KR101693504B1 - Discovery system for disease cause by genetic variants using individual whole genome sequencing data - Google Patents

Abstract

The present invention relates to a system for discovering the cause of a disease by analyzing genetic information from a personal genome by comparing individual whole genome information with a plurality of whole genome DB constructed by a genome project. The present invention includes an analysis data input part for inputting analysis data including personal genome information; a search control part for calculating an analysis result including at least one of genotypes, genotypes, rare mutations, disease and biologically active mutations in each gene by comparing the genetic information stored in the database with the genome information to be analyzed, and generating a result report through the analysis result; and a storage part for storing the gene information of a control group for comparison with the analysis data. According to the present invention, it is possible to provide a genetic analysis platform in which the genotype discrimination of the individual genome and the detection efficiency of significant mutation are improved by effectively comparing the gene mutation information stored in a control database and the individual genome as a subject to be analyzed.

Description

[0001] The present invention relates to a genome-wide genome sequencing system,

The present invention relates to a system for analyzing and providing genetic information from a personal genome by comparing the input full-length genome information with a multiple-length genome DB constructed by a genome project.

Currently, the trend of the IT market is changing in the order of Google cloud computing Ubiquitous. At the same time, biomedical, bioinformation and genome areas are changing to new trends in order of bio-google system bio personalized medicine. In particular, the post-human genome project has been actively pursuing personalized medicine with the rapid development of next-generation sequencing technology.

It is known that the next generation technology takes approximately two weeks to sequence the genome of a human (x30). Currently, more than 20,000 sequencers are reported worldwide, and about 500 billion won has recently been invested in the major development companies of the third-generation sequencer (Ion Torrent: 2.5 generation, third generation of Pacific BioScience) .

In addition, more than KRW 10 trillion has been invested in the relevant field worldwide and development is under way. With this trend, it is expected that the total sequencing cost of one person will be reduced to about $ 1,000 in the next two to three years. The most useful and readily available technologies based on the above next generation technologies are expected to be clinical genomics, pharmaco-genomics and translational medicine.

However, in order to provide commercialized customized medical services through analysis of individual genes, the following requirements must be improved.

First, there is a problem that the analysis speed between the control group and the individual genome should be improved. In order to increase the accuracy of the analysis, it is required to increase the amount of the control group. In order to solve this problem, it is required to provide an analysis system with improved analysis speed and an analysis platform with a database schema that can improve the analysis efficiency.

Second, an efficient method of detecting the variation marker between the control and the individual genome and an effective method of classifying the detected marker according to the purpose of analysis and deriving the analysis result are required.

In other words, since the human whole-genome genome contains an astronomical nucleotide sequence and a large amount of mutation markers are also detected in general, in order to provide a customized medical service commercialized using the genome, It is necessary to derive the analysis results by the classification system and the verification system.

Third, in order to improve the quality of customized medical services, a reporting module is required to provide analysis results in a visualized form so that the user can visually recognize the analyzed results easily.

In other words, even if the correctness is secured and the mutation marker of the individual genome is detected quickly, if the user provides a result of simply listing a large amount of mutation markers, the user can not grasp the meaning and the degree of mutation of the mutation marker, It is necessary to provide a reporting module that can facilitate the operation of the system.

Finally, in order to provide more accurate and diverse analysis results, information such as mutation characteristics and mechanism of action for more mutations should be collected and provided.

Research on gene mutation is continuously being actively researched by universities and research institutes in each country. In addition to the fact that a large amount of information has been verified, research results have been continuously expanded.

For example, about 138 disease information related to genetic mutation has been confirmed in the Bioethics and Safety Act, and phenotypic information about about 1,700 genes is contained in overseas databases such as PheWAS-GWAS and eMEREG , Drug Bank and Metabolic Bank contain information on the resistance and susceptibility of proteins, drugs and metabolites expressed by over 6,000 and 12,000 genes, respectively.

Such gene mutation-related information is increasing every year, and the present invention will continuously expand the analysis accuracy and area by expanding the verification data.

On the other hand, Applicants have been developing continuous techniques to improve the technical requirements in the field of gene analysis mentioned.

As a result of these efforts, we have developed a GPU-based analysis system (patent registration: 10-0996443) to build an analysis system to improve the genome analysis speed, and it is a technique to improve the data comparison speed. Based on the RVR file Developed the information retrieval method (patent registration: 10-0880531, patent registration: 10-1035959, patent registration: 10-1117603) and developed the ADISCAN method to efficiently judge the degree of variation between the control group and the individual genome Patent registration: 10-1400717, patent registration: 10-1460520, patent application: 10-2014-0020738, patent application: 10-2014-0020736).

(001) Korean Patent No. 10-0996443 (002) Korean Patent No. 10-1035959 (003) Korean Patent No. 10-1117603 (004) Korean Patent No. 10-1400717 (005) Korean Patent No. 10-1460520 (006) Korean Patent Publication No. 10-20120053623 (007) Korean Patent Publication No. 10-20150024232 (008) Korean Patent Publication No. 10-20150024231

Disclosure of the Invention The present invention has been made in order to improve the requirements for realizing the above-mentioned commercial personalized medicine according to the present invention. The present invention provides a genetic analysis platform to which a database schema can be applied, .

In addition, the present invention provides a gene analysis platform including a reporting module that enables detected mutation information to be easily recognized by a user.

이고, 변수 β는 임상정보 DB에 저장된 피검사 대상자의 연령, 성별 또는 체질량(BMI)을 포함하는 건강기록정보(PHR, personal health records)에 따른 매개변수이고; 변수 χ는 상기 검색제어부가 산출한 분석데이터에 포함된 질병관련 유전형에 따른 매개변수이다.
이때, 상기 저장부는, 상기 분석데이터와 대비하기 위하여 대조군 유전자의 유전형 정보를 저장한 하플로스캔(HaploScan) DB를 포함하여 구성되고; 상기 검색제어부는, 상기 검색제어부는, 상기 분석데이터를 상기 하플로스캔(HaploScan) DB와 대비하여, 상기 분석데이터의 유전형을 판별하는 하플로스캔(HaploScan) 엔진을 포함하여 구성될 수도 있다.
그리고 상기 하플로스캔(HaploScan) DB는, 단일유전자에 대한 유전형 정보를 저장하는 단일유전자정보데이터베이스와; 표현형별 다중 유전자의 유전형 정보를 저장하는 다중유전자정보 데이터베이스를 포함하여 구성될 수도 있다.
또한, 상기 단일유전자정보데이터베이스는, 대조군의 단일 유전자에 대하여, 인종별 반수체 및 형질 빈도를 점유 비율별로 구분(군집)하여 저장한 단일유전자 하플로(Haplo) 맵과; 상기 단일 유전자 하플로(Haplo) 맵에 저장된 단일 유전자의 유전형을 구분하는 변이에 대한 변이정보를 저장하는 단일유전자 하플로 프리컨시 정보를 포함하여 구성될 수도 있다.
그리고 상기 다중유전자정보 데이터베이스는, 표현형별 대조군의 다중 유전자에 대하여 유전형 연관 염기의 변이분포를 인종별로 구분(군집)하여 점유비율에 따라 저장한 다중유전자 하플로(Haplo) 맵과; 상기 다중유전자 하플로(Haplo) 맵에 저장된 상기 표현형에 대한 유전형을 구분하는 변이에 대한 변이정보를 저장하는 다중유전자 하플로 프리컨시 정보를 포함하여 구성될 수도 있다.
또한, 상기 검색제어부는, 검출된 변이 유전자 특성을 전장 유전자에 대하여, 유전형에 따라 분류하여 누적된 값을 점(point)으로 가시화한 맨하탄 플롯(Manhattan plot) 상에 표시한 결과 리포트를 생성할 수도 있다.
그리고 상기 맨하탄 플롯은, 변이 유전자의 유의성 여부를 가이드하는 설정값(cut-off)이 표시될 수도 있다.According to an aspect of the present invention, there is provided an apparatus for analyzing data, comprising: an analysis data input unit for inputting analysis data including personal genome information; A search control unit for calculating analysis results of rare mutations and disease variations of each gene by comparing the gene information stored in the database with the genome information to be analyzed and generating a result report through the analysis result; And a storage unit for storing the genetic information of the control group for comparison with the analysis data. The storage unit includes an ADISCAN DB in which the whole genome information of the control group is divided and stored according to the classification criteria including race, Wherein the search control unit comprises an ADISCAN engine that compares each base included in the analysis data with the ADISCAN DB and calculates a rarity with respect to the group of the control group; And IDA DB for storing known gene mutation information in relation to the gene sequence; Wherein the search control unit comprises an IDA search engine for comparing the analysis data with the IDA DB to detect a known gene-related disease variation included in the analysis data, wherein the storage unit stores the clinical information- And a clinical information DB in which environmental swine information of a subject to be examined to be taken into consideration together with a genetic characteristic to derive a result is stored. The search control unit is configured to perform a logistic regression analysis The disease causality relation (Πx) is calculated through an arithmetic expression to derive a disease cause prediction result:

, And the variable? Is a parameter according to the personal health records (PHR) including the age, sex or body mass (BMI) of the subject to be examined stored in the clinical information DB; The variable χ is a parameter according to the disease-related genotype included in the analysis data calculated by the search control unit.
At this time, the storage unit includes a HaploScan DB storing genotype information of a control gene to compare with the analysis data; The search control unit may include a HaploScan engine for comparing the analysis data with the HaploScan DB to determine the genotype of the analysis data.
The HaploScan DB includes: a single gene information database storing genotype information for a single gene; And a multiple gene information database for storing genotype information of multiple genes by phenotype.
In addition, the single gene information database includes a single gene Haplo map which is obtained by classifying (clustering) the race haplotypes and trait frequencies according to occupancy ratios of a single gene in a control group; And single gene hybrid free region information for storing mutation information for a mutation for discriminating a genotype of a single gene stored in the single gene Haplo map.
The multiple gene information database includes a multiple gene Haplo map which is obtained by dividing the variation distribution of the genotypically related bases into multiple genes of the control group according to the phenotype, and storing them according to the occupancy rate; And multigenic gene heterofluorescence information for storing the mutation information of the mutation for discriminating the genotype of the phenotype stored in the Haplo map of the multiple genes.
In addition, the search control unit may classify the detected mutated gene characteristics according to genotypes of the whole gene, generate a report on a Manhattan plot showing the cumulative value as a point visualized have.
In the manhattan plot, a cut-off may be displayed to guide the significance of the mutation gene.

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In the system for locating disease causation using the genetic variation information of the individual full-length genome according to the present invention as described above, it is possible to effectively compare the genetic variation information stored in the control database with the individual genome to be analyzed, It is possible to provide a gene analysis platform with improved efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an exemplary diagram showing a configuration of a gene analysis service to which the present invention is applied; FIG.
FIG. 2 is a block diagram showing a main configuration of a disease-causing excavation system according to a specific embodiment of the present invention. FIG.
3 is an exemplary diagram showing a configuration of a main database constituting a system for locating a disease cause according to the present invention;
FIG. 4 is an exemplary diagram showing a configuration example of a HaploScan DB constituting a specific embodiment of the present invention; FIG.
5 is an exemplary diagram showing a configuration example of an ADISCAN DB constituting a specific embodiment of the present invention.
6 is an exemplary diagram showing a configuration example of an IDA DB according to a specific embodiment of the present invention;
FIG. 7 is an exemplary diagram showing a configuration example of a BAV DB according to a specific embodiment of the present invention; FIG.
FIG. 8 is a flowchart illustrating a genetic information analysis method according to a specific embodiment of the present invention. FIG.
FIG. 9 is an exemplary diagram showing an example of a DNA sequencing data generating method according to a specific embodiment of the present invention. FIG.
10 is an exemplary diagram showing an example of a result report generated by a specific embodiment of the present invention;
11 is an exemplary diagram showing another example of a result report generated by a specific embodiment of the present invention.
FIG. 12 is an exemplary diagram showing an example of verification of a variation of a physiological activity according to a specific embodiment of the present invention. FIG.
FIG. 13 is an exemplary diagram showing a clinical information-based disease cause prediction calculation example according to a specific embodiment of the present invention; FIG.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

First, the structure of the gene analysis service to which the disease-causing system according to the present invention is applied will be briefly described.

As shown in FIG. 1, the gene analysis service collects samples of blood and the like from a personal gene collection agency such as a hospital, and requests the DNA diagnosis company to diagnose the sample.

The DNA diagnostic company produces a DNA custom chip from collected samples or performs DNA sequencing (NGS). Of course, since DNA sequencing can be generated by various methods according to recent technological developments, the method for generating DNA sequencing can be performed by various methods according to the technology level of a DNA diagnostic company.

The genetic information included in the individual genome is analyzed through the DNA sequencing system as described above, and the analyzed information is transmitted to a diagnostic institution or a consumer such as a hospital.

Of course, when the DNA dummy data is provided from the DNA diagnostic company, the system for detecting the cause of the disease according to the present invention forms a highly integrated index file from the DNA dummy data to analyze the genome sequence of the big data.

This will be described below with reference to FIG.

That is, the present invention relates to a disease-causing system for analyzing genetic information contained in a personal genome from DNA sequencing information, and a system for searching for a disease-causing agent according to the present invention will be described in detail below.

FIG. 2 is a block diagram showing a main configuration of a disease cause search system according to a specific embodiment of the present invention, FIG. 3 is an exemplary view showing a configuration of a main database constituting a disease cause search system according to the present invention, FIG. 4 is an exemplary view showing a configuration example of a HaploScan DB constituting a specific embodiment of the present invention, FIG. 5 is an exemplary view showing a configuration example of an ADISCAN DB constituting a specific embodiment of the present invention, and FIG. FIG. 7 is a diagram illustrating an example of a structure of an active variation DB according to a specific embodiment of the present invention. Referring to FIG.

2, the disease detection system according to the present invention includes an analysis data input unit 100, a search control unit 200, a result report providing unit 300, a HaploScan DB 400, an ADISCAN DB 500, An IDA DB 600, a physiological activity DB 700, and a reference DB 800.

The analysis data input unit 100 receives the DNA sequencing data.

The search control unit 200 detects a genotype, a genotype, a rare variation, a disease variation, and a bioactivity variation for each genotype, a phenotype for each gene from the inputted DNA sequencing. For this purpose, the search control unit 200 searches the HaploScan engine 210, an ADISCAN engine 220, an IDA search engine 230, and a biologically active variation search engine 240.

The HaploScan engine 210 determines the genotype by comparing the analysis data (input DNA sequencing) with the Haplo MAPs 414 and 424 stored in the HaploScan DB 400 described later.

The structure of the HaploScan DB 400 and the search method of the HaploScan engine 210 will be described in detail later.

The ADISCAN engine 220 compares ADISCAN DB 500 with each base included in the input analysis data by ADISCAN method, and calculates the rarity with respect to the group of the control.

In addition, the IDA search engine 230 detects an already known gene-related disease variation, and detects disease variation by comparing analysis data with an IDA DB 600 storing a known disease variation.

The biomarker mutation search engine 240 detects a protein-metabolism-related genetic mutation and discriminates genetic mutation of amino acids involved in protein-drug, protein-DNA, and protein-protein binding.

At this time, the physiological activity variation search engine 240 compares analysis data with the BAV DB 700 to determine whether or not the bases corresponding to amino acids related to protein binding stored in the BAV DB 700 of the analysis data are mutations .

Meanwhile, the search control unit 200 manages the genotypes determined by the HaploScan engine 210 and the ADISCAN engine 220, and manhattan (or the like) so that the diagnosis (or the user) can easily grasp the significance (rarity) Plot and Radial Variability Create a results report using a significance chart.

The generated result report is provided to the user through the result report providing unit 300.

Hereinafter, a database structure of a disease cause discovery system according to the present invention will be described.

The system for locating a disease cause according to the present invention includes HaploScan DB 400, ADISCAN DB 500, IDA DB 600, BAV DB 700, and Reference DB 800.

As shown in FIG. 3, the HaploScan DB 400 is a DB that summarizes a genotype of a control gene to calculate a genotype from individual genome information to be analyzed. The HaploScan DB 400 includes a genome- A single gene information database 410, and a multiple gene information database 420.

The single gene information database 410 is a database storing genotypes for a single gene. The single gene information database 410 includes a single gene Haplo map 414 and a single gene hybrid free region information 412.

Meanwhile, as shown in FIG. 4, the single gene Haplo map 414 is obtained by classifying (clustering) the mutation distribution by the occupancy ratio of the same gene of the entire control group, and 26 genes The calculation of race haplotypes and frequency of specific traits and the frequency of each sub-race are summarized.

The single gene hybrid free region information 412 stores information on each of the mutations. In this case, the single gene heterofunction information 412 may be data directly storing the variation information, or may be composed of an identification factor indicating the location of the information stored in the reperence DB 800, which will be described later. That is, the single gene haplophytic information 412 provides frequency and various disease-related annotation information in each gene in 39,000 human genes and 5,000 global races.

The multi-gene information database 420 includes a multi-gene Haplo map 424 and multi-gene hybrid free context information 422, do.

In this case, the multi-gene Haplo map 424 is a phenotypic phenotype of the genetic characteristics in which phenotypes are identified by multiple genes, and the distribution of mutations to the related bases of all the control groups is clustered according to occupancy ratios for each phenotype. And the frequencies of specific traits and the frequency of each sub-race were calculated and summarized for the calculation of haplotypes of 26 races in the world.

The multi-gene hybrid free status information 422 stores information on each of the mutations. At this time, the multi-gene heterofluorescence information 422 may be data directly storing the mutation information, or may be composed of an identification factor indicating the location of the information stored in the reperence DB 800, which will be described later.

That is, the multi-gene haplophytic information 422 provides frequency of phenotype-related gene sets and various disease-related annotation information in 39,000 human genes and 5,000 world races.

Referring to the example shown in FIG. 4, the X-axis of the HaploScan DB 400 is 3 billion nucleotides and the nucleotide sequence has 39,000 genes. If the mutation is found in the specific gene (i) in its schema, the mutation can be clustered using both the haplotype and the genotype in the Y axis: 5,000, and the clustered form becomes the HaploMap.

The first GP * 47 * 0 represents 47% of the global population, 0 bits different from the global average (same), and the second group The genotype GP * 25 * 1 means 25% of the world population, which means that it is 1 bit different from the global average.

In addition, multiple genetic-based HaploMaps are categorized and classified by the same method.

The ADISCAN DB 500 is a DB storing genome information of a control group. Specifically, the genome information known by the execution of a global genome project can be utilized for the collective genome.

As shown in FIGS. 3 and 5, the ADISCAN DB 500 stores the full-length genome information of the control group, and may be stored in accordance with a classification criterion forming genetic groups such as race.

At this time, the racial discrimination may be classified into five major categories or 26 sub-categories, in order to discriminate whether or not a mutant gene is detected by reflecting the genetic characteristics of each race.

As shown in FIGS. 3 and 6, the IDA DB 600 stores genetic mutation information and mutation information related to each disease in various diseases, The document information is stored and stored.

In addition, the BAV DB 700 stores gene information for determining the amino acid form of the binding position of various proteins.

Specifically, in the binding between protein-drug, protein-DNA and protein-protein, the amino acid affecting these bonds and the gene information affecting the corresponding amino acid are stored.

Accordingly, when a large number of mutations are generated in the bases for the amino acid that govern the binding of a specific metabolite, the subject of the analysis data has a high possibility that normal metabolism is difficult to be treated in the body.

That is, as shown in FIG. 7, the BAV DB 700 stores variations predicted for the protein binding activity, the promoter position, and the protein activity of the binding state, including known disease mutations.

The BAV DB 700 is a database for storing physiological activity-related gene information, and stores information on resistance and susceptibility to genes, drugs, metabolites, and foods. At this time, the BAV DB 700 may also be constructed by linking known data securing the public confidence. For example, the BAV DB 700 may include 6,000 pieces of drug information (interactive protein and binding region information, etc.) Information on the metabolism-related mutations of over 200 genes in DMET (metabolizing enzyme and transporter gene) and 12,000 metabolite information (interacting protein and binding domain information) known in the metabolism bank can be used. have.

Meanwhile, the reference DB 800 is a DB that stores information on a variation of a known dielectric, and can be constructed in association with a published information database as well as document information.

For example, PheWAS-GWAS (Genome wide association study) data and eMERGE (Electronic Medical Records and Genomics) data can be applied to the reference DB.

Meanwhile, although not shown, the search control unit 200 may include a clinical information DB storing environmental postmark information of a subject to be examined, which should be considered together with a genetic characteristic, in order to derive a clinical information- .

At this time, the clinical information DB stores personal environmental result data, population average, and reference information.

The individual environmental resultant data may be clinical information data such as individual comprehensive examination data, and the group average and reference information may utilize the results of the community cohort study provided by the CDC.

Hereinafter, a method for analyzing genetic information using a personal bio-based dielectric according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 8 is a flowchart illustrating a method for analyzing genetic information according to a specific embodiment of the present invention. FIG. 9 is a view illustrating an example of DNA sequencing data generating method according to a specific embodiment of the present invention. Fig. 11 is an exemplary view showing another example of a result report generated by a specific embodiment of the present invention, Fig. 12 is a diagram showing an example of a result report generated by the present invention FIG. 13 is an exemplary diagram illustrating an example of predicting and calculating disease cause based on clinical information according to a specific embodiment of the present invention. FIG.

First, as shown in FIG. 8, a method for analyzing genetic information using a personal full-length dielectric according to the present invention starts with receiving analysis data (DNA Sequencing) to be analyzed by an analysis data input unit (S100) .

At this time, the analysis data may be provided in a Dumy form composed of DNA fragments. In this case, as shown in FIG. 9, the present invention generates DNA sequencing in the form of an RVR file through highly integrated indexing on the provided Dumy data, do.

Hereinafter, the method for analyzing genetic information using the personal full-length genome according to the present invention performs four types of analysis according to the analysis target.

That is, the analysis of the genetic information using the private full-length genome according to the present invention can be carried out as follows: 1) genotype discrimination S200, 2) calculation of rare mutation S300, 3) calculation of disease mutation S400, In the following, a detailed description will be given of each of them.

[Genotype discrimination]

The HaploScan engine 210 detects the cluster and the information about the genotype of the single gene and the phenotype in comparison with the Haplo frequency 412 and the MAP 414 stored in the HaploScan DB 400.

Specifically, the HaploScan engine 210 compares the i-th gene of the DNA sequencing with the i-th gene information of the single gene Haplo Frequency 412 (S211). If the i-th gene of the individual genome to be analyzed is a single gene Haplo MAP 414 (S213, S215). In this case, it is determined whether the single gene classification is included in the single gene classification classified in the MAP 414 (S213, S215).

Thereafter, the HaploScan engine 210 repeats i = 1 to the end (about i = 39,000) to determine the genotype of the entire gene of the analysis data (S217, S219).

In addition, the HaploScan engine 210 compares the DNA sequencing with the multigene Haplo frequency 422 (S221). Then, the HaploScan engine 210 classifies the combination of multiple genes of the analyzed genome for each phenotype into the multiple gene Haplo MAP 424 (S223, S225). In the case of the multi-gene combination,

Again, the HaploScan engine 210 determines the genotype of the analysis data by repeating all the phenotypes stored in the multiple gene information database 420 (S227, S229).

Through this HaploScaning process, we can define single genetic mutations and genotypes associated with multiple gene mutations included in the genome to be analyzed.

[Rare variation calculation]

A rare mutation is a base mutation caused by a specific genetic mutation that is extremely unusual, and is often related to a rare disease. It is possible to detect the presence or absence of a mutation or a difference in a specific base to determine the possibility of developing a rare disease.

To this end, as shown in FIG. 8, the ADISCAN engine 220 selects a control group (S310).

Here, the control group is a control group for judging the rarity of the mutation, and may be limited to a specific race or to a specific country.

Thereafter, the ADISCAN engine 200 calculates a variation index using a base of the control DB and the ADISCAN method for a specific locus base, and performs such a process on the entire genome (n = 1 to n = about 3 billion) (S320, S330, S340).

Thus, the rarity of the bases with respect to the entire base sequence is calculated (S350).

The allelic depth and imbalance scanning (ADISCAN) for calculating the rare mutation is a technique of screening markers giving differences between normal and abnormal genes. Geometric allele difference, statistical allele difference or allelic imbalance ratio.

[Calculation of disease variation]

The IDA search engine 230 compares the analysis data with the ID information of the IDA DB 600 to determine the risk of the disease (S410).

In this way, the analysis data is reviewed for all diseases included in the IDA DB (S420), and the significant mutation-related diseases are calculated (S430).

[Calculation of physiological activity variation]

The physiological activity mutation search engine 240 searches the BAV DB (physiological activity mutation DB) (S510) and detects information on amino acids involved in protein binding (S520).

Herein, the protein binding includes binding of a protein-drug, a protein-DNA, and a protein-protein, and the amino acid information includes information on a base related to the amino acid.

Then, the physiological activity variation search engine 240 detects the amino acid in which the mutation has occurred on the analysis data and metabolite information related thereto, in comparison with the base included in the amino acid information (S530 and S540).

The physiological activity variation search engine 240 repeatedly performs mutation detection for all amino acids, and integrates the detected information to calculate physiological activity variation information (S550, S560).

FIG. 10 shows an example in which the influence of the amino acid variation of the detected protein on metabolism is verified by simulation.

Then, the search control unit 200 integrates the discriminated or calculated genotypes, rare mutations, disease variances, and physiological activity variations to generate a result report to be provided to the user (S600).

At this time, the search control unit 200 may calculate the cause of disease based on clinical information based on the clinical information of the examinee.

Specifically, in order to predict the cause of the disease, personal health records (PHRs) are needed that include the current status of environmental factors (comprehensive screening data and clinical information). In particular, the average and baseline information of the group is needed in the environmental factor (in the present invention, the reference information is utilized in the second community cohort study provided by the CDC). Here, the result of these environmental factors and linkage with the genotype is called PHR-trait.

As shown in FIG. 11, the disease causality diagram (Π) detection formula is based on a logistic regression analysis method. The variable χ is determined according to the genotype, rare mutation, disease variance, and biologic activity variation calculated as described above And the variable? Is a value determined from the PHR.

That is, the cause of disease is the genotype (group or cluster of genotypes) of Gene, Disease or Drug. PHR (BMI, AGE, SEX, etc.) can be calculated.

Therefore, the cause of the entire gene-based disease is calculated by calculating the relationship between the current clinical condition (normal, disease, or phenotype) and the Gene, Disease, and Drug genotypes calculated from 39,000 genes.

On the other hand, the system for locating disease causes according to the present invention generates the reporting data from the calculated gene mutation information.

 The resulting report, which is calculated at this time, basically uses the subharmonic plot and the radial variation chart for the visualization of the mutation gene although there are some differences depending on the products.

12 is an exemplary view showing an example of a Manhattan plot generated by a specific embodiment of the present invention.

As shown in FIG. 12, the Manhattan plot shows the standard genes of the genome project based on the non-sym variations of all known SNPs for 39,000 genes according to their genotypes, point of the graph.

By displaying the gene of the genome to be analyzed, the mutation specificity of the gene to be analyzed can be easily recognized compared with the control group.

This Manhattan plot not only makes it easier to identify the mutation locus, it also makes it easier to determine the degree of variation.

On the other hand, the significance variations displayed by the Manhattan plot can be represented by a radial variation chart, as shown in FIG. 13, depending on the degree of mutation and genetic characteristics.

At this time, the degree of mutation of the genome to be analyzed and the average of the control group are displayed together, so that the degree of mutation of the subject genome can be visibly and clearly displayed, and genetic characteristic information is additionally included to generate a result report It is possible.

The result report generated in the manner as described above is provided through the result report providing unit.

It is to be understood that the invention is not limited to the disclosed embodiment, but is capable of many modifications and variations within the scope of the appended claims. It is self-evident.

The present invention relates to a system for analyzing and providing genetic information from a personal genome by comparing the input full-length genome information with a multiple-length genome DB constructed by a genome project. According to the present invention, It is possible to provide an analysis platform.

Claims (12)

An analysis data input unit for inputting analysis data including personal genome information;
A search control unit for calculating an analysis result of rare mutation and disease variation of each gene by comparing the gene information stored in the database with the individual genome information of the subject of analysis and generating a result report through the analysis result; And
And a storage unit for storing the gene information of the control group for comparison with the analysis data,
Wherein,
The ADISCAN DB, which is stored as a separate classification according to the classification including race,
The search control unit,
And an ADISCAN engine that compares each base contained in the analysis data with the ADISCAN DB to calculate the rarity with respect to the group of the control:
Wherein,
An IDA DB for storing known gene mutation information related to each disease for each of a plurality of diseases;
The search control unit,
And an IDA search engine for comparing the analysis data with the IDA DB to detect a known gene-related disease variation included in the analysis data,
Wherein,
In order to derive the prediction result of the disease cause based on the clinical information, the clinical information DB storing the environmental test information of the testee to be considered together with the genetic characteristic is further comprised:
The search control unit,
Using the arithmetic formula obtained by logistic regression, we derive the disease causation relation (Πx) and derive the disease cause prediction result:
The disease causative relationship arithmetic expression,

ego,
The variable? Is a parameter according to the personal health records (PHR) including the age, sex or body mass (BMI) of the subject to be examined stored in the clinical information DB;
And the variable χ is a parameter according to the disease-related genotype included in the analysis data calculated by the search control unit.
The method according to claim 1,
Wherein,
And a HaploScan DB storing the genotype information of the control gene to compare with the analysis data;
The search control unit,
The search control unit,
And a HaploScan engine for comparing the analysis data with the HaploScan DB to identify the genotype of the analysis data. Excavation system.
3. The method of claim 2,
In the HaploScan DB,
A single gene information database for storing genomic information on a single gene;
And a genetic information database for storing the genotypic information of the multiple genotypes of the phenotypes.
The method of claim 3,
The single gene information database comprises:
A single gene Haplo map for a single gene in the control group, which is classified by the occupation ratio (population) of haplotypes and trait frequencies by race;
Wherein the genetic mutation information comprises a single gene haplotype free consensus information for storing mutation information of a mutation for discriminating a genotype of a single gene stored in the single gene Haplo map. Disease Causing System Used.
The method of claim 3,
Wherein the multiple gene information database comprises:
A multi-gene Haplo map which is obtained by dividing the variation distribution of the genotypically related bases into multiple genes of the control group by phenotype and clustering them according to occupancy rate;
Wherein the genetic variation information comprises mutation gene heterofluorescence information for storing mutation information for a mutation for discriminating a genotype of the phenotype stored in the multiple gene Haplo map. A system to identify disease causes using.
delete delete delete delete delete 6. The method according to any one of claims 1 to 5,
The search control unit,
A mutant full-length genome is generated by classifying the detected mutant gene characteristics according to genotypes of a full-length gene, and displaying a cumulative value on a Manhattan plot visualized as a point. Detection system of disease cause using genetic mutation information.
12. The method of claim 11,
The Manhattan plot,
Wherein the genetic mutation information is indicative of a cut-off for guiding the significance of the mutation gene.

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