JP2016077227A - Genomic-analysis apparatus, genomic-analysis method, and genomic-analysis program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique which enables identification of the responsible mutation related to a particular hereditary phenotype with high accuracy.SOLUTION: A genomic-analysis apparatus related to the one aspect of the invention comprises: a candidate mutation evaluating part which calculates evaluation values for evaluating specificity of each candidate mutation by obtaining each genome base sequence of a target organism with a particular hereditary phenotype and a plurality of control organisms without the hereditary phenotype, calculating the different degree of the mutation part between the target organism and each control organism, and the different degrees of the mutation parts among a plurality of control organisms, for every partial genome base sequence comprising the candidate mutation which serves as a candidate of the responsible mutation of the hereditary phenotype, and examining whether the difference between both calculated different degrees is significant; and an output control part which outputs the result of the examination in the condition that the order of each candidate mutation can be identified based on the evaluation values.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a genome analysis device, a genome analysis method, and a genome analysis program.

  The realization of personalized medicine using human genome information and the application of medical research related to the pursuit of the cause of the disease are priority issues in the big data era that extends to the medical field. Conventionally, as a method for identifying a cause of a disease, for example, in Non-Patent Document 1, a linkage analysis method for identifying a cause of a disease by specifying a mutation site that commonly occurs in a family sharing the same genetic disease Has been proposed.

  Further, for example, in Non-Patent Document 2, known information such as documents showing gene mutations causing genetic diseases (hereinafter also referred to as “responsible mutations”) are collected in a mutation database and generated in the target organism. A method for evaluating each candidate mutation and identifying the cause of the disease by comparing and comparing a plurality of candidate mutations that are candidates for the disease with the mutation database has been proposed.

G. M. Lathrop, J. M. Lalouel, C. Julier, J. Ott, "Strategies for multilocus linkage analysis in humans", Proc. Natl. Acad. Sci. USA, 1984, p.3443-3446 Mark Yandell, Chad Huff, Hao Hu, et al. "A probabilistic disease-gene finder for personal genomes", Genome Res. 2011; 21, p.1529-1542

  In recent years, with the advent of genome information analysis technology using next-generation sequencers, it has become possible to identify a large amount of mutation sites in genome base sequences. However, it is still very difficult to identify the responsible mutation responsible for the disease.

  For example, with the linkage analysis technique as described in Non-Patent Document 1, it is difficult to identify the cause of a disease unless genome information of several families over several generations can be acquired. Therefore, in today's society where nuclear families, declining birthrates, and late marriage are progressing, it is very difficult to acquire genome information for several generations, and the situations where this linkage analysis method can be used are very limited. Expected to be. Further, in the method based on comparative reference of known mutation database information as in Non-Patent Document 2, it is difficult to cope with an unknown cause of disease, so it is difficult to identify a new responsible mutation of a genetic disease.

  In other words, attempts have been made to identify the cause of the disease from the information obtained by genome analysis. However, conventional methods for identifying the cause of the disease have the limitations described above, and it will become more difficult to identify the cause of the disease in the future. There was also a problem that was expected. This problem also applies to organisms other than humans.

  In one aspect, the present invention has been made in consideration of such points, and it is possible to express specific heritability without relying on case comparisons in genetic families and comparative references based on known mutation database information. It is an object to provide a technique that makes it possible to accurately identify a responsible mutation associated with a type.

  The present invention employs the following configuration in order to solve the above-described problems.

  That is, the genome analysis apparatus according to one aspect of the present invention includes a plurality of genome base sequences of a target organism having a specific hereditary phenotype and the same species as the target organism not having the hereditary phenotype. A genome base sequence acquisition unit that acquires a control part with a reference genomic base sequence that serves as a reference for the target base genomic base sequence to identify a mutation site, and the reference genomic base A plurality of partial genomic base sequences that identify a plurality of candidate mutations designated as responsible mutations of the inherited phenotype of the target organism from the mutations identified by comparison with the sequence, and each include at least one candidate mutation , And based on the number of mutations and position information contained in each partial genome base sequence, the target organism and each control organism A difference degree calculation unit for calculating a difference degree indicating a difference degree of the partial genome base sequence and a difference degree indicating a difference degree of the partial genome base sequence between the plurality of control organisms for each partial genome base sequence; Based on a predetermined test method, whether there is a significant difference between the degree of difference of the mutation site between the target organism and each of the control organisms and the degree of difference of the mutation site between the plurality of control organisms Is performed for each partial genome base sequence, and as a result an evaluation value for evaluating the specificity of the candidate mutations included in each partial genome base sequence, the statistic based on the test is calculated for each partial genome base sequence. A candidate mutation evaluation unit that outputs the result of the test in a state in which the rank of the candidate mutation based on the evaluation value can be specified. And the difference calculation unit changes the size of each partial genome base sequence, the difference of the mutation site between the target organism and each control organism, and the mutation site between the plurality of control organisms The degree of difference is configured to be calculated again for each partial genome base sequence, and the candidate mutation evaluation unit calculates the difference degree of the mutation site between the target organism and the control organisms calculated again, and the plurality of control organisms. The statistic by the test is recalculated for each partial genome base sequence by performing the test again using the degree of variation between the mutation sites, and the statistic calculated for each change in size is evaluated. The statistic adopted as a value is selected for each partial genome base sequence.

  The genome analysis apparatus according to the above configuration includes a genome base sequence of a target organism having a specific hereditary phenotype and genomes of a plurality of control organisms of the same species as the target organism that do not have the hereditary phenotype. And base sequence. A specific hereditary phenotype is a heritable characteristic that appears in the target organism, for example, a hereditary disease. In each genomic base sequence, a location (mutation location) where a base mutation has occurred is identified by comparison with a reference genomic base sequence that serves as a reference for the genomic base sequence of the target organism.

  Next, a plurality of candidate mutations that are candidates for responsible mutations related to the inherited phenotype of the target organism are identified from the mutations identified by comparison with the reference genome base sequence, and each of the candidate mutations is included. Specify multiple partial genome sequences. And about this partial genome base sequence, the difference degree of the mutation location between a target organism and each control organism and the difference degree of the mutation location between several control organisms are calculated.

  Further, for example, based on a predetermined test method such as t-test, is there a significant difference between the degree of variation between the target organism and each control organism and the degree of variation between the plurality of control organisms? The test for whether or not is performed for each partial genome base sequence. Thereby, the statistic calculated by the test performed for each partial genome base sequence is adopted as an evaluation value for evaluating the specificity of candidate mutations included in each partial genome base sequence. Then, the result of the test is output in a state where the rank based on the evaluation value can be specified.

  Here, in the processing for calculating the degree of difference, the size of each partial genome sequence is changed, and the degree of variation between the target organism and each control organism and the degree of variation between the plurality of control organisms. And calculate again. Then, the above-described test is performed again using the difference between the mutation points between the target organism and each control organism calculated again and the difference between the mutation points between the plurality of control organisms. Recalculation is performed for each partial genome base sequence, and a statistic to be adopted as an evaluation value is selected for each partial genome base sequence from among the statistics calculated for each change in size.

  Therefore, according to the above configuration, for each candidate mutation, it is possible to evaluate whether the candidate mutation specifically occurs in the target organism while adjusting the size of the partial genome base sequence. Therefore, it is possible to accurately identify a responsible mutation related to a specific hereditary phenotype without depending on case comparison in a genetic family and comparison reference based on known mutation database information.

  Further, as another form of the genome analysis apparatus according to the above aspect, the dissimilarity calculation unit, with respect to the number of mutations included in the union of the mutation sites in each of the two organisms included in each partial genome base sequence, You may calculate the difference degree of the mutation location defined by the ratio of the number of the mutation location which can be recognized only in either organism between both these organisms. According to the said structure, since the difference degree of a displacement location is defined by the ratio based on what is called a Hamming distance, each candidate variation | mutation can be evaluated by simple calculation.

  As another form of the genome analysis apparatus according to the above aspect, the genome sequence acquisition unit may acquire genome base sequences of the plurality of control organisms including organisms unrelated to the target organism. According to this configuration, since there is no need to have a blood relationship between the control organism and the target organism, the degree of freedom in selecting the control organism can be increased.

  Further, as another form of the genome analysis apparatus according to the above aspect, the difference calculation unit combines the partial genomic base sequences by combining a plurality of partial genomic regions spaced from each other on the genomic base sequence. May be included. According to this configuration, a partial genome base sequence can be specified by combining a plurality of partial genomic regions that are separated from each other on the base sequence. The degree of freedom in designating the base sequence can be increased.

  As another form of the genome analysis apparatus according to the above aspect, the genome base sequence acquisition unit removes at least a part of intron regions from the entire genome region as the genome base sequences of the target organism and the control organism. A genomic base sequence may be obtained. The intron region is a region having no genetic information. Therefore, intron regions need not be considered in identifying the cause of the inherited phenotype. According to this configuration, since at least a part of the intron region is removed from the entire genome region, it is possible to reduce the amount of calculation required for the genome analysis of the present invention.

  As another form of the genome analyzing apparatus according to the above aspect, the target organism and the plurality of control organisms may be humans, and the specific hereditary phenotype may be a hereditary disease. Good. Furthermore, the candidate mutation may be a mutation that is detected only at a certain frequency or less in a homogenous population of organisms detected in the genome base sequence of the target organism. According to this configuration, it is possible to configure a genome analysis apparatus that can identify a responsible mutation that causes a hereditary disease.

  In addition, as another form of the genome analysis apparatus according to each of the above forms, an information processing system that realizes each of the above configurations, an information processing method, or a program may be used. It may be a storage medium that can be read by a computer, other devices, machines, or the like in which such a program is recorded. Here, the computer-readable recording medium is a medium that stores information such as programs by electrical, magnetic, optical, mechanical, or chemical action. The information processing system may be realized by one or a plurality of information processing devices.

  For example, in the genome analysis method according to one aspect of the present invention, a computer has a genome base sequence of a target organism having a specific hereditary phenotype and the same species as the target organism not having the hereditary phenotype. A genomic base sequence obtaining step of obtaining a plurality of control organism genomic base sequences in a state in which a mutation site is identified by comparing each of the genomic base sequences of a plurality of control organisms with a reference genomic base sequence serving as a reference for the genomic base sequence of the target organism, A plurality of candidate mutations that are specified as a responsible mutation in the inherited phenotype of the target organism are identified from the mutations identified by comparison with a reference genome base sequence, and a plurality of portions each include at least one candidate mutation. A genome base sequence is designated, and based on the number and position information of mutations included in each partial genome base sequence, the target organism and the A degree of difference indicating the degree of difference in the partial genome base sequence between control organisms and a degree of difference indicating the degree of difference in the partial genome base sequence between the plurality of control organisms for each partial genome base sequence Based on the calculation step and a predetermined test method, there is a significant difference between the difference degree of the mutation site between the target organism and each control organism and the difference level of the mutation site between the plurality of control organisms. Is performed for each partial genome base sequence, and the statistical value obtained by the test is used as an evaluation value for evaluating the specificity of candidate mutations included in each partial genome base sequence. Candidate mutation evaluation step calculated for each sequence, and outputs the result of the test in a state where the rank of the candidate mutation based on the evaluation value can be specified An output step, and in the difference degree calculation step, the size of each partial genome base sequence is changed, the difference degree of the mutation site between the target organism and each control organism, and the plurality of controls. The degree of difference between mutation sites between organisms is calculated again for each partial genome base sequence. In the candidate mutation evaluation step, the degree of difference between mutation sites between the target organism and each control organism calculated again, and the plural By performing the test again using the degree of variation of the mutation site between the control organisms, the statistic by the test is recalculated for each partial genome base sequence, and the statistic calculated for each change in size. Is an information processing method for selecting, for each partial genome base sequence, the statistic employed as an evaluation value.

  In addition, for example, the genome analysis program according to one aspect of the present invention provides a computer with a genome base sequence of a target organism having a specific hereditary phenotype and the target organism not having the hereditary phenotype. A genome base sequence acquisition step of acquiring a mutation site in a specified state by comparing each of the genome base sequences of a plurality of control organisms of the same type with a reference genome base sequence serving as a reference of the genome base sequence of the target organism, and A plurality of candidate mutations specified as responsible mutations of the inherited phenotype of the target organism are identified from the mutations identified by comparison with the reference genome base sequence, and each of the candidate mutations includes at least one candidate mutation. Based on the number and position information of the mutations contained in each partial genome base sequence. The degree of difference indicating the difference in the partial genome base sequence between the organism and each control organism and the degree of difference indicating the difference in the partial genome base sequence between the plurality of control organisms for each partial genome base sequence Based on a difference degree calculation step to be calculated and a predetermined test method, the difference degree of the mutation site between the target organism and each control organism and the difference level of the mutation site between the plurality of control organisms are significant. By performing the test for whether there is a significant difference for each partial genome base sequence, the statistical value by the test is used as an evaluation value for evaluating the specificity of the candidate mutation included in each partial genome base sequence. A candidate mutation evaluation step for calculating each partial genome base sequence, and the test in a state where the rank of the candidate mutation can be specified based on the evaluation value. An output step of outputting a result, and in the difference calculation step, the computer changes the size of each of the partial genome base sequences to change the mutation site between the target organism and each control organism. The degree of difference and the degree of difference between the plurality of control organisms are calculated again for each partial genome base sequence. In the candidate mutation evaluation step, the computer calculates the target organism and the control organisms calculated again. The statistic by the test is recalculated for each partial genome base sequence by performing the test again using the degree of difference of the mutation site between the control organisms and the difference of the mutation site between the plurality of control organisms. , The statistic to be adopted as an evaluation value out of the statistic calculated for each change in size It is a program that allows selection for each genome base sequence.

  According to the present invention, it is possible to accurately identify a responsible mutation related to a specific hereditary phenotype without depending on case comparison in a hereditary family and comparison reference based on known mutation database information. .

FIG. 1 shows an example of a scene where the present invention is applied. FIG. 2 illustrates a hardware configuration of the genome analysis apparatus according to the embodiment. FIG. 3 illustrates the functional configuration of the genome analysis apparatus according to the embodiment. FIG. 4 illustrates a processing procedure related to genome analysis according to the embodiment. FIG. 5 exemplifies a method for comparing displacement points in the partial genome base sequence according to the embodiment. FIG. 6 shows an example of a method for designating a partial genome base sequence. FIG. 7 shows a calculation example of the degree of difference between displacement points between a patient and a healthy person. FIG. 8A shows a family that is the target of identifying the cause of the disease of hypertrophic cardiomyopathy. FIG. 8B shows a family that is the target for identifying the cause of the disease of hypertrophic cardiomyopathy. FIG. 8C shows a family that is the target for identifying the cause of the disease of restrictive cardiomyopathy. FIG. 9 shows a processing result of the genome analysis apparatus according to the embodiment.

  Hereinafter, an embodiment according to an aspect of the present invention (hereinafter, also referred to as “this embodiment”) will be described with reference to the drawings. However, this embodiment described below is only an illustration of the present invention in all respects. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention. That is, in implementing the present invention, a specific configuration according to the embodiment may be adopted as appropriate. Although data appearing in the present embodiment is described in a natural language, more specifically, it is specified by a pseudo language, a command, a parameter, a machine language, or the like that can be recognized by a computer.

§1 Application scene First, the scene where the present invention is applied will be described with reference to FIG. FIG. 1 illustrates a scene where the genome analysis apparatus 1 according to the present embodiment is used. The genome analysis apparatus 1 according to the present embodiment includes a genome base sequence of a patient (case) suffering from a genetic disease and a genome base sequence of a healthy subject (control) suffering from the genetic disorder. It is an information processing apparatus that can identify a responsible mutation that causes a genetic disease by analyzing and evaluating each candidate mutation that is a candidate for the cause of the genetic disease.

  Specifically, the genome analysis apparatus 1 according to the present embodiment acquires a genome base sequence in which mutation sites are specified for each of a patient and a plurality of healthy individuals. A mutation site in each genomic base sequence can be identified by comparing a reference genomic base sequence such as hg19 with each genomic base sequence.

  Subsequently, the genome analyzing apparatus 1 identifies a plurality of candidate mutations that are candidates for responsible mutations from among the mutations occurring on the patient's genomic base sequence, and a plurality of partial genomic bases including at least one of the candidate mutations Specify an array. And the genome analyzer 1 calculates the difference which shows the difference degree of the variation | mutation location between a patient and a healthy subject about each designated partial genome base sequence, and stores the calculated difference in a 1st group. . Similarly, the genome analysis apparatus 1 calculates a difference indicating the difference between the mutated portions between healthy individuals for each designated partial genome base sequence, and stores the calculated difference in the second group.

  Furthermore, the genome analysis apparatus 1 performs a test for whether there is a significant difference between the first group and the second group for each partial genome base sequence based on a predetermined test method such as a t test. . The statistic calculated for each partial genome base sequence by this test process is whether or not there is a significant difference between the difference in variation between patients and healthy individuals and the difference in variation between healthy individuals. Show. Therefore, this statistic can be used as an evaluation value for evaluating whether or not each candidate mutation is specifically generated in a patient.

  Here, it is assumed that the candidate mutation specifically generated in the patient is highly likely to be a responsible mutation for the inherited disease affected by the patient. Therefore, the genome analysis apparatus 1 outputs the result of the test in a state where the rank of each candidate mutation based on this evaluation value can be specified. As a result, the user who sees this result identifies candidate mutations that are likely to be responsible mutations of hereditary diseases that the patient suffers from, and identifies responsible mutations from the higher-ranking candidate mutations. Is possible.

  However, each evaluation value can depend on the size of each partial genome base sequence, that is, the size of the range for evaluating each candidate mutation. Depending on the size setting of each partial genome base sequence, each candidate mutation may not be evaluated correctly. Therefore, the genome analysis apparatus 1 according to the present embodiment changes the size of each partial genomic base sequence in the process related to the calculation of the degree of difference, and the degree of difference between the mutated portion between the patient and the healthy person and between the healthy person Recalculate the degree of difference between the mutation sites. Then, the genome analyzer 1 re-calculates the statistic based on the test for each partial genome base sequence by re-executing the test using the recalculated degree of difference, and calculates each time the size is changed. Among the statistics, the statistics used as the evaluation value can be selected for each partial genome base sequence.

  Thus, according to the present embodiment, for each candidate mutation, the size of the partial genome base sequence, that is, the number of bases included in the partial genome base sequence is adjusted, and the candidate mutation occurs specifically in the patient. It is possible to evaluate whether or not there is a mutation. Therefore, it is possible to accurately identify a responsible mutation that causes a hereditary disease without depending on case comparison in a genetic family and comparison reference based on known mutation database information.

  In the present embodiment, as described above, an example in which the present invention is applied to processing for identifying a responsible mutation of a genetic disease will be described. The “hereditary disease” corresponds to an example of the “specific hereditary phenotype” of the present invention. Here, the “phenotype” indicates a characteristic in which the composition of a gene of an organism appears as a trait. The “patient” corresponds to an example of the “target organism” of the present invention, and the “normal subject” corresponds to an example of the “control organism” of the present invention. However, the applicable scope of the present invention is not limited to the process for identifying a responsible mutation of a genetic disease, and the present invention includes a process for identifying a responsible mutation that causes a specific hereditary phenotype to occur. Widely applicable to.

  That is, the characteristics that can be adopted as the “specific hereditary phenotype” of the present invention are not limited to hereditary diseases, and can be appropriately selected according to the embodiment. The “specific hereditary phenotype” can be appropriately selected from phenotypes maintained by animals or plants for several generations in addition to hereditary diseases. In this embodiment, the species of the test organism (target organism) having a specific genetic disease and the test organism (control organism) having no genetic disease are animals other than humans. It may be a plant or a plant. The species of “target organism” and “control organism” of the present invention can be appropriately selected according to the embodiment. However, the same kind of organism is set as the “target organism” and “control organism”.

§2 Configuration example <Hardware configuration>
Next, the hardware configuration of the genome analysis apparatus 1 will be described with reference to FIG. FIG. 2 illustrates the hardware configuration of the genome analysis apparatus 1 according to this embodiment. As illustrated in FIG. 2, the genome analysis apparatus 1 stores a control unit 11 including a CPU, a RAM (Random Access Memory), a ROM (Read Only Memory), and the like, a program 5 executed by the control unit 11, and the like. Unit 12, a display device 13 for displaying an image, such as a liquid crystal display, an input device 14 for inputting a mouse, a keyboard, etc., an external interface 15 for connecting to an external device, and communicating via a network And a drive 17 for reading a program stored in the storage medium 6 is electrically connected to the computer. In FIG. 2, the communication interface and the external interface are described as “communication I / F” and “external I / F”, respectively.

  It should be noted that regarding the specific hardware configuration of the genome analyzing apparatus 1, the components can be omitted, replaced, and added as appropriate according to the embodiment. For example, the control unit 11 may include a plurality of processors. The display device 13 and the input device 14 may be replaced with a touch panel display. Furthermore, the genome analysis device 1 may include a plurality of external interfaces 15 and may be connected to a plurality of external devices.

  The program 5 stored in the storage unit 12 is a program for causing the genome analysis apparatus 1 to execute each process related to genome analysis, which will be described later, and corresponds to a “program” of the present invention. The program 5 may be recorded on the storage medium 6.

  The storage medium 6 stores information such as a program by an electrical, magnetic, optical, mechanical, or chemical action so that information such as a program recorded by a computer or other device or machine can be read. It is a medium to do. The storage medium 6 corresponds to the “storage medium” of the present invention.

  Here, in FIG. 2, as an example of the storage medium 6, a disk type storage medium such as a CD (Compact Disk) or a DVD (Digital Versatile Disk) is illustrated. However, the type of the storage medium 6 is not limited to the disk type and may be other than the disk type. Examples of the storage medium other than the disk type include a semiconductor memory such as a flash memory.

  Further, as the genome analysis device 1, for example, a general-purpose device such as a PC (Personal Computer), a tablet terminal, or the like may be used in addition to a device designed exclusively for the provided service. Furthermore, the genome analysis apparatus 1 may be implemented by one or a plurality of computers.

<Functional configuration example>
Next, the functional configuration of the genome analysis apparatus 1 will be described with reference to FIG. FIG. 3 illustrates a functional configuration of the genome analysis apparatus 1 according to the present embodiment. In the present embodiment, the control unit 11 of the genome analysis apparatus 1 expands the program 5 stored in the storage unit 12 in the RAM. And the control part 11 interprets and runs the program 5 expand | deployed by RAM by CPU, and controls each component. Thereby, the genome analysis apparatus 1 functions as a computer including the genome base sequence acquisition unit 21, the difference calculation unit 22, the candidate mutation evaluation unit 23, and the output control unit 24.

  The genome base sequence acquisition unit 21 obtains a genome base sequence of a target organism having a specific hereditary phenotype and a genome base sequence of a plurality of control organisms not having the heritable phenotype, respectively. Obtained in a state where the mutation site is identified by comparing with a reference genome base sequence that serves as a reference of the sequence. In this embodiment, the specific hereditary phenotype is a hereditary disease and the target and control organisms are humans. Further, the reference organism related to the reference genome base sequence is also human and is the same species as the target organism and the control organism. The reference genomic base sequence may be a known genomic base sequence such as hg19, or may be a genomic base sequence appropriately set by the user.

  Next, the degree-of-difference calculation unit 22 identifies a plurality of candidate mutations designated as the responsible mutations of the inherited phenotype of the target organism from the mutations identified by comparison with the reference genome base sequence, and selects candidate mutations. A plurality of partial genome base sequences each including at least one are designated. The degree-of-difference calculation unit 22 then shows the degree of difference indicating the degree of difference in the displacement location in the partial genome base sequence between the target organism and each control organism based on the number of mutations and position information included in each partial genome base sequence. And the degree of difference indicating the degree of difference in the displacement location in the partial genome base sequence between the plurality of control organisms is calculated for each partial genome base sequence.

  Further, the candidate mutation evaluation unit 23 is significant in the degree of difference between the displacement points between the target organism and each control organism and the degree of difference between the mutation points among the plurality of control organisms based on a predetermined test method such as t-test. A test for whether there is a significant difference is performed for each partial genome base sequence. Thereby, the candidate mutation evaluation unit 23 calculates a statistic based on the test for each partial genome base sequence as an evaluation value for evaluating the specificity of the candidate mutation included in each partial genome base sequence.

  Then, the output control unit 24 outputs the result of the test in a state where the rank of candidate mutations based on the evaluation value can be specified. In a state in which the rank of candidate mutations based on the evaluation value can be specified, the output control unit 24 may display the candidate mutations arranged in order from the highest evaluation value, or the rank based on the evaluation value is assigned to each candidate mutation. You may display correspondingly. The display form can be set as appropriate.

  Here, the difference degree calculation unit 22 changes the size of each partial genome base sequence, the difference degree of the displacement place between the target organism and each control organism, and the difference of the mutation place between the plurality of control organisms. The degree is calculated again for each partial genome sequence. Then, the candidate mutation evaluation unit 23 re-uses the above limitation using the recalculated difference in displacement location between the target organism and each control organism, and the difference in variation location between the plurality of control organisms. By executing, the statistical quantity by the test is calculated again for each partial genome base sequence, and the statistical quantity to be adopted as the evaluation value is selected for each partial genomic base sequence among the statistical quantities calculated for each change in size. Thus, it is possible to evaluate whether or not the candidate mutation is a mutation specifically generated in the target organism while adjusting the size of the partial genome base sequence.

  In the present embodiment, an example in which these functions are realized by a general-purpose CPU has been described. However, some or all of these functions may be realized by one or more dedicated processors. In addition, regarding the functional configuration of the genome analysis apparatus 1, functions may be omitted, replaced, and added as appropriate according to the embodiment. Each function will be described in detail in an operation example described later.

§3 Operation Example Next, an operation example of the genome analysis apparatus 1 will be described with reference to FIGS. FIG. 4 illustrates a processing procedure related to genome analysis of the genome analysis apparatus 1. In addition, the process procedure regarding the genome analysis demonstrated below is only an example, and each process may be changed as much as possible. Further, in the processing procedure described below, steps can be omitted, replaced, and added as appropriate according to the embodiment.

(Step S101)
In step S101, the control unit 11 functions as the genomic base sequence acquisition unit 21, and acquires the genomic base sequence in which the mutation site is specified for each of the patient and a plurality of healthy persons. When acquiring each genome base sequence, the control unit 11 advances the processing to the next step S102.

  A patient is a person who suffers from a hereditary disease, and a healthy person (control) is a person who does not suffer from the hereditary disease that the patient suffers from. The healthy person may be a patient's relative or an unrelated person. The genomic base sequences of patients and healthy individuals can be analyzed by next-generation sequencers such as HiSeq / MiSeq (Illumina) and Genome Sequencer FLX + System (Roche). This genome base sequence includes information on bases constituting the genome and position information indicating positions (base numbers) on the base sequences.

  For example, the control unit 11 acquires the genome base sequences of the patient and the healthy person analyzed by a next-generation sequencer or the like. For healthy persons, the control unit 11 acquires genome base sequences for a plurality of people. Each genome base sequence may be acquired from a storage medium incorporated in the storage unit 12 or the drive 17 in the genome analyzing apparatus 1 or may be acquired from another information processing apparatus via a network.

  In addition, the control unit 11 acquires a reference genome base sequence that serves as a reference for the human genome base sequence. As with each genomic base sequence, the reference genomic base sequence may be acquired from a storage medium incorporated in the storage unit 12 or the drive 17 in the genome analyzer 1 or other information processing apparatus via a network. May be obtained from This reference genomic base sequence may be, for example, a known genomic base sequence such as hg19, or may be a genomic base sequence appropriately set by the user if it is not a subject-derived genomic base sequence.

  And the control part 11 compares each genome base sequence of a patient and a healthy subject, and a reference genome base sequence, and the location (mutation location) where the single nucleotide variation (SNV; Single Nucleotide Variant) has occurred in each genome base sequence ). Specifically, the control unit 11 determines whether or not the bases arranged at the same position (base number) match between each genomic base sequence and the reference genomic base sequence. And when it determines with the base arrange | positioned at the same position (base number) not matching, the control part 11 carries out the single base mutation (SNV) of the said position (base number) determined not to correspond. ; Single Nucleotide Variant) is recognized as a mutation site. On the other hand, when the bases arranged at the same position (base number) match, the control unit 11 recognizes the position (base number) as a place where no mutation has occurred. Thereby, the control part 11 can acquire the genome base sequence by which the variation | mutation location was identified about each of a patient and several healthy persons.

  Note that the comparison process with the reference genome base sequence may be executed by another information processing apparatus. That is, the control unit 11 may omit the comparison process with the reference genome base sequence, and acquire the genomic base sequence in which the mutation site has been specified in advance for each of the patient and a plurality of healthy persons.

  The whole genome region includes an intergenic region and an intragenic region (intron region, exon region). Of these, the intron region is a region that does not encode genetic information, and since it is unlikely to contain a responsible mutation causing a genetic disease, it may be excluded in identifying the responsible mutation. Similarly, the intergenic region is also unlikely to contain a responsible mutation that causes a hereditary disease, and thus may be excluded in identifying the responsible mutation.

  Therefore, each genome base sequence acquired in this step S101 does not have to correspond to the entire genome region, and is obtained by removing at least a part of the intron region and / or the intergenic region from the entire genome region. May be. Furthermore, each obtained genome base sequence may correspond to the entire exon region obtained by removing all intron regions and intergenic regions from the entire genome region. As a result, the size of each genome base sequence to be analyzed can be suppressed, and the amount of calculation required for a series of processing in this operation example can be reduced.

  Note that the entire exon region may include a region near each exon region. The neighboring region is a region including several to several tens of bases from the end of each exon region, for example. Further, hg19, which is an example of the reference genome base sequence, corresponds to the entire genome region. Therefore, when hg19 is used as a reference genome base sequence and an intron region or the like is excluded in step S101, the control unit 11 excludes the intron region or the like from hg19.

(Step S102)
In the next step S102, the control unit 11 functions as the dissimilarity calculation unit 22, specifies the size of each partial genomic base sequence including each candidate mutation, and specifies between two individuals (between a patient and a healthy person or a healthy person). And the degree of difference between mutation sites occurring in each partial genome base sequence in each other). When the calculation of each degree of difference is completed, the control unit 11 advances the processing to the next step S103.

  FIG. 5 exemplifies a comparison scene of mutation sites occurring in each partial genome base sequence between two individuals (between a patient and a healthy person or between a healthy person and another healthy person).

  First, the control unit 11 identifies a candidate mutation from among mutations detected in the patient's genomic base sequence. As illustrated in FIG. 5, candidate mutations are designated from among single nucleotide mutations detected in a patient's genomic base sequence as candidates for inherited disease responsible mutations. Candidate mutations can be designated as appropriate. For example, rare mutations that are detected only within a certain frequency or less in a human biological population detected in a patient's genomic base sequence may be adopted as candidate mutations. .

Such rare mutations can be identified as follows. That is, among the single nucleotide mutations obtained by comparison with the reference genome base sequence,
1) Mutation whose sequence quality value is below a predetermined value 2) Mutation included in strand bias 3) Mutation that does not cause codon changes 4) Known single nucleotide polymorphisms (SNPs) found in healthy individuals
By excluding, rare mutations can be identified. Generally, about 1.5 million single base mutations are identified from a patient's genomic base sequence by comparison with a reference genomic base sequence. On the other hand, by excluding the above 1) to 4), mutations found in the patient's genomic base sequence can be limited to about 600 rare mutations.

  The strand bias indicates a state in which a sequence tag of only one strand exists in the pair end analysis. The paired-end analysis is a sequence analysis method provided by Illumina, in which only both ends of a partial sequence having a specific length are sequenced. The sequenced sequence (tag) is usually mapped to a highly homologous portion of the sequence with reference to the reference genome base sequence. However, for some reason or for some reason, a sequenced tag may only be mapped to one of the sequences at both ends. That is, there is a possibility that some kind of error has occurred in a region where only one of the sequenced tags is mapped, that is, a region having a strand bias. Therefore, in the above 2), in order to exclude such a possibility, the control unit 11 excludes the mutation included in the region having the strand bias.

  Next, the control unit 11 designates a plurality of partial genomic base sequences each including at least one candidate mutation in the patient's genomic base sequence. For example, as illustrated in FIG. 5, the control unit 11 specifies the range of each partial base sequence so that each candidate mutation is located in the center of each partial genomic base sequence and includes 30K (30000) bases. To do. Since the genome base sequence holds the position information of each base, the range of each partial genome base sequence can be specified by the position information of the bases at both ends of each partial genome base sequence. And the control part 11 designates the area | region of the same position and range as each partial genome base sequence designated in the patient's genome base sequence as each partial genome base sequence in the genome base sequence of each healthy subject.

  The size of the partial genome base sequence, the number of candidate mutations included, and the method for specifying the position and range of the candidate mutations included can be selected as appropriate according to the embodiment. For example, the position of the candidate mutation in each partial genome sequence may not be limited to the center of the sequence, but may be the end of the sequence.

  In FIG. 5, the control unit 11 designates one continuous region as each partial base sequence. However, the method of designating the genomic region included in each partial base sequence need not be limited to such an example, and the control unit 11 is spaced from the position on the genomic base sequence as illustrated in FIG. A partial genome base sequence may be designated by combining a plurality of partial genome regions.

  FIG. 6 shows an example of a scene in which a partial genome base sequence is designated by combining a plurality of partial genomic regions that are spaced apart from each other on the genomic base sequence. In FIG. 6, there are three partial genomic regions (31, 32, 33) that are separated on the genomic base sequence, and the region 32 includes candidate mutations. In this case, for example, the control unit 11 combines the three regions (31, 32, 33) by omitting the bases included between the region 31 and the region 32 and between the region 32 and the region 33. By this, one partial genome base sequence can be designated.

  According to this region designating method, it is possible to designate a region far from the position on the genome base sequence as one partial genome base sequence. Therefore, it is possible to increase the degree of freedom in designating a partial genome base sequence that is a range for evaluating candidate mutations.

  Next, as illustrated in FIG. 5, the control unit 11 indicates the degree of variation between the patient and each healthy person based on the number of mutations and position information included in each partial genome base sequence. The degree of difference and the degree of difference indicating the degree of variation of the mutation location between healthy individuals are calculated. The degree of difference may be appropriately set according to the embodiment as long as it is an index that can indicate the degree of difference between the mutation sites in the two partial genome base sequences corresponding to each other, such as a correlation coefficient.

  For example, the number of mutation sites is the ratio of the number of mutation sites that can only be found in either organism between the two organisms with respect to the total number of mutations included in the union of mutation sites in each of the two organisms included in each partial genome base sequence The degree of difference may be defined. The degree of difference defined in this way will be described further with reference to FIG.

  FIG. 7 illustrates a scene in which the degree of difference between mutation sites included in a partial genome base sequence between two organisms is calculated. In the scene illustrated in FIG. 7, the patient's partial genome base sequence includes five mutations including candidate mutations. On the other hand, the partial genome base sequence of a healthy subject corresponding to the partial genome base sequence also contains five mutations. Both partial genome base sequences contain two common mutations at the same position. It can be determined by referring to the position information of the mutation whether or not the mutation sites included in both partial genome base sequences are identical.

  In this case, the total number of mutation sites included in the union of the mutation sites in each of the two organisms included in each partial genome base sequence is 2 mutations out of a total of 10 mutation sites included in both partial genome base sequences. Since the places are common to each other, there are 8 places. In addition, the number of mutation sites that can be found only in either organism between the two organisms is the two mutation sites common to each other (total of 4 sites) among the total of 10 mutation sites included in both partial genome sequences In order to eliminate, there are 6 places. Therefore, the dissimilarity given by this definition is 0.75.

  As the ratio defined as above includes more mutation sites having the same position between two partial genome sequences, the number of mutation sites that can be found only in either organism between the two organisms decreases. The value becomes smaller. Therefore, it is possible to show the degree of difference between mutation sites in two partial genome base sequences corresponding to each other by the ratio defined as described above. Note that the ratio (degree of difference) defined as above is also referred to as a “hamming distance ratio” below.

  The number of mutation sites that can be found only in either organism between these two organisms corresponds to the so-called Hamming distance between the two sets. This Hamming distance calculation can be configured by, for example, exclusive OR calculation and count calculation, and can be performed very easily. Therefore, by defining the degree of difference as described above, it is possible to derive the degree of difference between mutation sites between two organisms by a very simple calculation.

  It should be noted that the method for calculating the degree of difference between mutations in healthy individuals can be explained in the same way by replacing the partial genomic nucleotide sequence of the patient with the partial genomic nucleotide sequence of another healthy subject. The control part 11 calculates the difference degree of the mutation location between a patient and each healthy person for every partial genome base sequence, and stores the calculated difference degree in a 1st group. On the other hand, the control part 11 calculates the difference degree of the mutation location between two healthy persons for every partial genome base sequence, and stores the calculated difference degree in a 2nd group. When there is one patient and 41 healthy people, 41 differences are calculated for each partial genome base sequence between the patient and each healthy subject. On the other hand, the difference degree of the mutation location between two healthy individuals is calculated for 820 per partial genome base sequence.

(Step S103)
In the next step S103, the control unit 11 functions as the candidate mutation evaluation unit 23, and whether or not there is a significant difference between the first group and the second group based on a predetermined test method. The test is performed for each partial genome sequence. Moreover, the control part 11 calculates the test amount by the said test for every partial genome base sequence as a candidate of the evaluation value for evaluating the specificity of the candidate variation | mutation contained in each partial genome base sequence. Then, after calculating the evaluation value, the control unit 11 advances the processing to the next step S104.

  Note that the predetermined test method may be any test method that can determine whether or not there is a difference between two data, and can be appropriately selected according to the embodiment. For example, Kolmogorov-Smirnov test, t-test, etc. can be adopted as the predetermined test method. When Kolmogorov-Smirnov test, t-test, etc. are adopted as a predetermined test method, in this step S103, t value and / or p value are calculated as statistics in the test.

  Here, the t value is a numerical value indicating the degree of difference between the two groups. The larger the absolute value of the t value, the more the null hypothesis that there is no difference between the two groups can be rejected, that is, there is a significant difference between the two groups. Therefore, when the t value is used as an evaluation value for evaluating the specificity of the candidate mutation, the control unit 11 treats the evaluation indicated by the evaluation value as the absolute value of the t value is higher.

  Moreover, p value is a numerical value which shows the possibility that the difference between both groups will arise accidentally. The closer the p-value is to 0, the more the null hypothesis that there is no difference between the two groups can be rejected, that is, there is a significant difference between the two groups. Therefore, when using the p value as an evaluation value for evaluating the specificity of the candidate mutation, the control unit 11 treats the evaluation indicated by the evaluation value as the p value is closer to 0.

  That is, the higher the evaluation indicated by the evaluation value, the more significant difference is generated between the first group and the second group. Therefore, it can be shown that the higher the evaluation indicated by the evaluation value, the patient has a specific mutation in the corresponding partial genome base sequence.

  Here, since the healthy person does not suffer from the hereditary disease that the patient suffers from, the mutation that occurs specifically in the patient is likely to be a responsible mutation of the hereditary disease. Therefore, it can be shown that the higher the evaluation indicated by the evaluation value is, the higher the possibility that the candidate mutation included in the corresponding partial genome base sequence is a responsible mutation of the hereditary disease.

(Step S104)
In the next step S104, the control unit 11 functions as the dissimilarity calculation unit 22, changes the size of each partial genome base sequence, and between two individuals (between a patient and a healthy person or between a healthy person and another healthy person). The difference between the mutation sites in (between) is recalculated for each partial genome base sequence. Then, after recalculating the degree of difference, the control unit 11 advances the processing to the next step S105. Except for changing the size of each partial genome base sequence, the process of step S104 is the same as the process of step S102.

  For example, when the size of each partial genome base sequence is set to 30K in step S102 and the size of each partial genome base sequence is changed at an interval of 10K (10000), the control unit 11 Reset the genome sequence size to 40K (40000). And the control part 11 recalculates the difference degree of the variation location between two individuals based on each partial genome base sequence reset to 40K pieces.

  In step S104, the size of each partial genome base sequence may be changed to be smaller than the size of each partial genome base sequence. The amount of change in the size of each partial genome base sequence can be appropriately set according to the embodiment.

(Step S105)
In the next step S105, the control unit 11 functions as the candidate mutation evaluation unit 23 and uses each recalculated degree of difference to determine whether there is a significant difference between the first group and the second group. The above test is performed again for each partial genome sequence. Thereby, the control part 11 recalculates the statistic obtained with respect to each partial genome base sequence, ie, the candidate of the evaluation value of each candidate mutation. And after recalculating the candidate of the evaluation value of each candidate mutation, the control part 11 advances a process to following step S106. Note that the processing in step S105 is the same as that in step S103, except that the differences calculated in step S104 are used. Therefore, description of this step S105 is abbreviate | omitted.

(Step S106)
In the next step S106, the control unit 11 determines whether to repeat the processes in steps S104 and S105. And when it determines with repeating the process of step S104 and step S105, the control part 11 returns a process to step S104. On the other hand, when it determines with not repeating the process of step S104 and step S105, the control part 11 advances a process to step S107.

  For example, when changing the size of each partial genome base sequence from 30K to 100K (100,000) at intervals of 10K and calculating the evaluation value of each candidate mutation, the control unit 11 performs steps S104 and S100. The process of S105 is repeated seven times. In this case, when the control unit 11 calculates an evaluation value for each partial genome base sequence having a size of 100K in step S105, the process proceeds to the next step S107. On the other hand, when that is not right, the control part 11 returns a process to step S104.

  Note that the number of repetitions can be set as appropriate according to the embodiment. Moreover, the number of times of repeating the processes of step S104 and step S105 may be different for each partial genome base sequence. Furthermore, when a tendency that the statistic repeatedly calculated in step S105 does not fluctuate beyond a predetermined value appears, the control unit 11 may determine that the processes in steps S104 and S105 are not repeated. . Conditions for repeating the processes of step S104 and step S105 can be set as appropriate according to the embodiment.

(Step S107)
In the next step S107, the control unit 11 selects the statistic to be adopted as the evaluation value of each candidate mutation among the statistics calculated in step S103 and step S105 (evaluation value candidates for each candidate mutation) Select every genome base sequence). Then, after selecting evaluation values for all candidate mutations, the control unit 11 advances the processing to the next step S108.

  It should be noted that the rules for employing the evaluation values of the candidate mutations can be set as appropriate according to the embodiment. For example, the control unit 11 may employ a statistic indicating the highest evaluation among the calculated statistics as the evaluation value of each candidate mutation. In this case, when the t value is adopted as the evaluation value of each candidate mutation, the control unit 11 adopts the t value having the largest absolute value among the t values calculated in step S103 and step S105 as the evaluation value of each candidate mutation. To do. On the other hand, when adopting the p value as the evaluation value of each candidate mutation, the control unit 11 employs the p value closest to 0 among the p values calculated in step S103 and step S105 as the evaluation value of each candidate mutation.

(Step S108)
In the next step S108, the control unit 11 functions as the output control unit 24, and outputs the result of the test in a state where the rank of candidate mutations based on the evaluation value selected in step S107 can be specified. Thereby, the processing according to this operation example ends.

  In addition, as long as the rank of each candidate mutation based on the evaluation value can be specified, the output mode of the test result can be appropriately set according to the embodiment. For example, the control unit 11 may output the test result in a state where the candidate variables can be arranged in ascending order or descending order of evaluation values. For example, the control unit 11 may output the test result in a state where each candidate mutation is simply associated with the evaluation value.

  In addition, the output method of the test result can be appropriately selected according to the embodiment. For example, the control unit 11 may output the test result by displaying it on the display device 13 (display). For example, when the genome analysis apparatus 1 is connected to a printing device such as a printer via the external interface 15, the control unit 11 controls the printing device and outputs the test result by printing on paper or the like. May be.

(Action / Effect)
As described above, the genome analysis apparatus 1 according to the present embodiment acquires the genome base sequence in which the mutation site is specified for each of the patient and the healthy individual. Next, the genome analysis apparatus 1 identifies a plurality of candidate mutations that are candidates for responsible mutations from among the mutations occurring on the patient's genomic base sequence, and a plurality of partial genomic bases including at least one of the candidate mutations Specify an array. Subsequently, the genome analysis device 1 calculates the difference between the mutation sites between the patient and each healthy person and the difference between the mutation sites between the two healthy persons for each designated partial genome base sequence. Then, the genome analyzer 1 determines that there is a significant difference between the difference between the mutation sites between the patient and each healthy person and the difference between the mutation sites between the two healthy persons based on a predetermined test method. Whether or not there is a test is performed, and a statistic calculated by the test is used as an evaluation value of a candidate mutation included in the corresponding partial genome base sequence.

  Here, the statistic calculated for each partial genome base sequence is the range for evaluating whether or not the mutation site is different between the patient and the healthy person, that is, the size of each partial genome base sequence. Can depend. Therefore, if the size of each partial genome base sequence is fixed, the difference between the mutation sites contained in each partial genome base sequence cannot be accurately evaluated between the patient and the healthy subject, and each candidate mutation can be obtained. There is a possibility that the state of each partial genome base sequence cannot be correctly reflected in the evaluation value. As a result, the ranking of each candidate mutation based on the evaluation value is not accurate, and the accuracy of identifying the responsible mutation of the hereditary disease may be lowered.

  On the other hand, the genome analysis apparatus 1 according to the present embodiment calculates the evaluation value of each candidate mutation by changing the size of each partial genome base sequence by repeating the processing of step S104 and step S105. repeat. Thus, according to this embodiment, for each candidate mutation, the number of bases included in the partial genome base sequence is adjusted, and whether or not the candidate mutation is a mutation that is specifically generated in the patient is evaluated. It can be performed. Therefore, it is possible to accurately identify a responsible mutation that causes a hereditary disease without depending on case comparison in a genetic family and comparison reference based on known mutation database information.

(Other)
As mentioned above, although embodiment of this invention has been described in detail, the above description is only illustration of this invention in all the points. It goes without saying that various improvements and modifications can be made without departing from the scope of the present invention.
§4 Processing example

  Examples of the present invention will be described below. However, the present invention is not limited to the following examples.

  The genome analysis apparatus 1 according to the above embodiment makes it possible to identify a responsible mutation of the genetic disease by ranking a plurality of candidate mutations that are candidates for the responsible mutation of the genetic disease. Therefore, an example corresponding to the above embodiment was prepared, and an experiment was conducted to determine whether or not a known mutation is correctly ranked as a responsible mutation for a hereditary disease.

  8A to 8C show the family of the subject. In each figure, a square mark indicates a male and a circle indicates a female. Moreover, in FIG. 8A and FIG. 8B, the mark filled with black shows the person who suffers from hypertrophic cardiomyopathy, and the mark which does not show the person who does not suffer from hypertrophic cardiomyopathy. Similarly, in FIG. 8C, a black-filled mark indicates a person suffering from restrictive cardiomyopathy, and a mark other than that represents a person not suffering from restrictive cardiomyopathy.

  The family A patient (II-2) shown in FIG. 8A is a 58 year old male, the patient (III-1) is a 32 year old female, the family A patient (II-2) and the patient (III-1) Suffered from hypertrophic cardiomyopathy that could be inherited in the form of autosomal dominant. And the patient of family A (II-2) and patient (III-1) have a mutation of c.173G> A (p.R58Q) in MYL2 gene (rs104894369) which is one of the causes of hypertrophic cardiomyopathy. I was sharing. “Rs104894369” indicates the registration number of the mutation registered in dbSNP, which is the SNP database provided by NCBI (National Center for Biotechnology Information). “C.” Indicates that the coding DNA level is described, and “c.173G> A” indicates that the 173rd guanine (G) of the MYL2 gene is mutated to adenine (A). . “P.” Indicates that the protein level is described, and “p.R58Q” indicates that the 58th arginine (R) is substituted with glutamine (Q). The same applies to the following.

  In addition, the family B patient (III-4) shown in FIG. 8B is a 39-year-old male who suffered from hypertrophic cardiomyopathy that can be inherited in the form of autosomal dominant inheritance. In the family B patient (III-4), a mutation of c.746G> A (p.R249Q) was observed in the MYH7 gene (rs3218713), which is one of the causes of hypertrophic cardiomyopathy. “Rs3218713” indicates the registration number of the dbSNP. “C.746G> A” indicates that guanine (G) at the 746th base sequence of the MYH7 gene is mutated to adenine (A), and “p.R249Q” indicates that the 249th arginine (R) is glutamine ( Q) indicates substitution.

  On the other hand, as a negative control, this patient was diagnosed as constrained cardiomyopathy, and the mutation was not diagnosed in the patient's parents. An example evaluation experiment was performed. FIG. 8C shows Family C that was the subject of the experiment. The family C patient (III-2) shown in FIG. 8C was a 7-year-old female who suffered from restrictive cardiomyopathy that could develop due to a de novo mutation in the troponin gene or the like. This family C patient (III-2) has a mutation of c.584T> C (p.I195T) in the TNNI3 gene that has been reported to be one of the causes of restrictive cardiomyopathy. Was seen. “C.584T> C” indicates that the thymine (T) at the 584th nucleotide sequence of the TNNI3 gene is mutated to cytosine (C), and “p.I195T” indicates that the 195th isoleucine (I) is It shows that the threonine (T) is substituted.

  In the genome analysis apparatus according to the example, the genomic base sequence of each patient was obtained by analyzing the genomic base sequence of each patient using Illumina's Hiseq2000. Then, by comparing the obtained genomic base sequence of each patient with hg19, the mutation location on the genomic base sequence of each patient was identified. Further, in this example, in the same manner as for each patient, the genome base sequences of 41 healthy individuals who are not related to each patient are obtained, and the obtained genome base sequences of each healthy subject are compared with hg19. By doing so, the variation | mutation location on the genome base sequence of each healthy subject was identified.

  Next, in this example, the rare mutation identified by the above method among the mutations occurring on the genome base sequence of each patient is set as a candidate mutation, and each part is included so that each candidate mutation is included in the center of the sequence. Genome base sequence was specified. In addition, the Hamming distance rate defined as above is adopted as the degree of variation between two organisms in each partial genome sequence, the t-test is adopted as a predetermined test method, and each candidate mutation The p value calculated by the t-test was adopted as the evaluation value. Then, the size of each partial genome base sequence is varied at 10K intervals from 30K to 100K, the p value is calculated for each partial genome base sequence, and the smallest p value among the calculated p values is supported. It was adopted as the evaluation value of candidate mutations contained in the partial genome base sequence. FIG. 9 shows the result of ranking each candidate mutation of each patient according to this example.

  FIG. 9 shows the result of ranking each candidate mutation of each patient by the processing according to this example. When calculating the Hamming distance rate, if the number of single nucleotide mutations contained in each partial genome base sequence is small, the Hamming distance rate value may become unreasonably large and an error may occur. . Therefore, in this example, when each partial genome base sequence did not contain three or more single base mutations, evaluation of candidate mutations contained in the partial genome base sequences was omitted.

  Thereby, in the process concerning the patient of family A (II-2), evaluation values were calculated for 542 mutations out of 614 mutations to be evaluated as candidate mutations. In the treatment relating to the family A patient (III-1), evaluation values were calculated for 570 mutations out of 646 mutations to be evaluated as candidate mutations. In the processing related to the family B patient (III-4), evaluation values were calculated for 534 mutations among 606 mutations to be evaluated as candidate mutations. In the process related to the family C patient (III-2), evaluation values were calculated for 547 mutations out of 631 mutations to be evaluated as candidate mutations.

  According to the ranking results shown in FIG. 9, the evaluation value (p value) of the mutation of c.173G> A (p.R58Q) in the MYL2 gene (rs104894369) seen in the family A patient (II-2) is It was 0.02, and was ranked 12th, 2.2% from the top among all the rare mutations whose evaluation values were calculated. In addition, the evaluation value (p value) of c.173G> A (p.R58Q) mutation in the MYL2 gene (rs104894369) found in the family A patient (III-1) is 0.02, and the evaluation value is calculated. Among all the rare mutations, it was ranked 16th, 2.8% from the top. Furthermore, the evaluation value (p value) of the mutation of c.746G> A (p.R249Q) in the MYH7 gene (rs3218713) found in the family B patient (III-4) is 0.02, and the evaluation value is calculated. Among all the rare mutations, it ranked 9th, 1.7% from the top. That is, in hypertrophic cardiomyopathy, which is an example of a genetic disease, each mutation causing the genetic disease could be ranked within 3% from the top among rare mutations.

  On the other hand, the evaluation value (p value) of the mutation of c.584T> C (p.l195T) in the TNNI3 gene found in the family C patient (III-2) is 0.34, and all the evaluation values calculated Among the rare mutations, it was ranked 232, which is 42.4% from the top. That is, it was shown that the mutation causing constrained cardiomyopathy, which is presumed not to be a hereditary disease because no mutation was found in the parents, was not ranked high among the rare mutations. Therefore, the experimental results according to the present example showed that mutations causing genetic diseases can be ranked highly accurately among rare mutations.

1 ... Genome analyzer,
5 ... Program, 6 ... Storage medium,
DESCRIPTION OF SYMBOLS 11 ... Control part, 12 ... Memory | storage part, 13 ... Display apparatus, 14 ... Input device,
15 ... External interface, 16 ... Communication interface, 17 ... Drive,
21 ... Genome base sequence acquisition unit, 22 ... Difference calculation unit,
23 ... Candidate mutation evaluation unit, 24 ... Output control unit

Claims (8)

A genome base sequence of a target organism having a specific hereditary phenotype and a genome base sequence of a plurality of control organisms of the same species as the target organism that do not have the hereditary phenotype, respectively, A genome base sequence acquisition unit that acquires a mutation location by comparing it with a reference genome base sequence that serves as a reference for the base sequence;
A plurality of candidate mutations specified as responsible mutations of the inherited phenotype of the target organism are identified from the mutations identified by comparison with the reference genome base sequence, and a plurality of candidate mutations each including at least one candidate mutation The degree of difference indicating the degree of difference in the partial genome base sequence between the target organism and each control organism based on the number and location information of the mutations included in each partial genomic base sequence by designating the partial genomic base sequence And a difference degree calculation unit for calculating a difference degree indicating the degree of difference in the partial genome base sequence between the plurality of control organisms for each partial genome base sequence,
Based on a predetermined test method, whether there is a significant difference between the degree of difference of the mutation site between the target organism and each of the control organisms and the degree of difference of the mutation site between the plurality of control organisms Is performed for each partial genome base sequence, and as a result an evaluation value for evaluating the specificity of the candidate mutations included in each partial genome base sequence, the statistic based on the test is calculated for each partial genome base sequence. A candidate mutation evaluation unit,
An output control unit that outputs the result of the test in a state where the rank of the candidate mutation based on the evaluation value can be specified;
With
The difference calculation unit changes the size of each partial genomic base sequence to determine the difference between mutation sites between the target organism and each control organism, and the difference between mutation sites between the plurality of control organisms. Is calculated again for each partial genome base sequence,
The candidate mutation evaluation unit re-performs the test using the difference degree of the mutation site between the target organism and the control organisms calculated again, and the difference level of the mutation site between the plurality of control organisms. Then, the statistic based on the test is recalculated for each partial genome base sequence, and the statistic to be used as the evaluation value is selected for each partial genome base sequence among the statistics calculated for each change in the size. ,
Genome analyzer. The difference calculation unit is a mutation site that can be found only in any organism between the two organisms with respect to the number of mutations included in the union of the mutation sites in each of the two organisms included in each partial genome base sequence. Calculating the degree of difference between mutation sites defined by the ratio of the number of
The genome analysis apparatus according to claim 1. The genome sequence acquisition unit acquires genome base sequences of the plurality of control organisms including organisms unrelated to the target organism.
The genome analysis apparatus according to claim 1 or 2. The dissimilarity calculation unit includes specifying the partial genome base sequence by combining a plurality of partial genomic regions spaced apart on the genomic base sequence,
The genome analysis apparatus according to any one of claims 1 to 3. The genomic base sequence obtaining unit obtains a genomic base sequence obtained by removing at least a part of an intron region from the entire genomic region as a genomic base sequence of the target organism and the control organism.
The genome analysis apparatus according to any one of claims 1 to 4. The target organism and the plurality of control organisms are humans;
The specific hereditary phenotype is a hereditary disease,
The candidate mutation is a mutation that is detected only at a certain frequency or less in a homogenous population of organisms detected in the genomic base sequence of the target organism.
The genome analysis apparatus according to any one of claims 1 to 5. Computer
A genome base sequence of a target organism having a specific hereditary phenotype and a genome base sequence of a plurality of control organisms of the same species as the target organism that do not have the hereditary phenotype, respectively, A genome base sequence acquisition step for acquiring a mutation location by comparing with a reference genome base sequence serving as a reference for the base sequence;
A plurality of candidate mutations specified as responsible mutations of the inherited phenotype of the target organism are identified from the mutations identified by comparison with the reference genome base sequence, and a plurality of candidate mutations each including at least one candidate mutation The degree of difference indicating the degree of difference in the partial genome base sequence between the target organism and each control organism based on the number and location information of the mutations included in each partial genomic base sequence by designating the partial genomic base sequence And a difference degree calculating step for calculating a difference degree indicating the degree of difference in the partial genome base sequence between the plurality of control organisms for each partial genome base sequence,
Based on a predetermined test method, whether there is a significant difference between the degree of difference of the mutation site between the target organism and each of the control organisms and the degree of difference of the mutation site between the plurality of control organisms Is performed for each partial genome base sequence, and as a result an evaluation value for evaluating the specificity of the candidate mutations included in each partial genome base sequence, the statistic based on the test is calculated for each partial genome base sequence. A candidate mutation evaluation step,
An output step of outputting the result of the test in a state where the rank of the candidate mutation based on the evaluation value can be specified;
Run
In the difference calculation step, the size of each partial genome base sequence is changed, the difference between the mutation sites between the target organism and each control organism, and the difference between the mutation sites between the plurality of control organisms. Is calculated again for each partial genome base sequence,
In the candidate mutation evaluation step, the test is performed again by using the difference degree of the mutation site between the target organism and the control organisms calculated again, and the difference level of the mutation site between the plurality of control organisms. Then, the statistic based on the test is recalculated for each partial genome base sequence, and the statistic to be used as the evaluation value is selected for each partial genome base sequence among the statistics calculated for each change in size. ,
Genome analysis method. On the computer,
A genome base sequence of a target organism having a specific hereditary phenotype and a genome base sequence of a plurality of control organisms of the same species as the target organism that do not have the hereditary phenotype, respectively, A genome base sequence acquisition step for acquiring a mutation location by comparing with a reference genome base sequence serving as a reference for the base sequence;
A plurality of candidate mutations specified as responsible mutations of the inherited phenotype of the target organism are identified from the mutations identified by comparison with the reference genome base sequence, and a plurality of candidate mutations each including at least one candidate mutation The degree of difference indicating the degree of difference in the partial genome base sequence between the target organism and each control organism based on the number and location information of the mutations included in each partial genomic base sequence by designating the partial genomic base sequence And a difference degree calculating step for calculating a difference degree indicating the degree of difference in the partial genome base sequence between the plurality of control organisms for each partial genome base sequence,
Based on a predetermined test method, whether there is a significant difference between the degree of difference of the mutation site between the target organism and each of the control organisms and the degree of difference of the mutation site between the plurality of control organisms Is performed for each partial genome base sequence, and as a result an evaluation value for evaluating the specificity of the candidate mutations included in each partial genome base sequence, the statistic based on the test is calculated for each partial genome base sequence. A candidate mutation evaluation step,
An output step of outputting the result of the test in a state where the rank of the candidate mutation based on the evaluation value can be specified;
And execute
In the difference degree calculating step, the computer changes the size of each partial genome base sequence, the degree of difference between the target organism and each control organism, and the variation among the plurality of control organisms. The difference in location is calculated again for each partial genome base sequence,
In the candidate mutation evaluation step, the test is performed on the computer using the difference degree of the mutation site between the target organism and the control organisms calculated again, and the difference level of the mutation site between the plurality of control organisms. , The statistic obtained by the test is recalculated for each partial genome base sequence, and the statistic used as an evaluation value among the statistics calculated for each change in size is used as the partial genome base sequence. Let me choose every
Genome analysis program.

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