intelligent genetic disease diagnostic software filters and sorts all gene variants in the genotype data file (VCF) of the analyzed object based on phenotypic information and the characteristics of each gene variant, such as variant type, pathogenicity predicted by the algorithm, and population gene frequency. Finally, it outputs a list of candidate pathogenic gene variants. Population gene frequency is one of the important indicators for gene variant filtering. Population gene frequency is the relative frequency of a particular allele at a gene locus in a normal population. Generally speaking, true pathogenic gene variants are relatively rare in the normal population, so their population gene frequency values ​​are relatively low. Conventional intelligent genetic disease diagnostic software, when evaluating and searching for candidate pathogenic gene variants, will treat variants with population gene frequencies above a certain threshold (e.g., 0.5%) as benign variants and filter them out, retaining variants below the threshold for further evaluation and screening.

[0013] However, because some pathogenic gene mutations have relatively high frequencies in the population (e.g., above 0.5%), these "high-frequency pathogenic" gene variants are often filtered out by existing intelligent genetic disease diagnostic software under default conditions, treating them as benign variants. This can lead to false negatives in genetic analysis and evaluation. However, considering that some "high-frequency pathogenic" gene variants with excessively high population frequencies (e.g., above 10%) do exist clinically, increasing the population gene frequency filtering threshold would result in a large number of negative gene variants in the output list, contradicting the original intention of reducing the workload of analysis and evaluation personnel and achieving "intelligent evaluation."

[0014] Based on this, in order to avoid false negatives that may occur when conventional intelligent diagnostic software for genetic diseases diagnoses patients carrying "high-frequency pathogenic" gene variants, the inventors proposed the above-mentioned gene variant and phenotypic information association analysis method. By introducing a whitelist of high-frequency pathogenic gene variants, high-frequency pathogenic genes are evaluated separately, and the associated high-frequency pathogenic gene variants are added as a supplement to the final set of associated gene variants. This not only solves the problem of false negatives in conventional analysis methods (software), but also avoids the problem of decreased analysis accuracy and a large amount of negative gene variant data that may result from raising the population variant frequency threshold.

[0015] Understandably, the above phenotypic information uses standardized HPO (human phenotype ontology) entries to represent the phenotypic characteristics (such as clinical symptoms) of the subject being analyzed (e.g., a patient). For example, HP:0001250 represents "seizures". Typically, a patient's phenotypic information can be represented by multiple HPO entries, such as "HP:0001942 (metabolic acidosis), HP:0002900 (hypokalemia), HP:0011423 (hyperchloremia), HP:0002049 (proximal renal tubular acidosis)".

[0016] The genotypic data mentioned above is recorded in VCF (variant call format) files. VCF files contain information on all gene variants of the patient, such as variant location and variant type, and are one of the standard storage files for gene sequencing data.

[0017] In one embodiment, the whitelist of high-frequency pathogenic gene variants is obtained by collecting high-frequency pathogenic gene variants in populations confirmed to be pathogenic from literature and / or clinical practice, and compiling the whitelist. It is understood that the aforementioned whitelist of high-frequency pathogenic gene variants can be adjusted based on the accumulation of actual testing data and scientific developments, such as through updates, expansions, or deletions.

[0018] In one embodiment, the genes in the high-frequency pathogenic gene variant whitelist include: PRRT2, G6PD, UGT1A1, ACAD9, GJB2, HFE, MEFV, PIBF1, ACADS, and BTD genes.

[0019] Specifically, the detailed information of the genes in the above-mentioned whitelist of high-frequency pathogenic gene variants (reference genome version number: hg19) is as follows:

[0020]

[0021]

[0022] In one embodiment, the association analysis of high-frequency pathogenic gene variants employs the following method:

[0023] S31: Obtain gene-HPO phenotype association information;

[0024] S32: Obtain the HPO phenotype entries corresponding to the high-frequency pathogenic gene variants of the object to be analyzed, and compare the HPO phenotype entries with the phenotype information of the object to be analyzed. If there is an intersection, the high-frequency pathogenic gene variant is determined to be an associated high-frequency pathogenic gene variant and included in the associated high-frequency pathogenic gene variant set.

[0025] In one embodiment, after the association analysis of high-frequency pathogenic gene variants, the association degree of high-frequency pathogenic gene variants is calculated, and a list of high-frequency pathogenic gene variants is output in descending order of association degree. The method for calculating the association degree of high-frequency pathogenic gene variants is as follows:

[0026] Association degree of high-frequency pathogenic gene variants = number of HPO entries with overlap ÷ total number of HPO entries in the phenotypic information of the subject to be analyzed × 100%.

[0027] In one embodiment, the conventional variant association analysis employs the following method:

[0028] S41: Obtain genotype data, population gene frequency of gene variants, and pathogenicity rating information of the subjects to be analyzed;

[0029] S42: Remove gene variants with a population gene frequency higher than a threshold. The remaining gene variants are scored based on population gene frequency and pathogenicity rating information to obtain a variant score. Remove gene variants with a variant score lower than a predetermined value. The remaining variants are gene variants to be associated.

[0030] S43: Obtain data from the gene-phenotype association database, map the gene variant to be associated to the corresponding phenotype, and then perform association analysis between the mapped phenotype and the patient phenotype data. If the mapped phenotype is related to the patient phenotype data, then the gene variant to be associated is an associated gene variant and is included in the associated gene variant set.

[0031] In one embodiment, the thresholds are set as follows: 0.1% for dominant mutation, 0.1% for recessive homozygous mutation, 2.0% for recessive compound heterozygous mutation, and 0.2% for mitochondrial mutation.

[0032] In one embodiment, the following step S44 is also included: S44: Assign a corresponding phenotypic score to each gene variant based on the correlation strength between the mapped phenotype and the patient phenotypic data, add the variant score and the phenotypic score to obtain a comprehensive score, and output a list of associated gene variants in descending order of comprehensive score.

[0033] Understandably, the list of high-frequency pathogenic gene variants and the list of output-related gene variants together constitute the candidate pathogenic gene variant list.

[0034] In one embodiment, the method is performed by an electronic device.

[0035] Understandably, the above-mentioned methods for analyzing the association between gene variations and phenotypic information are used for non-diagnostic and non-therapeutic purposes.

[0036] This invention also discloses a system for analyzing the association between gene variation and phenotypic information, comprising:

[0037] Data storage module, used to store data information;

[0038] The data analysis module is used to perform analysis according to the aforementioned method for correlation analysis between gene variation and phenotypic information; and

[0039] The data display module is used to output and display the list of candidate gene variants associated with the phenotype.

[0040] The present invention also discloses a computer-readable storage medium storing a computer program that, when executed by the processor, implements the gene variation and phenotypic information association analysis method as described above.

[0041] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for analyzing the association between gene variation and phenotypic information as described above.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention provides a method for analyzing the association between gene variation and phenotypic information. By introducing a whitelist of high-frequency pathogenic gene variations, high-frequency pathogenic gene variations are evaluated separately. The associated high-frequency gene variations are then added to the final set of associated gene variations as a supplement. This method not only solves the problem of false negatives in conventional analysis methods (software), but also avoids the problem of decreased analysis accuracy and the large amount of negative gene variation data that may result from raising the population variation frequency threshold. Attached Figure Description

[0044] Figure 1 This is a flowchart of the gene variation and phenotypic information association analysis method in Example 1. Detailed Implementation

[0045] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] The term "gene variation" as used in this article refers to "a variant site on a gene," as shown in the example table below, where each row represents a gene variation. A gene variation corresponds to a specific coordinate. It can be understood that a gene may contain one or more gene variations (e.g., the UGT1A1 gene in the table contains two gene variations).

[0048] Unless otherwise specified, the methods used in the following embodiments are conventional methods that can be implemented.

[0049] Example 1

[0050] A method for analyzing the association between gene variation and phenotypic information, the process is as follows: Figure 1 As shown, it includes the following steps:

[0051] S1: Obtain a whitelist of high-frequency pathogenic gene mutations.

[0052] Understandably, the aforementioned white list of high-frequency pathogenic gene variants can be obtained through the collection of literature and clinical practice. When a high-frequency gene variant is confirmed to be pathogenic, it is collected as a high-frequency pathogenic gene variant to form a white list of high-frequency pathogenic gene variants.

[0053] In this embodiment, the inventors, relying on the clinical testing big data collected by their unit and with the assistance of literature, compiled the following white list of high-frequency pathogenic gene mutations.

[0054] Table 1. List of high-frequency pathogenic gene variants (Reference genome version: hg19)

[0055] Gene chromosome coordinate Reference base Mutant bases Maximum population frequency (%) PRRT2 16 29825015 GC G 0.96 G6PD X 153760472 C T 0.70 UGT1A1 2 234669144 G A 15.3 UGT1A1 2 234668879 C CAT 40.4 ACAD9 3 128598490 C CTAAG 12.6 GJB2 13 20763612 C T 8.3 HFE 6 26091179 C G 14.4 HFE 6 26093141 G A 5.8 MEFV 16 3299586 G A 7.1 MEFV 16 3299468 C T 5.4 PIBF1 13 73409497 G A 10.2 ACADS 12 121175678 C T 6.3 BTD 3 15686693 G C 5.6

[0056] S2: Obtain the genotype data (VCF file) of the subject to be analyzed (patient), compare the gene variants in the data with the gene variants in the whitelist above, take the intersection, and obtain the high-frequency pathogenic gene variants of the subject to be analyzed.

[0057] S3: Obtain the high-frequency pathogenic gene variants of the above patients and the phenotypic information (HPO entries) of the subjects to be analyzed, perform high-frequency pathogenic gene variant association analysis, and obtain the associated high-frequency pathogenic gene variants that are associated with the phenotype of the subjects to be analyzed, which will be used as the associated high-frequency pathogenic gene variant set for later use.

[0058] The association analysis of the above-mentioned high-frequency pathogenic gene variants specifically adopted the following methods:

[0059] S31: Obtain gene-HPO phenotype association information;

[0060] Understandably, the gene-HPO phenotype association information can be obtained from the HPO official website (https: / / hpo.jax.org / ) as a summary table of "gene-HPO" association information.

[0061] S32: Obtain the HPO phenotype entries corresponding to the high-frequency pathogenic gene variants of the object to be analyzed, and compare the HPO phenotype entries with the phenotype information of the object to be analyzed. If there is an intersection, the high-frequency pathogenic gene variant is determined to be an associated high-frequency pathogenic gene variant and included in the associated high-frequency pathogenic gene variant set.

[0062] S33: Calculate the association degree of high-frequency pathogenic gene variants for the high-frequency pathogenic gene variants included in the associated high-frequency pathogenic gene variant set, and output the gene variant list in descending order of association degree.

[0063] The method for calculating the association degree of the high-frequency pathogenic gene variants is as follows:

[0064] Association degree of high-frequency pathogenic gene variants = Number of HPO entries with overlap ÷ Total number of HPO entries in the phenotypic information of the subject to be analyzed × 100%

[0065] The obtained high-frequency pathogenic gene variants are sorted from high to low in terms of correlation and then added to the candidate pathogenic gene variant list.

[0066] The following are examples:

[0067] For example, if a patient's genotype data contains a "high-frequency pathogenic" gene variant in the G6PD gene, the "gene-HPO" association information table shows that the G6PD gene corresponds to a total of 21 HPO phenotype entries, including HP:0004447, HP:0001423, HP:0001945, and HP:0011463. Simultaneously, the patient's clinical information can be summarized as three HPO phenotype entries: HP:0000126, HP:0000787, and HP:0001945. Therefore, HP:0001945 represents the intersection of two sets of HPO entries. Finally, the association degree of this G6PD "high-frequency pathogenic" gene variant is calculated as: 1 ÷ 3 × 100% = 33.3%.

[0068] S4: Obtain the genotype data and phenotypic information of the objects to be analyzed, perform routine association analysis, and obtain associated gene variants as an associated gene variant set for later use.

[0069] The above-mentioned conventional variation association analysis specifically adopts the following methods:

[0070] S41: Obtain genotype data, population gene frequency of gene variants, and pathogenicity rating information of the subjects to be analyzed;

[0071] S42: Remove gene variants with a population gene frequency higher than a threshold. The remaining gene variants are scored based on population gene frequency and pathogenicity rating information to obtain a variant score. Remove gene variants with a variant score lower than a predetermined value. The remaining variants are gene variants to be associated.

[0072] S43: Obtain data from the gene-phenotype association database, map the gene variant to be associated to the corresponding phenotype, and then perform association analysis between the mapped phenotype and the patient phenotype data. If the mapped phenotype is related to the patient phenotype data, then the gene variant to be associated is an associated gene variant and is included in the associated gene variant set. If the mapped phenotype is not related to the patient phenotype data, then the gene variant to be associated is filtered out and removed, and is not output as a candidate gene variant.

[0073] S44: Assign a corresponding phenotypic score to each gene variant based on the correlation between the mapped phenotype and the patient's phenotypic data. Add the variant scores and phenotypic scores to obtain a comprehensive score, and output the gene variant list in descending order of comprehensive score.

[0074] Understandably, the aforementioned conventional variant association analysis methods can be directly processed using common intelligent diagnostic software for genetic diseases such as Exomiser, PhenIX, AMELIE, and LIRCIAL. The software first filters the input genotype data based on variant type, population gene frequency, pathogenicity prediction score, and the rating and inclusion status in databases such as HGMD / ClinVar. The obtained gene variant sites are then associated with phenotypic information, and finally, the gene variants are ranked according to the strength of the association, and a candidate list is output.

[0075] Taking the Exomiser software (https: / / github.com / exomiser / Exomiser, version 13.1.0) as an example, the software first annotates the VCF file; then it sequentially removes gene variants of non-exon type and those with population gene frequencies exceeding a set threshold (dominant variant: 0.1%, recessive homozygous variant: 0.1%, recessive compound heterozygous variant: 2.0%, mitochondrial variant: 0.2%); next, Exomiser assigns a variant score to each gene variant based on population gene frequency values ​​and the prediction scores from pathogenicity prediction software (MutationTaster, Polyphen2, and SIFT); in the phenotype association stage, Exomiser maps the gene variants obtained in the previous step to corresponding phenotypes in existing human, mouse, and zebrafish gene-phenotype association databases, then performs association analysis between these phenotypes and patient phenotype data, assigning a phenotype score to each gene variant based on the strength of the correlation; finally, Exomiser calculates a phenotype score for each gene variant based on both the variant score and phenotype. The combined score is used to output a list of associated gene variants sorted by the combined score from highest to lowest.

[0076] S5: Obtain the associated high-frequency pathogenic gene variant set and the associated gene variant set, compare them, take the union, and output the gene variant list in the union, which is the candidate pathogenic gene variant list.

[0077] The aforementioned method for analyzing the association between gene variants and phenotypic information, by introducing a whitelist of high-frequency pathogenic gene variants and evaluating these variants separately, supplements the final set of associated high-frequency pathogenic gene variants. This approach not only solves the false negative problem inherent in conventional analysis methods (software) but also avoids the potential decrease in analytical accuracy caused by raising the population gene variant frequency threshold. It improves analytical accuracy while leveraging intelligent software analysis, thus reducing the workload of analysts.

[0078] Example 2

[0079] Using the method described in Example 1, genetic disease diagnosis analysis was performed on the patient based on Exomiser software.

[0080] The patient is a seven-month-old male infant with epilepsy (HP:0001250) and presenting with status epilepticus (HP:0032660). The patient's genotype data (VCF file) was compared with gene variants in the whitelist, and the intersection was taken. The high-frequency pathogenic gene variants for the analyzed subject were found to be PRRT2 (16-29825015-GC-G). The patient has two phenotypic information entries (HPO entries): HP:0001250 and HP:0032660. By querying the "Gene-HPO" association information table in S31, it was found that PRRT2 corresponds to a total of 116 HPO entries, including the HPO entry for one patient (HP:0001250). According to step 5, the phenotypic association degree is calculated to be 50%.

[0081] Association analysis of high-frequency pathogenic gene variants revealed associated high-frequency pathogenic gene variants that are associated with the phenotype of the subject under analysis, including: PRRT2 (16-29825015-GC-G).

[0082] The patient's genotype data (VCF file) and phenotypic information (HPO entries) were input into the Exomiser software. According to the threshold setting in Example 1, the list of associated gene variants was obtained as shown in Table 2 below.

[0083] Table 2. List of candidate pathogenic gene variants directly output by Exomiser software (top 10 sorted)

[0084] Ranking Gene chromosome coordinate Reference base Mutant bases Total Score 1 GAL 11 68455544 C T 0.9713 2 GRIK2 6 102483286 A G 0.9233 3 ADGRB1 8 143562684 G A 0.8241 4 ZSCAN32 16 3434525 C A 0.8204 5 CTBP2 10 126727614 CA C 0.7858 6 TLL1 4 166996084 C T 0.7825 7 STYK1 12 10782120 A T 0.7786 8 SLC17A6 11 22396340 A G 0.7664 9 CDC27 17 45234360 A C 0.7651 10 HLA-DRB1 6 32551954 CG - 0.7641

[0085] The high-frequency pathogenic gene variants obtained according to the method in Example 1 are shown in Table 3 below.

[0086] Table 3. List of high-frequency pathogenic gene variants obtained according to the method in Example 1

[0087] Ranking Gene chromosome coordinate Reference base Mutant bases Phenotypic correlation 1 PRRT2 11 68455544 C T 50％

[0088] As can be seen from the results, in Table 2, the true pathogenic gene variant PRRT2 (16-29825015-GC-G) was filtered out because its population frequency value was higher than the threshold of Example 1, and therefore did not appear in the final list of candidate pathogenic gene variants. After using the association analysis method of the present invention, PRRT2 (16-29825015-GC-G) was protected and located in the high-frequency pathogenic gene variant appendix, as shown in Table 3.

[0089] Example 3

[0090] A review of data from 152 patients with genetic diseases in our unit was conducted. The diagnostic analysis was performed using the method described in Example 1, and 3 cases were found to have associated high-frequency pathogenic gene mutations, which reduced the false negative rate by 2%.

[0091] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for analyzing the association between gene variation and phenotypic information, characterized in that, Includes the following steps: S1: Obtain a white list of high-frequency pathogenic gene variants; the white list of high-frequency pathogenic gene variants is obtained by the following method: collecting high-frequency pathogenic gene variants in populations confirmed to be pathogenic in literature and / or clinical practice, and forming a white list of high-frequency pathogenic gene variants. S2: Obtain the genotype data of the object to be analyzed, compare the gene variants therein with the gene variants in the whitelist above, take the intersection, and obtain the high-frequency pathogenic gene variants of the object to be analyzed. S3: Obtain the high-frequency pathogenic gene variants and phenotypic information of the objects to be analyzed, perform high-frequency pathogenic gene variant association analysis, obtain the associated high-frequency pathogenic gene variants that are associated with the phenotype of the objects to be analyzed, and use them as the associated high-frequency pathogenic gene variant set. Calculate the association degree of high-frequency pathogenic gene variants, and output the list of high-frequency pathogenic gene variants in descending order of association degree. The calculation method of the association degree of high-frequency pathogenic gene variants is as follows: High-frequency pathogenic gene variant association degree = Number of HPO entries with intersection ÷ Total number of HPO entries in the phenotypic information of the objects to be analyzed × 100%; The association analysis of high-frequency pathogenic gene variants adopts the following method: S31: Obtain gene-HPO phenotype association information; S32: Obtain the HPO phenotype entry corresponding to the high-frequency pathogenic gene variant of the object to be analyzed, and compare the HPO phenotype entry with the phenotype information of the object to be analyzed. If there is an intersection, the high-frequency pathogenic gene variant is determined to be an associated high-frequency pathogenic gene variant and included in the associated high-frequency pathogenic gene variant set. S4: Obtain the genotype data and phenotypic information of the objects to be analyzed, perform routine association analysis, and obtain the associated gene variants as the associated gene variant set for later use; S5: Obtain the associated high-frequency pathogenic gene variant set and the associated gene variant set, compare them, take the union, and output the gene variant list in the union, which is the candidate pathogenic gene variant list.

2. The method for analyzing the association between gene variation and phenotypic information according to claim 1, characterized in that, The genes in the whitelist of high-frequency pathogenic gene variants include: PRRT2, G6PD, UGT1A1, ACAD9, GJB2, HFE, MEFV, PIBF1, ACADS, and BTD genes.

3. The method for analyzing the association between gene variation and phenotypic information according to claim 1, characterized in that, The conventional variation association analysis was performed using the following method: S41: Obtain genotype data, population gene frequency of gene variants, and pathogenicity rating information of the subjects to be analyzed; S42: Remove gene variants with a population gene frequency higher than a threshold. The remaining gene variants are scored based on population gene frequency and pathogenicity rating information to obtain a variant score. Remove gene variants with a variant score lower than a predetermined value. The remaining variants are gene variants to be associated. S43: Obtain data from the gene-phenotype association database, map the gene variant to be associated to the corresponding phenotype, and then perform association analysis between the mapped phenotype and the patient phenotype data. If the mapped phenotype is related to the patient phenotype data, then the gene variant to be associated is an associated gene variant and is included in the associated gene variant set.

4. The method for analyzing the association between gene variation and phenotypic information according to claim 3, characterized in that, The thresholds are set as follows: 0.1% for dominant mutation, 0.1% for recessive homozygous mutation, 2.0% for recessive compound heterozygous mutation, and 0.2% for mitochondrial mutation.

5. The method for analyzing the association between gene variation and phenotypic information according to claim 3, characterized in that, It also includes the following S44 step, S44: Assign a corresponding phenotypic score to each gene variant based on the correlation strength between the mapped phenotype and the patient's phenotypic data. Add the variant scores and phenotypic scores to obtain a comprehensive score. Sort the comprehensive scores from high to low and output the list of associated gene variants.

6. The method for analyzing the association between gene variation and phenotypic information according to any one of claims 1-5, characterized in that, The method is performed by an electronic device.

7. A system for analyzing the association between gene variation and phenotypic information, characterized in that, include: Data storage module, used to store data information; The data analysis module is used to perform analysis according to the gene variation and phenotypic information association analysis method according to any one of claims 1-6; as well as The data display module is used to output and display the list of candidate gene variants associated with the phenotype.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

9. A computer device, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1-6.

Images