Method for analyzing gene mutations based on nucleic acid sequence analysis

Abstract

Since the types of gene mutations detected by NGS are very wide and not all gene mutations always lead to disease, it is difficult to quickly and accurately analyze the meaning of the detected gene mutations in relation to disease. The present invention relates to a gene mutation analysis method based on nucleic acid sequence analysis, and the gene mutation analysis method of the present invention provides an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines, and is expected to be widely used in the fields of life science and medical health care.

Description

[Technical field]

[0001] The present invention relates to a method for analyzing gene mutations based on nucleic acid sequence analysis. [Background technology]

[0002] Next Generation Base Sequence Analysis (NGS), a technology for decoding large volumes of base sequences With the rapid development of next-generation sequencing (NGS), research into genome data, including mutation tracking, has been actively conducted, and efforts are ongoing to utilize this data for disease diagnosis. However, the types of gene mutations detected by NGS are very wide-ranging, and some gene mutations only induce simple phenotypic differences, so not all gene mutations necessarily lead to disease, making it difficult to quickly and accurately analyze the meaning of the detected gene mutations in terms of disease association. In addition, there is a need for technology that can communicate the meaning of gene mutations analyzed from NGS information in common terms to facilitate smooth communication between scientists and medical professionals around the world. In this context, the American Medical College of Medical Genetics and Genomics (ACMG), the Association for Molecular Pathology (AMP), and the College of American Pathologists (CGA) have jointly established the WHO-affiliated National Institute of Medical Genetics (NICU) and the WHO-affiliated National Institute of Medical Genetics (WHO-affiliated National Institute of Medical Genetics). The American College of Pathologists (CAP) proposed ACMG guidelines to classify pathogenicity information into 28 types of evidence and to determine the pathogenicity level into 5 types based on the recommendations of a joint conference. However, it is still very complicated and difficult to integrate various gene mutation information detected by NGS and determine the pathogenicity of mutations in accordance with the ACMG guidelines, and there are difficulties in research and clinical application.

[0003] Therefore, the present invention has been made to solve the above problems, and relates to a method for analyzing gene mutations based on nucleic acid sequence analysis. The gene mutation analysis method of the present invention provides an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines, and is expected to be widely used in the fields of life science and medical health care. Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention has been made to solve the above-mentioned problems in the conventional technology, and relates to a method for analyzing gene mutations based on nucleic acid sequence analysis.

[0005] In one aspect, the present invention provides a method for determining the pathogenicity of a genetic mutation. In another aspect of the invention, a device for determining the pathogenicity of a genetic mutation is provided. In yet another aspect of the invention, a method for predicting disease onset in an individual is provided. In yet another aspect of the invention, a method is provided for providing information regarding the diagnosis of the etiology of a disease in an individual.

[0006] However, the technical problems that the present invention aims to solve are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those having ordinary skill in the art from the following description. [Means for solving the problem]

[0007] Various embodiments described herein will now be described with reference to the drawings. In the following description, various specific details, such as specific configurations, compositions, and steps, are described in order to provide a thorough understanding of the present invention. However, a particular embodiment may include at least one of these specific details. In other instances, known processes and manufacturing techniques have not been described in specific detail in order to avoid unnecessarily obscuring the present invention. References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, aspect, composition, or characteristic described in connection with the embodiment is included in one or more embodiments of the present invention. Thus, the appearance of "in one embodiment" or "an embodiment" in various places throughout this specification does not necessarily refer to the same embodiment of the present invention. Additionally, particular features, aspects, compositions, or characteristics may be combined in any suitable manner in one or more embodiments.

[0008] Unless otherwise defined in the specification, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0009] Throughout the specification, when a part "comprises" a certain element, this means that it can further include other elements, not excluding other elements, unless specifically stated to the contrary.

[0010] The present invention provides an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines, which are a joint recommendation from the American Medical College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology.

[0011] In the present invention, the "ACMG Guidelines" refers to the American Medical College of Medical Genetics The ACMG guidelines are guidelines for the analysis of sequence variants recommended by a joint conference of the American College of Pathologists (ACMG), the Association for Molecular Pathology (AMP), and the College of American Pathologists (CAP). The ACMG guidelines classify pathogenicity information into a total of 28 types of evidence, and provide a classification method for determining pathogenicity levels into five types based on this.

[0012] The classification method of pathogenicity information according to the ACMG guidelines is shown in Table 1 below, and the pathogenicity determination method is shown in Table 2.

[0013] [Table 1]

[0014] [Table 2]

[0015] Regarding the ACMG guidelines including Tables 1 and 2, additional information can be found in prior art (S Richards et al., Genet Med. 2015 May; 17(5): 405-424.) and is applicable to the present invention.

[0016] The term "logic tree", "logic tree system" or "system" as used herein refers to a process that computer programming, etc. should carry out to solve a given problem. When a desired result can be obtained by mechanically processing in a certain order, the certain order is called a logic tree for the purpose. The term algorithm can also be used interchangeably.

[0017] In the present invention, the logic tree system is an algorithm that includes a method for classifying pathogenicity information based on the ACMG guidelines from gene mutation information detected by NGS and determining the pathogenicity level, or an apparatus system that realizes this.

[0018] The algorithm of the present invention collects various data useful for analyzing gene mutations from various databases known in the art, extracts necessary information, classifies the evidence in accordance with the ACMG guidelines, and determines the pathogenicity level based on this.

[0019] The algorithm of the present invention is characterized in that it does not process the information collected from the various databases known in the art, but extracts and uses only a necessary portion of the information.

[0020] The algorithm of the present invention is characterized in that it uses different databases (DBs) depending on the collected information of interest. The collected information of interest may be clinical feature information, population frequency information of single nucleotide polymorphisms (SNPs), repetitive sequence information, protein domain information, and / or in-silico prediction information, and the in-silico prediction information may be missense prediction information, splice prediction information, and / or conservation prediction information. In this case, the database for collecting each piece of information may preferably be, in the case of clinical feature information, a database based on clinical feature information provided by the National Center for Biotechnology Information (NCBI); in the case of public SNP frequency information, a database providing frequency information of SNPs of an unspecified number of 10,000 or more; in the case of repetitive sequence information, a database including interspersed repeats and low complexity DNA sequences; in the case of protein domain information, a database in which multiple sequence alignments of protein families are collected using a hidden Markov model; and in the case of in-silico prediction information, a database divided into missense prediction information, splice prediction information, and conservation prediction information.For example, the ClinVar database can be used for the clinical feature information, the GnomAD database for the population SNP frequency information, the RepeatMasker database for the repetitive sequence information, the Pfam database for the protein domain information, a database using the MetaSVM, REVEL, Eigen, Polyphen2, Provean, or VEST3 algorithm for the missense prediction information, a database using the Ada_score&Rf_score algorithm for the splice prediction information, and a database using the GERP++ algorithm for the conservation prediction information, but is not limited thereto. The identity (source) of each of the databases is shown in Tables 3 and 4 below.

[0021] [Table 3]

[0022] [Table 4]

[0023] In one embodiment of the present invention, there is provided a method for determining the pathogenicity of a gene mutation, comprising the steps of: (a) obtaining gene mutation information from the result of a base sequence analysis; (b) classifying the pathogenicity information by comparing the gene mutation information with clinical feature information, single base mutation occurrence frequency information, repetitive sequence information, protein domain information, and in-silico prediction information; and (c) determining the pathogenicity level. The base sequence analysis may be, but is not limited to, a conventional Sanger-based dideoxynucleotide base sequence analysis or a new massively parallel base sequence analysis such as next-generation base sequence analysis. The method for determining the pathogenicity of a gene mutation is provided, in which the clinical feature information, the frequency of occurrence of a single nucleotide mutation, the repetitive sequence information, the protein domain information, and the in-silico prediction information are extracted from public databases. The clinical feature information is extracted from a database based on clinical feature information provided by the National Center for Biotechnology Information (NCBI), the frequency of occurrence of a single nucleotide mutation is extracted from a database providing frequency information of an unspecified number of single nucleotide polymorphisms (SNPs), the repetitive sequence information is extracted from a database including interspersed repeats and low complexity DNA sequences, and the protein domain information is extracted from a database in which multiple sequence alignments of protein families are collected using a hidden Markov model. The present invention provides a method for determining the pathogenicity of a gene mutation, in which in-silico prediction information is extracted from an in-silico database that separately collects missense prediction information, splice prediction information, and conservation prediction information. The present invention also provides a method for determining the pathogenicity of a genetic mutation, in which the classification of the pathogenic information in step (b) is performed by classifying the pathogenic criteria into very strong, strong, moderate, or supporting, and the benign criteria into stand-alone, strong, or supporting. Alternatively, the present invention provides a method for determining the pathogenicity of a genetic mutation, in which the classification of the pathogenic information in step (b) is performed by classifying the pathogenic criteria into very strong 1 level, strong 1 to 4 levels, moderate 1 to 6 levels, or supporting 1 to 5 levels, and the benign criteria into stand-alone 1 level, strong 1 to 4 levels, and supporting 1 to 7 levels.

[0024] The present invention provides a method for determining the pathogenicity of a gene mutation, wherein the clinical feature information is applicable to one or more classifications selected from the group consisting of very strong pathogenicity criteria 1 level, strong pathogenicity criteria 1 level, normal pathogenicity criteria 1 level, normal pathogenicity criteria 5 levels, pathogenicity criteria 2 levels, strong benignity criteria 1 level, and benignity criteria 6 levels. The single base mutation occurrence frequency information is applicable to one or more classifications selected from the group consisting of normal pathogenicity criteria 2 levels, benignity criteria 1 independent level, benignity criteria 1 level, and benign criteria 2 levels. Provided is a method for determining the pathogenicity of a genetic mutation, wherein the repetitive sequence information is applicable to one or more classifications selected from the group consisting of four levels of support for pathogenicity criteria and three levels of support for benignity criteria; provided is a method for determining the pathogenicity of a genetic mutation, wherein the protein domain information is applicable to one or more classifications selected from the group consisting of three levels of support for pathogenicity criteria, four levels of support for benignity criteria, and seven levels of support for benignity criteria. This means that, it can be understood that the present invention provides a method for determining the pathogenicity of a genetic mutation, wherein the clinical feature information is applicable to PVS1, PS1, PM1, PM5, PP2, BS1, BP1, or BP6 classification; a method for determining the pathogenicity of a genetic mutation, wherein the occurrence frequency information of a single base mutation is applicable to PM2, BA1, BS1, or BS2 classification; a method for determining the pathogenicity of a genetic mutation, wherein the repetitive sequence information is applicable to PM4 or BP3 classification; a method for determining the pathogenicity of a genetic mutation, wherein the protein domain information is applicable to PM1 classification; and a method for determining the pathogenicity of a genetic mutation, wherein the in-silico prediction information is applicable to PP3, BP4, or BP7 classification, but is not limited thereto.

[0025] In the method for determining the pathogenicity of a gene mutation of the present invention, the determination of the pathogenicity level in the step (c) may be made based on the following criteria: pathogenic, likely and a method for determining the pathogenicity of a genetic variant, the method comprising determining whether the genetic variant is pathogenic, benign, likely benign, or of uncertain significance, the method comprising determining whether the genetic variant is pathogenic, benign, likely benign, or of uncertain significance, the method comprising determining whether the genetic variant is pathogenic, the method comprising determining whether the genetic variant is pathogenic, benign, likely benign, or of uncertain significance, the method comprising determining whether the genetic variant is pathogenic, the method comprising determining whether the genetic variant is pathogenic, benign, likely benign, or of uncertain significance, the method comprising determining whether the genetic variant is pathogenic, the method comprising determining whether the genetic variant is pathogenic, The present invention provides a method for determining the pathogenicity of a genetic variant, wherein the determination of the pathogenicity level is classified as shown in Table 7 of the present invention.

[0026] In another embodiment of the present invention, there is provided an apparatus for determining the pathogenicity of a genetic mutation, comprising: (a) an input unit for inputting genetic mutation information obtained from the result of nucleotide sequence analysis; (b) a calculation unit for classifying pathogenicity information by comparing the genetic mutation information with clinical feature information, single nucleotide mutation occurrence frequency information, repetitive sequence information, protein domain information, and in-silico prediction information; and (c) a determination unit for determining a pathogenicity level. The limitations of each part constituting the apparatus overlap with those described in the method for determining the pathogenicity of a genetic mutation, and will be omitted below to avoid excessive complexity of the present specification.

[0027] In yet another embodiment of the present invention, there is provided a method for predicting the onset of a disease in an individual, comprising the steps of: (a) performing a base sequence analysis on a sample isolated from a target individual; (b) determining the pathogenicity of a genetic mutation by the method of claim 1; and (c) predicting the onset of the disease in the individual according to the determined pathogenicity. The limitations of each part constituting the method for predicting the onset of a disease in an individual overlap with those described in the method for determining the pathogenicity of a genetic mutation, and will be omitted below to avoid excessive complexity of this specification.

[0028] In yet another embodiment of the present invention, there is provided a method for providing information on diagnosis of the etiology of an individual, comprising the steps of: (a) performing a base sequence analysis on a sample isolated from an individual of interest; (b) determining the pathogenicity of a genetic mutation by the method of claim 1; and (c) grasping the cause of the onset of a disease in the individual according to the result of the determination of the pathogenicity. The limitations of each part constituting the method for providing information on diagnosis of the etiology of an individual are the same as those described in the method for determining the pathogenicity of a genetic mutation, and will be omitted below to avoid excessive complexity of the present specification.

[0029] The present invention will now be described in detail with reference to examples. Effect of the Invention

[0030] The gene mutation analysis method of the present invention provides an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines, and is therefore expected to be widely used in the fields of life science and medical health care. [Brief description of the drawings]

[0031] [Figure 1] This is a result of comparing the accuracy of determining pathogenicity levels between the logic tree of the present invention and a control group logic tree according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] The present invention provides, as a best mode for carrying out the invention, a method for determining the pathogenicity of a gene mutation, comprising the steps of: (a) obtaining gene mutation information from a result of a base sequence analysis; (b) classifying pathogenicity information by comparing the gene mutation information with clinical characteristic information, information on the frequency of occurrence of single base mutations, information on repetitive sequence, information on protein domain, and in silico prediction information; and (c) determining a pathogenicity level. In the step (b), the classification of the pathogenicity information is performed by classifying the pathogenic criteria into very strong 1 stage, strong 1-4 stages, moderate 1-6 stages, and supporting 1-5 stages, and the benign criteria into stand-alone 1 stage, strong 1-4 stages, and supporting 1-7 stages, and the clinical characteristic information is classified into very strong 1 stage, strong 1 stage, moderate 1 stage, and supporting 1-5 stages. The method of determining the pathogenicity of a gene mutation is applied to any one or more classifications selected from the group consisting of: 1 stage for pathogenicity criteria, 5 stages for normal pathogenicity criteria, 2 stages for pathogenicity criteria, 1 stage for strong benign criteria, and 6 stages for benign criteria, and the determination of the pathogenicity level in step (c) is classified into pathogenic, likely pathogenic, benign, likely benign, uncertain significance, uncertain significance-pathogenic, or uncertain significance-benign. EXAMPLES

[0033] The present invention will be described in more detail with reference to the following examples. It will be obvious to those skilled in the art that these examples are merely for the purpose of illustrating the present invention in more detail, and that the scope of the present invention is not limited to these examples according to the gist of the present invention.

[0034] Throughout the specification, when a part "comprises" a certain element, this means that it can further include other elements, but not to the exclusion of other elements, unless specifically stated to the contrary.

[0035] Example 1. Development of an NGS mutation data analysis logic tree We developed an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines.

[0036] Hereinafter, the algorithm of the present invention will be referred to as "SAToK."

[0037] Generally, an algorithm changes output results infinitely depending on input values, so for the purpose of the present invention, selection of input information is very important for rapid and accurate NGS mutation data analysis. The logic tree of the present invention selects clinical feature information, population single nucleotide polymorphism (SNP) occurrence frequency information, repetitive sequence information, protein domain information, and in-silico prediction information as input information, and the in-silico prediction information selects missense prediction information, splice prediction information, and conservation prediction information. Specifically, the clinical feature information is collected from a database based on clinical feature information provided by the National Center for Biotechnology Information (NCBI), the public SNP frequency information is collected from a database providing frequency information of SNPs of an unspecified number of 10,000 or more people, the repetitive sequence information is collected from a database including interspersed repeats and low complexity DNA sequences, and the protein domain information is collected from a database that collects multiple sequence alignments of protein families using a hidden Markov model. The in-silico prediction information was collected by dividing it into missense prediction information, splice prediction information, and conservation prediction information.

[0038] The collected information was substituted into the classification method of pathogenicity information in the ACMG guidelines, and the collected information was substituted for each classification of pathogenicity information in the ACMG guidelines. This is shown in Tables 5 and 6 below. In Tables 5 and 6, "Information 1" is the classification method of pathogenicity information in the National Center for Biotechnology Information (NCBI) of the United States. "Information 2" indicates clinical feature information collected from a database based on clinical feature information provided by the National Center for Clinical Biology (NCBI), "information 2" indicates public SNP frequency information collected from a database that provides frequency information of SNPs of an unspecified number of 100 or more people, "information 3" indicates repetitive sequence information collected from a database including interspersed repeats and low complexity DNA sequences, "information 4" indicates protein domain information collected from a database in which multiple sequence alignments of protein families are collected using a hidden Markov model, and "information 5" indicates in-silico prediction information collected by classifying missense prediction information, splice prediction information, and conservation prediction information. In addition, the "O" mark indicates a case where information is assigned, the "X" mark indicates a case where information is not assigned, and "-" indicates a case where classification is not performed automatically by the logic tree of the present invention.

[0039] [Table 5]

[0040] [Table 6]

[0041] The pathogenicity determination logic tree of the present invention, derived by substituting the collected information into the classification method of pathogenicity information in the ACMG guidelines, is shown in Table 7 below.

[0042] [Table 7]

[0043] In Table 7, VUSp is "PM2 and PP3 and PM1" and is classified as VUS in the ACMG guidelines, but is classified as a mutation close to pathogenic in the logic tree (SAToK algorithm) of the present invention. VUSb is "when BP6 is present" or "when ClinVar review status ≧ 2 stars P, LP is absent and ClinVar review status of the gene ≧ 2 stars P, LP variant Max.MAF < target mutation MAF and not PP3 and MAF > 0.01% and not domain", which is classified as VUS in the ACMG guidelines, but is classified as VUS in the logic tree (SAToK algorithm) of the present invention, but is classified as a mutation close to benign.

[0044] Example 2. Validation of NGS mutation data analysis logic tree We verified whether the NGS mutation data analysis logic tree of Example 1 can be reliably applied to substantially analyze NGS mutation data and determine pathogenicity.

[0045] Specifically, a total of 3,373 non-overlapping mutations were selected from 52 patient samples (over 260 genes) tested using a gene panel for inborn errors of metabolism, and compared and analyzed using the logic tree of the present invention (SAToK algorithm) and a control logic tree (InterVar algorithm). The InterVar algorithm used as the control was invented for the purpose of facilitating the analysis of gene mutations based on nucleic acid sequence analysis, just like the present invention, and is well known in the field to which the present invention belongs (Am J Hum Genet. 2017Feb2;100(2):267-280.). The logic tree of the present invention differs from the control group logic tree in that the logic tree of the present invention compares genetic mutation information with clinical feature information, single base mutation frequency information, repetitive sequence information, protein domain information, and in-silico prediction information, whereas the control group logic tree compares clinical feature information, single base mutation frequency information, and in-silico prediction information. The logic tree of the present invention also differs from the control group logic tree in that the logic tree of the present invention extracts and applies only highly reliable information (review status = 2) from a database based on clinical feature information provided by the National Center for Biotechnology Information (NCBI), whereas the control group logic tree applies all information without verifying its reliability.

[0046] As a result of the analysis, as shown in Table 8 below, the pathogenicity level was determined in each logic tree, and only for mutations determined to be "pathogenic (P)" or "likely pathogenic (LP)" in the logic tree of the present invention and the control group logic tree, the accuracy of the logic tree of the present invention and the control group logic tree was compared with the ClinVar algorithm (Nucleic Acids Res. 2016Jan 4; 44 (D1): D862-8.). The results are shown in Figure 1 below.

[0047] [Table 8]

[0048] As shown in FIG. 1, in the logic tree of the present invention, the mutations determined to be "pathogenic (P)" or "likely pathogenic (LP)" had a first concordance rate (when the determination was completely the same) and a second concordance rate (when P or LP was recognized as the same determination) of 50% and 80%, respectively, with the results of the ClinVar algorithm, whereas the concordance rates were 20% and 20%, respectively, in the control group logic tree. This indicates that the logic tree of the present invention can be used to quickly and accurately classify pathogenicity information based on the ACMG guidelines.

[0049] Although specific parts of the present invention have been described in detail above, such specific descriptions are merely preferred embodiments for those skilled in the art. It is clear that the scope is not limited, and therefore the true scope of the invention is defined by the appended claims and their equivalents. [Industrial Applicability]

[0050] The gene mutation analysis method of the present invention provides an NGS mutation data analysis logic tree that can classify pathogenicity information and determine the pathogenicity level based on the ACMG guidelines, and is therefore expected to be widely used in the fields of life science and medical health care.

Claims

1. (a) obtaining gene mutation information from the results of base sequence analysis; (b) classifying pathogenicity information by comparing the gene mutation information with clinical feature information, single base mutation frequency information, repetitive sequence information, protein domain information, and in silico prediction information; (c) determining the level of pathogenicity.

2. The method of claim 1, wherein the clinical feature information, the frequency of occurrence of single base mutations, the repetitive sequence information, the protein domain information, and the in silico prediction information are extracted from public databases.

3. The classification of the pathogenicity information in the step (b) includes: The pathogenic criteria are classified into very strong, strong, moderate, and supporting.

2. The method of claim 1, which divides benign criteria into stand-alone, strong, and supporting.

4. The classification of the pathogenicity information in the step (b) includes: The pathogenic criteria are classified into very strong 1 stage, strong 1-4 stage, moderate 1-6 stage, and supporting 1-5 stage. The method according to claim 3, wherein the benign criteria are classified into stand-alone 1 stage, strong 1 to 4 stages, and supporting 1 to 7 stages.

5. The method of claim 4, wherein the clinical characteristic information is applied to one or more classifications selected from the group consisting of very strong pathogenic criteria level 1, strong pathogenic criteria level 1, average pathogenic criteria level 1, average pathogenic criteria level 5, support for pathogenic criteria level 2, strong benign criteria level 1, and support for benign criteria level 6.

6. The method according to claim 4, wherein the occurrence frequency information of the single base mutation is applied to one or more classifications selected from the group consisting of two normal stages of pathogenic criteria, one independent stage of benign criteria, one supporting stage of benign criteria, and two supporting stages of benign criteria.

7. The method of claim 4, wherein the repetitive sequence information is applicable to any one or more classifications selected from the group consisting of four common stages of pathogenic criteria and three supporting stages of benign criteria.

8. The method of claim 4, wherein the protein domain information is applied to a normal one-stage classification of pathogenicity criteria.

9. 5. The method of claim 4, wherein the in silico predictive information is applied to any one or more classifications selected from the group consisting of three levels of support for pathogenic criteria, four levels of support for benign criteria, and seven levels of support for benign criteria.

10. The determination of the pathogenicity level in the step (c) may be classified into pathogenic, likely pathogenic, benign, and semi-benign.

2. The method of claim 1, wherein the classification is into likely significance, likely benefit, and uncertain significance.

11. 11. The method of claim 10, wherein the determination of likely benign pathogenicity is classified as uncertain significance, uncertain significance-pathogenic, and uncertain significance-benign.

12. (a) an input unit for inputting gene mutation information obtained as a result of a base sequence analysis; (b) a calculation unit for classifying pathogenicity information by comparing the gene mutation information with clinical feature information, single base mutation occurrence frequency information, repetitive sequence information, protein domain information, and in silico prediction information; (c) a determination unit for determining the pathogenicity level of a genetic mutation.

13. The apparatus of claim 12, wherein the clinical feature information, the frequency of occurrence of single base mutations, the repetitive sequence information, the protein domain information, and the in silico prediction information are extracted from public databases.

14. (a) performing sequence analysis on a sample isolated from an individual of interest; (b) determining the pathogenicity of the genetic variant by the method of claim 1; (c) predicting the onset of disease in said individual depending on the result of the pathogenicity determination.

15. (a) performing sequence analysis on a sample isolated from an individual of interest; (b) determining the pathogenicity of the genetic variant by the method of claim 1; (c) determining the cause of the onset of disease in said individual depending on the result of the pathogenicity determination.

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