WO2006088208A1 - Method of estimating physiological change in living body and apparatus tehrefor - Google Patents

Method of estimating physiological change in living body and apparatus tehrefor Download PDF

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Publication number
WO2006088208A1
WO2006088208A1 PCT/JP2006/303083 JP2006303083W WO2006088208A1 WO 2006088208 A1 WO2006088208 A1 WO 2006088208A1 JP 2006303083 W JP2006303083 W JP 2006303083W WO 2006088208 A1 WO2006088208 A1 WO 2006088208A1
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Prior art keywords
physiological change
expression level
gene
individuals
physiological
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PCT/JP2006/303083
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French (fr)
Japanese (ja)
Inventor
Yosuke Nagasaka
Reiji Teramoto
Toru Kimura
Hiroyuki Nakagawa
Akira Ito
Hayao Ebise
Caroline Graff
Bengt Winblad
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Dainippon Sumitomo Pharma Co., Ltd
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Priority to JP2007503790A priority Critical patent/JPWO2006088208A1/en
Publication of WO2006088208A1 publication Critical patent/WO2006088208A1/en

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the present invention relates to a technique for predicting physiological changes in a living body based on gene expression data.
  • a sample used for predicting physiological changes in a living body was collected from a site where a physiological change occurred, that is, from a site where a disease appears if the physiological change occurred.
  • a biological tissue or a biological sample prepared from them is used (Non-patent Document 1).
  • the expression level of a biological group having an element that induces a certain physiological change is compared with the expression level of a biological group that does not have a powerful element. It is known to select a gene to be a marker gene (Non-patent Document 3).
  • Non-Patent Document 1 Biochemical and Biophysical Research Communications 315, 1088-10 96 (2004)
  • Non-Patent Document 2 Nature 415, 530-526 (2002)
  • Non-Patent Document 3 New England Journal of Medicine 347, 1999-2009
  • an object of the present invention is to provide a technique for predicting a physiological change with high accuracy using a sample obtained by collecting a part force different from a part where a physiological change occurs in a living body.
  • a method for predicting physiological changes in a living body includes a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change. Detecting a gene expression level of a plurality of genes by means of a biological tissue from which a site force different from the measurement target site is also collected, and among the genes, the individual causing the physiological change and the physiological Selecting a gene that has a statistical difference in expression level as an marker gene group from an individual that does not cause a change, and an individual that produces the physiological change and the physiological change that does not occur Perform multivariate analysis on the expression level of marker gene groups with individuals !, and based on the expression level of marker gene groups!
  • Generate discrimination criteria to determine the presence or absence of the onset And the individual to be predicted The step of detecting the gene expression level of at least a gene including a marker gene group in a biological tissue collected from a site force different from the target site of the physiological change prediction And a step of predicting the presence or absence of a physiological change in the target region by applying the determination criterion to the gene expression level of a gene including the marker gene group of the prediction target individual.
  • the presence or absence of a physiological change with high accuracy can be predicted based on the biological tissue collected from a site force different from the site causing the physiological change.
  • the method for predicting physiological changes in a living body includes at least a biological tissue obtained by collecting a site force different from the target site of the physiological change prediction for an individual to be predicted. Apply judgment criteria to the step of detecting gene expression level for genes including marker gene groups and the gene expression level for genes including marker gene groups of the target individuals to predict the presence or absence of physiological changes at the target site Comprising the steps of
  • the marker gene group consists of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue collected from a portion different from the target site for the physiological change prediction.
  • the gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between the individual that produces the physiological change and the individual that does not produce the physiological change.
  • the selection criterion is that a multivariate analysis is performed on the expression level of the marker gene group between the individual causing the physiological change and the physiological change! /, And the individual. Based on the expression level of the group, it is a discriminant criterion created based on the expression level.
  • a method for generating a discrimination criterion used for predicting physiological changes of a living body according to the present invention is intended for a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change.
  • the method for selecting a marker gene according to the present invention comprises a plurality of individuals that cause physiological changes and a plurality of individuals that do not cause the physiological changes. Different body force, the step of detecting the gene expression level of a plurality of genes using the collected biological tissue, and the individual of the gene that causes the physiological change and the individual that does not cause the physiological change. And a step of selecting genes to be expressed as marker gene groups when the difference in expression level is statistically observed.
  • a marker gene with high accuracy can be selected based on the biological tissue collected from a site force different from the site causing the physiological change.
  • the method for predicting physiological changes in a living body according to the present invention is characterized in that the detection of the gene expression level is performed using a gene expression detection element.
  • the determination criterion creating program includes a plurality of individuals that cause a physiological change and a plurality of individuals that do not cause the physiological change, And a step of detecting gene expression levels of a plurality of genes by using different body forces, and a step of using the detected gene expression levels for each individual as basic data in association with the presence or absence of the physiological change.
  • a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is a marker gene.
  • a criterion for predicting the presence or absence of a physiological change with high accuracy can be created based on a biological tissue collected from a part force different from a part that causes a physiological change.
  • the marker gene selection program according to the present invention is intended for a plurality of individuals that cause physiological changes and a plurality of individuals that do not cause the physiological changes. Detecting a gene expression level of a plurality of genes by means of a biological tissue from which different site forces have been collected, and, among genes, between an individual that causes the physiological change and an individual that does not cause the physiological change. In this case, the computer is caused to execute a step of statistically seeing the difference in the expression level and selecting the gene to be expressed as a marker gene group.
  • a marker gene with high accuracy can be selected based on a biological tissue collected from a site force different from the site causing the physiological change.
  • the prediction program according to the present invention relates to a gene including at least a marker gene group for a biological tissue obtained by collecting a site force different from the target site of the physiological change prediction for an individual to be predicted.
  • the computer executes the steps of detecting the gene expression level and applying a judgment criterion to the gene expression level for the gene including the marker gene group of the individual to be predicted, and predicting the presence or absence of a physiological change in the target region.
  • the marker gene group is composed of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue in which a force different from the site where the physiological change occurs is collected.
  • the gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between an individual that produces the physiological change and an individual that does not produce the physiological change among the genes.
  • the criterion is based on the expression level of the marker gene group by performing a multivariate analysis on the expression level of the marker gene group between the individual causing the physiological change and the physiological change not occurring! It is characterized by the discriminant criteria created by the above.
  • the program according to the present invention uses a gene expression detection element to detect the gene expression level.
  • the gene expression detection element comprises a substrate and a probe formed on the substrate in order to detect each gene expression level for the marker gene group, and the marker gene group comprises: Targeting a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, a part of the tissue that is different from the target site for the physiological change prediction is collected from a plurality of genes. A gene expression level is detected, and a gene in which a difference in the expression level is statistically found between the individual causing the physiological change and the individual not causing the physiological change is selected from the genes described above. It is characterized by being.
  • a prediction device is a prediction device for predicting physiological changes in a living body
  • the conversion unit that converts the gene expression level captured by the probe into an electric signal, and the expression level of each gene
  • a prediction unit that receives a corresponding electrical signal and predicts the presence or absence of a physiological change based on a criterion
  • the marker gene group consists of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue collected from a portion different from the target site for the physiological change prediction.
  • the gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between the individual that produces the physiological change and the individual that does not produce the physiological change.
  • a gene selected, and the criterion is that an individual who develops the physiological change and a person who does not develop the physiological change.
  • V multivariate analysis based on the expression level of the marker gene group between individuals, and the distinction criteria created based on the expression level of the marker gene group! /
  • the program according to the present invention is characterized in that the gene expression detection element is a DNA chip or a DNA array.
  • the prediction device is characterized in that the gene expression level of a gene for a living tissue is detected based on the living tissue or a biological sample prepared therefrom.
  • the prediction device according to the present invention is characterized in that the living tissue is skin tissue or mucosal tissue.
  • the prediction device according to the present invention is characterized in that the biological sample is a fibroblast.
  • the prediction device according to the present invention is characterized in that the biological sample is fibroblast-derived RNA.
  • the prediction device according to the present invention is characterized in that the onset site is the brain.
  • the prediction device according to the present invention is characterized in that the physiological change is the onset of a disease.
  • the prediction device according to the present invention is characterized in that the disease is a central nervous disease.
  • the prediction device is characterized in that the central nervous system disease is dementia, Parkinson's disease, amyotrophic lateral sclerosis, or prion disease (Kreuzfeld-Jakob disease). .
  • the prediction device according to the present invention is characterized in that the dementia is Alzheimer's disease or frontotemporal dementia.
  • the element that induces a physiological change is a Swedish mutation, Arctic Mutation and preserinin 1 gene It is characterized by being one or more elements selected for H136Y mutation.
  • the prediction apparatus according to the present invention is characterized in that the multivariate analysis is an analysis method including principal component analysis and linear discriminant analysis.
  • the predicting apparatus according to the present invention is characterized in that the difference in the expression level is observed! Selection of the gene to be displayed is performed based on the information amount standard.
  • the prediction device is characterized in that the information criterion is an Allen cross-validation criterion.
  • the detection of the gene expression level is performed by detecting a change in optical characteristics due to the labeled gene bound to the probe of the gene expression detection element by hybridization. It is characterized by things.
  • the detection of the gene expression level is performed by detecting a change in electrical characteristics due to the gene bound to the probe of the gene expression detection element by hybridization. It is characterized by that.
  • a prediction apparatus is a gene expression detection element used for predicting physiological changes in a living body
  • the marker gene group is:
  • Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change a different site force from the target site for the physiological change prediction.
  • a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
  • the probe for each marker gene is
  • Principal component analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and corresponds to each gene according to the coefficient of the synthetic variable related to the principal component.
  • the detection sensitivity of the probe to be set is set. It is said.
  • a biological physiological change prediction system includes a server device and a terminal device,
  • the terminal device is configured to detect a gene expression level detected for a gene including at least a marker gene group in a biological tissue collected from a site force different from the target site of the physiological change prediction for an individual to be predicted.
  • Transmission means for transmitting the information indicating the reception, reception means for receiving the prediction result data from the server device, and output means for outputting the received prediction result data,
  • a server device that receives information indicating the gene expression level from the terminal device; a prediction unit that applies a determination criterion to the gene expression level and predicts the presence or absence of a physiological change in the target site; Transmission means for transmitting the prediction result data by the prediction means to the terminal device,
  • the marker gene group is:
  • Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change a different site force from the target site for the physiological change prediction.
  • a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
  • the criterion is
  • a multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and V is created based on the expression level of the marker gene group. It is characterized by being a discriminant criterion!
  • the server device provides at least a marker for a biological tissue in which a part force different from the physiological change prediction target part is collected for an individual to be predicted.
  • a receiving means for receiving information indicating a gene expression level detected for a gene including one gene group from the terminal device, and a prediction for predicting the presence or absence of a physiological change in the target site by applying a criterion to the gene expression level Means, and transmission means for transmitting the prediction result data by the prediction means to the terminal device,
  • the marker gene group is:
  • Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change a different site force from the target site for the physiological change prediction.
  • a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
  • the criterion is
  • a multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and V is created based on the expression level of the marker gene group. It is characterized by being a discriminant criterion!
  • the presence or absence of a physiological change with high accuracy can be predicted based on the collected body tissue from a site force different from the site causing the physiological change.
  • a terminal device includes at least a marker gene group for a biological tissue in which a site force different from the target site for the physiological change prediction is collected for an individual to be predicted
  • a transmission means for transmitting information indicating the gene expression level detected for the gene
  • a reception means for receiving the prediction result data of the server device power
  • an output means for outputting the received prediction result data
  • the marker gene group is:
  • Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change a different site force from the target site for the physiological change prediction.
  • the genes that are found to be statistically different in the expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected. It is characterized by that.
  • the marker gene is specified by an accession number of the gene information database "Genbank” of the National Center for Biotechnology Information (NCBI), It is characterized by at least the following 51 genes included! /, Ru:
  • physiological change of a living body refers to an observable change that occurs in a part of an organism such as a cell, tissue, organ, or the entire individual. For example, it is a concept that includes shape, color, size, temperature, energy consumption, substance production and changes in movement and behavior, and the onset of disease.
  • the “element that expresses physiological change” includes any material or non-material matter that can induce the physiological change of the living body. Specific examples include genes, environment (temperature, water temperature, humidity, osmotic pressure, sound, vibration, etc.), nutritional status, drug administration, stress, personality, personality, and preferences. Not. “Elements that induce physiological changes” are also synonymous.
  • Prediction of physiological change is the prediction of the presence or absence of a current physiological change in a site that is difficult to observe directly, not only when predicting a physiological change that will occur in the future. It is a concept that includes cases where
  • Bio physiological change prediction marker is a direct marker for predicting physiological changes in the living body. It is used directly or indirectly. This includes genes, nucleotides, polynucleotides or proteins, polypeptides, and polynucleotides capable of specifically recognizing and binding to them whose expression varies in the body in relation to physiological changes in the body. Or an antibody is included. Based on the above properties, these nucleotides, polynucleotides and antibodies are used as probes for detecting the above-described genes and proteins expressed in vivo, and nucleotides and polynucleotides are expressed in vivo. As a primer for amplifying the protein, the protein can be effectively used for screening a substance to be bound. “Physiological change prediction marker”, “prediction marker”, and “marker” are also synonymous.
  • Gene includes genetic information represented by a base sequence such as RNA or DNA. Also included are orthologous genes that are conserved among species such as humans, mice, and rats. A gene may function as RNA or DNA in addition to those that encode proteins. A gene generally encodes a protein according to its base sequence, but a protein having a biological function equivalent to the protein (for example, a homologue (such as a homologue splice variant), a mutant or a derivative). May be used. For example, a protein that encodes a protein whose base sequence is slightly different from the protein indicated by the base sequence based on genetic information, and whose base sequence hybridizes with a complementary sequence of the base sequence based on the genetic information. May be.
  • DNA is a concept that includes each single-stranded DNA such as a sense strand and an antisense strand that constitutes a double-stranded DNA alone.
  • DNA includes not only double-stranded DNA containing human genomic DNA, but also single-stranded DNA (positive strand) containing cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof. This is a concept that includes deviations.
  • DNA is a concept that includes functional regions such as expression control region, coding region, exon, intron, and so on. It is also a concept that includes cDNA, genomic DNA, synthetic DNA, and so on.
  • RNA is a concept including single-stranded RNA having a complementary sequence to single-stranded RNA and double-difference RNA composed thereof.
  • TotalRNA, mRNA, rR It is a concept that includes NA.
  • Gene expression detection element refers to an element that detects the presence or absence or expression level of gene expression, and includes an element that electrically detects the expression level in addition to the one that optically detects the expression level. This refers to the presence / absence of expression and the expression level converted into physical quantities.
  • This concept includes DNA chips and DNA arrays, including those in which probe DNA is placed on the glass surface, plastic wells, side and bottom surfaces of tubes, and the surface of microbeads.
  • the “DNA chip” and “DNA array” have a structure in which probe DNA is arranged on a substrate, and measure the expression of a plurality of genes by hybridization. This includes not only optically measuring the expression level but also outputting the expression level electrically.
  • GeneChip (trademark) manufactured by Affymetritas can be used as the “DNA chip”.
  • CodeLink Expression Bioarray (trademark) of Amersham Biosciences can be used.
  • DNA arrays include not only DNA microarrays but also DNA macroarrays.
  • “Expression level” is a concept that includes a value calculated by a predetermined calculation or a statistical technique, in addition to a value obtained by directly measuring the expression level of a gene.
  • “gene expression level”, “expression signal”, “gene expression signal”, “expression signal value”, “gene expression signal value”, “gene expression data”, “expression data”, etc. It is synonymous to indicate the value to be reflected.
  • Gene expression refers to an aspect of gene expression in a living body expressed by the expression level of a gene, and is expressed by the expression level of one gene or the expression level of a plurality of genes. Any of the cases are included. “Expression” is also synonymous with the expression of gene expression in a living body.
  • Detecting the expression level of a gene in a living tissue means detecting the expression level using a biological sample prepared based on the living tissue, which is not only when detecting the expression level using the living tissue itself. It is a concept that includes cases.
  • Bio sample refers to a sample prepared from collected tissues, such as cells, fibroblasts, erythrocytes, leukocytes, lymphocytes, nucleic acids, fibroblast-derived RNA, and the like.
  • Program refers to a source consisting of only a program that can be directly executed by a CPU.
  • the concept includes a format program, a compressed program, an encrypted program, and the like.
  • FIG. 1 and 2 show the flow of processing in a method for predicting physiological changes in a living body according to an embodiment of the present invention.
  • Figure 1 shows the generation of discrimination criteria
  • Fig. 2 shows the prediction using the discrimination criteria.
  • the biological group that produces the physiological change (referred to as the first biological group) and the! / ⁇ biological group (referred to as the second biological group). ) (Step Pl).
  • a biological tissue is collected from each individual belonging to the first biological group and the second biological group (step P2).
  • a biological tissue at a site different from the target site for which a physiological change is predicted is collected. For example, when predicting physiological changes in the brain, tissue such as human upper arm skin is collected.
  • a sample is prepared on the basis of the collected biological tissue for each individual force (step P3).
  • fibroblasts are prepared from the collected biological tissue.
  • step P4 hybridization using a DNA chip is performed using this sample (step P4).
  • mRNA is removed from a sample, and cDNA (complementary DNA) of this mRNA is replicated.
  • This cDNA is fluorescently treated.
  • an aqueous solution containing the fluorescently treated cDNA is dropped onto the probe of the DNA chip to perform hybridization (duplex formation reaction).
  • the DNA chip is provided with a large number of probe regions in the vertical and horizontal directions, and a large number of DNA probes are provided in each probe region.
  • the DNA probe has a different base sequence for each probe region.
  • the fluorescence-treated cDNA interacts with a DNA probe having a corresponding base sequence. Therefore, the expression level of mRNA can be detected by measuring the color density of each probe region.
  • the hybridized DNA chip is imaged with a scanner to obtain an image having a color density corresponding to the expression level of mRNA.
  • Sarako based on this image by image analysis software Next, obtain concentration data for each probe region as gene expression data (step P5).
  • a marker gene group Based on the gene expression data for each individual obtained as described above, by comparing the gene expression data of the first biological group and the second biological group, the first biological group and the second biological group Then select genes with significantly different expression data (Step P6).
  • the gene group selected in this way is defined as a marker gene group.
  • a marker gene group can be selected by using an information criterion such as a cross-reduction criterion.
  • multivariate analysis is performed on the gene expression data of the marker gene group in each individual to generate a reference for discriminating between the first biological group and the second biological group.
  • a principal component analysis can be performed to obtain a discrimination criterion.
  • a discrimination criterion used for predicting physiological changes can be generated.
  • FIG. 2 shows the prediction of physiological changes.
  • a biological tissue is collected from the individual that is the prediction target.
  • a biological tissue of a site different from the target site for which physiological change is predicted is collected.
  • a biological sample is prepared from the collected biological tissue (step P12). It is preferable that the biological sample is the same type of biological sample from which the tissue strength of the same part as that used in creating the discrimination criterion is also collected.
  • step P13 hybridization using a DNA chip is performed (step P13). For example, mRNA is removed from a sample, and cDNA (complementary DNA) of this mRNA is replicated. This cDNA is fluorescently treated. Furthermore, an aqueous solution containing fluorescently treated cDNA is dropped onto the probe of the DNA chip, and hybridization (duplex formation reaction) is performed.
  • the DNA chip used here can be the same DNA chip used to generate the discrimination criteria! /, But a dedicated DNA chip with only probes corresponding to the marker gene group. Is preferred.
  • the hybridized DNA chip is imaged with a scanner to obtain an image with a color density corresponding to the expression level of mRNA. Further, based on this image, image analysis software acquires concentration data for each probe region as gene expression data (step P14). [0093] Subsequently, a discrimination criterion is applied to the acquired gene expression data to predict the presence or absence of a physiological change (Step P15), and obtain a prediction result (Step P16). As described above, physiological changes in the living body can be predicted.
  • the marker gene selection method, the discrimination criterion creation method, and the prediction method based on the discrimination criterion can be performed independently.
  • the discrimination criteria shown in FIG. 1 can be created according to the present invention, and the discrimination criteria can be used for other prediction methods or other than the prediction method.
  • the method for selecting a marker gene indicated by steps P1 to P6 in Fig. 1 is performed, and based on the selected marker gene, a discrimination criterion is generated by a method other than the present invention, or the selected marker gene is selected. Can be used for purposes other than generating discrimination criteria.
  • the generation of discrimination criteria shown in Fig. 1 and the prediction of physiological changes shown in Fig. 2 can be performed without using a computer. However, considering a large amount of data processing, it is preferable to implement as a device as shown below.
  • Figure 3 shows a functional block diagram of the discrimination criterion generator.
  • the expression level detection means 22 can obtain expression level data for each gene from the DNA chips Dl and D2 ⁇ ⁇ Dn that have been hybridized. Furthermore, the basic data is generated by the basic data generation means 24 by combining the expression level data with the physiological change presence / absence data of each individual.
  • the marker selection means 26 selects the marker gene by comparing the expression data of the first biological group and the second biological group.
  • the discrimination criterion generation means 28 performs multivariate analysis based on the expression level data of the marker gene group, and calculates a criterion for discriminating between the first biological group and the second biological group. As a result, the discrimination criterion 30 is recorded.
  • FIG. 4 shows the hardware configuration when the discrimination criterion generator is realized by a computer.
  • CPU 2 Connected to CPU 2 are display 4, scanner 6, memory 8, CD-ROM drive 10, and hard disk 12.
  • the scanner 6 reads the probe area of the DNA chip on which hybridization has been performed as an image.
  • the scanner 6 is connected to the CPU 2 and can directly capture data.
  • the image data read by the scanner 6 may be recorded on a portable recording medium (CD-RW, etc.) and read from the CD-ROM drive 10!
  • Memory 8 is used as a work area of CPU2.
  • the operating disk 16 and the discriminant reference generation program 18 are recorded on the memory disk 12. These programs are recorded on the CD-ROM 14 and installed on the hard disk 12 via the CD-ROM drive 10.
  • the discrimination criterion generation program 18 performs its function in cooperation with the operating system 16. Note that the discrimination criterion generation program 18 may be a program that functions alone.
  • 5 and 6 show flowcharts of the discrimination criterion generation program.
  • the physiological change of the living body to be predicted will be described as Alzheimer's disease.
  • fibroblasts were isolated and cultured by the method described in Neuroscience Letters, 220 9-12 (199 6), and 3 to 10 million fibroblasts per sample. This was used as a biological sample. Sarako, this fibroblastic force also extracted TotalRNA. For extraction, Rneasy Mini kit (Qiagen, Valencia, CA) can be used.
  • step S1 the CPU 2 reads an image of a DNA chip that is set in the scanner 6 and hybridized with a biological sample of the first individual. This picture The image has a fluorescence concentration corresponding to the expression level of each gene.
  • CPU2 generates expression level data for each gene based on the fluorescence concentration of each probe region of the image. Thereby, the expression level data can be acquired (step S2). Also for these, the method described in JP-A-2003-169867 can be used. In addition, the expression level data acquisition part can be realized by using analysis software Microarray Suite version 5.0 of Affymetritas.
  • the CPU 2 acquires data on whether or not Alzheimer's disease develops for the individual (step S3).
  • an individual who has the ability to develop Alzheimer's disease or an individual who is certain to develop it in the future was treated as an individual who “develops Alzheimer's disease”.
  • This may be input from a keyboard or the like (not shown), or may be acquired from data recorded in advance on the hard disk 12 for each individual. In the latter case, it is advisable to record the data by attaching an ID to each individual so that the corresponding data can be obtained by inputting the ID when reading the image of the DNA chip.
  • the target individual is obtained, and the presence or absence of Arnno and Imah disease and the expression level for each gene are obtained as basic data and recorded on the hard disk 12 (step S4).
  • CPU 2 determines whether or not the above processing has been performed for all individuals (step S5). If there is an unprocessed individual, the above steps S1 to S5 are repeated.
  • Figure 7 shows a part of the basic data recorded on the hard disk 12.
  • the top column is the individual ID.
  • the DNA chip (HG-U133A) used in this embodiment has 22,283 types of probes. Therefore, in this embodiment, the number n of genes recorded on the hard disk 12 is 22,283.
  • CPU 2 selects a marker gene based on the basic data recorded on hard disk 12.
  • CPU2 excludes genes that are not expressed and genes with low expression levels (signal less than 44).
  • the first gene is set as a target gene (step S6), and the maximum expression level and the minimum expression level are extracted from all individuals for the target gene (step S7). Based on the following formula, an intermediate value between the maximum value and the minimum value is calculated.
  • Step S8 using this intermediate value as a boundary, it is divided into a large expression level group and a low expression level group (step S8). Further, based on the presence or absence of Alzheimer's disease in each individual, it is divided into two groups (Step S9).
  • the expression level data of the target gene is divided into four groups as shown in FIG. Regions 1 and 1 are “onset” and “high expression” regions, regions 1 and 2 are “onset” and “low expression”, regions 2 and 1 are “onset” and “high expression” Regions 2 and 2 are “no onset” and “small expression” regions.
  • the CPU 2 calculates the degree that the expression level of this gene is not related to "onset” and "no onset” (independent model) and the degree of relation (dependent model).
  • the maximum log likelihood Lde of the dependent model and the maximum log likelihood Lin of the independent model are calculated according to the following equation based on Allen's cross validation (CV) standard (step S10).
  • CV Allen's cross validation
  • statistical analysis software “Visual Mining Studio ver. 3.0” (Mathematical Systems Inc.) can be used.
  • n is the number of samples (total number of individuals), and n (i, j) is the number of samples (individuals) that fall within regions i and j in FIG.
  • CPU2 records the calculated CV value in the hard disk 12 in association with the gene.
  • CPU 2 determines whether or not CV values have been calculated for all genes constituting the probe of the DNA chip (step S12). If there is an uncalculated gene, the next gene is the target gene (step S14), and step S7 and subsequent steps are repeated.
  • a predetermined number of genes having a large CV value are selected as one gene (step S13).
  • the top 200 genes having a CV value of 3 or more were selected as marker genes.
  • the marker gene may be selected by combining the CV value and the number as in this embodiment, but may be selected only by the CV value or by the number!
  • the CPU 2 changes the support vector machine (SVM) when the CV value as a threshold is changed.
  • SVM support vector machine
  • the CV value that maximizes the correct answer rate of LOOCV may be selected. For example, one sample (one individual) is removed from all samples (all individuals), and the remaining sample (individual) is subjected to discriminant analysis by SVM using the expression level of the selected marker gene. The discriminant plane between the first group and the second group having the presence or absence of onset is obtained. Remove! Based on the expression level of only one sample, it was projected onto the discriminant space to determine whether or not discrimination was performed correctly. Repeat this procedure for all samples with different samples to be removed. Thereby, the correct answer rate is calculated.
  • the LOOCV cross-validation part by SVM is the statistical analysis software “R” and “R” statistical analysis package “el071” (http: ⁇ www.cran.us.r-project.org/) This can be done using.
  • CPU 2 normalizes the expression levels ⁇ 1, ⁇ 2... ⁇ ⁇ of each marker gene based on the following equation (step S 15).
  • ⁇ ⁇ , ⁇ ⁇ ... ⁇ ⁇ is the average value of all the markers for the expression levels ⁇ 1, ⁇ 2.
  • ⁇ 1, ⁇ 2 ⁇ ⁇ ⁇ are standard deviations in all individuals with respect to the expression levels ⁇ 1, ⁇ 2 ⁇ ⁇ ⁇ of each marker gene. In this embodiment, ⁇ is 200.
  • CPU2 calculates the standardized expression level Dl of each marker gene calculated above, ⁇ 2 ⁇ ⁇ ⁇
  • a principal component analysis is performed to calculate a first principal component X, a second principal component Y, and a fourth principal component ⁇ (step S16).
  • Pli is the eigenvector for the i-th marker gene of the first principal component.
  • P2i is the eigenvector for the i-th marker gene of the second principal component.
  • P4i is the eigenvector for the i-th marker gene of the fourth principal component.
  • CPU2 is the first, second, and fourth principal components of the first population that develops Alzheimer, and the first, second, and fourth major components of the second population that does not develop the Arnno
  • a discriminant for discriminating between the first group and the second group is calculated by linear discriminant analysis. Specifically, a, b, c, and d in the following formula are calculated.
  • the value of the above formula is predicted to develop Alzheimer's disease if it is greater than A force ⁇ , and less than 0 If so, it can be predicted that Arno and Imah disease will not develop.
  • two or less forces using three main components, or four or more main components may be used.
  • the first, second, and fourth principal components are used, but this is more than the case where the first, second, and third principal components are used.
  • the prediction accuracy is higher.
  • the prediction accuracy is often higher when the first, second, and third principal components are used.
  • Figure 11 shows the functional block diagram of the prediction device.
  • the above discriminant is recorded as a discrimination criterion.
  • the expression level detection means 32 obtains the expression level data for each gene of the individual to be predicted from the DNA chip D that has been hybridized.
  • a DNA chip having only a probe corresponding to a marker gene is used, but a DNA chip having other gene probes may also be used.
  • the predicting means 34 calculates a numerical value A based on the recorded discriminant, and predicts that if it is greater than 0, it will develop Alzheimer's disease. Moreover, if it is smaller than 0, it is predicted that Alcno and Imah's disease will not occur.
  • the output means 36 outputs this prediction result to a display, a printer or the like.
  • Figure 12 shows the hardware configuration when the prediction device is implemented by a computer.
  • a display 4, a scanner 6, a memory 8, a CD-ROM drive 10, and a hard disk 12 are connected to the CPU 2.
  • the scanner 6 reads the probe area of the DNA chip that has been hybridized as an image.
  • the scanner 6 is connected to the CPU 2 and can directly capture data.
  • the image data read by the scanner 6 may be recorded on a portable recording medium (CD-RW, etc.) and read from the CD-ROM drive 10.
  • the memory 8 is used as a work area of the CPU2.
  • the operating disk 16, the prediction program 17, and the discriminant 19 are recorded on the memory disk 12.
  • Discriminant formula 19 is described as part of the program of prediction program 17!
  • These programs are recorded on the CD-ROM 14 and installed on the hard disk 12 via the CD-ROM drive 10.
  • the forecast program 17 performs its function in cooperation with the operating system 16.
  • the forecast program 17 is a program that works alone.
  • FIG. 13 shows a flowchart of the prediction program.
  • a physiological change of a living body to be predicted will be described as Arno and Imah's disease.
  • step S51 the CPU 2 reads an image of a DNA chip that is set in the scanner 6 and hybridized with a biological sample of an individual to be predicted. This image has a fluorescence concentration corresponding to the expression level of each marker gene. Next, the CPU 2 generates expression level data for each marker gene based on the fluorescence concentration of each probe region of the image. Thereby, expression level data can be acquired (step S52).
  • CPU 2 reads the discriminant (recorded above in Equation 6) recorded on hard disk 12, and calculates numerical value A based on the expression level of each marker gene (step S53). If the calculated numerical value A is smaller than 0, the prediction target individual predicts that Alzheimer's disease will not occur, and records the prediction result on the node disk 12 (step S55). If the calculated numerical value A force is greater than or equal to the predicted value, the prediction target individual predicts that the Arnotnoima disease will develop, and the prediction result is recorded on the node disk 12 (step S56).
  • CPU 2 outputs numerical value A and the prediction result from display 4 or a printer (not shown) (step S 57).
  • Figure 15 shows the configuration of the prediction system performed via the network.
  • Terminal device Comb 50
  • server device 54 computer
  • the hardware configuration of the server device 54 is shown in FIG.
  • the force scanner 6 that has almost the same configuration as the prediction device in FIG. 12 is not provided.
  • a communication circuit 7 for communicating with the terminal device 50 via the Internet 52 is provided.
  • FIG. A communication circuit 7 is provided for communicating with the server device 54 via the force Internet 52, which has almost the same configuration as the prediction device of FIG.
  • a data acquisition program 15 is recorded on the hard disk 12.
  • the DNA chip D hybridized with respect to the biological sample of the individual to be predicted is read by the scanner of the terminal device 50.
  • the CPU 2 executes step S51 in FIG.
  • the CPU 2 transmits this image data to the server device 54 through the communication circuit 7.
  • CPU 2 of server device 54 executes steps S 52 to S 57 of FIG. 13 according to prediction program 17. That is, expression level data is acquired from this image data, and the presence or absence of onset is predicted.
  • step S57 the CPU 2 transmits the numerical value A and the prediction result to the terminal device 50.
  • the terminal device 50 receives this and displays it on the display 4.
  • the presence / absence of onset can be predicted without a prediction program on the terminal device 50 side.
  • the image data is transmitted to the server device 54.
  • the expression level data may be obtained by the terminal device 50 and transmitted to the server device 54.
  • a prediction device 40 in which a processing circuit 42 for prediction and a display device 44 for displaying a determination result are incorporated in a DNA chip can be constructed.
  • a probe region 46 a probe corresponding to the marker gene is provided.
  • Each probe emits an electrical signal when bound to a biological sample.
  • This electrical signal is amplified by a transistor or the like and output as an expression level signal.
  • This expression level signal is given to the processing circuit 42. Electronic See Analytical and Bioanalytical Chemistry, 377 (3) 521-527, 20 03, The Analyst, 130 (5), 687-693, 2005, etc. for details of the expression DNA chip.
  • the processing circuit 42 includes a CPU and a memory, and has a program for executing steps S52 and S57 in FIG.
  • a display 44 such as an LCD is connected to the CPU of the processing circuit 42, and the CPU displays the numerical value A on the display 44 in step S57! Note that the determination result may be displayed. If this DNA chip type prediction device is used, prediction can be performed easily.
  • the power using the CPU for the processing circuit 42 may be a hardware circuit that executes a discriminant operation as shown in Fig. 14B.
  • the expression level data ⁇ 2, 2... ⁇ ⁇ obtained by converting the expression level signal from each probe into digital data by an AZD converter (not shown) is given to the subtractor 62 via the multiplexer 60.
  • constant data ⁇ 1, ⁇ 2... ⁇ ⁇ (average value in the above equation 3) is also given to the subtractor 62 via the multiplexer 64.
  • Multiplexers 60 and 64 switch the expression data ⁇ ⁇ , 2 ⁇ ⁇ and constant data ⁇ 1, ⁇ 2 ⁇ ⁇ ⁇ by timing pulses ⁇ 1, ⁇ 2 ⁇ ⁇ Apply to subtractor 62. Therefore, the subtracter 62 sequentially includes the combination of the expression level data / ⁇ 1 and the constant data ⁇ 1 and the combination of the expression level data ⁇ 2 and the constant data ⁇ 2. A combination of ⁇ ⁇ is output. Therefore, the subtractor 62 sequentially subtracts the expression level data / ⁇ 1 from the constant data ⁇ 1, the expression level data from the constant data ⁇ 2; the operation to subtract ⁇ 2 ⁇ Expression from the constant data ⁇ ⁇ Performs subtraction of quantity data / ⁇ ⁇ . Then, the subtraction result is given to the multipliers 66, 68 and 70 in accordance with the timing rule.
  • the multiplier 66 sequentially performs an operation of multiplying this by P11Z ⁇ 1 and an operation of multiplying this by P12Z ⁇ 2 ⁇ Multiplication of ⁇ 1 ⁇ / ⁇ (see Equation 3 and Equation 4). See).
  • the output is given to the adder 72 according to the timing pulse.
  • the adder 72 cumulatively adds the calculation results sent sequentially. Accordingly, when the timing pulse advances from TP1 to ⁇ , the first principal component data X is output from the adder 72. Similarly, the adder 74 and adder 76 output the second principal component data ⁇ and the fourth principal component data ⁇ .
  • the first principal component data X is multiplied by a coefficient a (see Equation 5) by a multiplier 78 to obtain the second principal component data.
  • the data Y is multiplied by the coefficient b by the multiplier 80, and the fourth principal component data Z is multiplied by the coefficient c by the multiplier 82 and then added by the adder 84, respectively. Therefore, the numerical value A can be obtained from the adder 84.
  • the manufacturing cost can be suppressed relatively inexpensively as well as being able to perform prediction even in an environment without a computer or an expensive scanner.
  • the necessary main components such as a DNA chip for the first main component, a DNA chip for the second main component, and a DNA chip for the fourth main component are used. Install a compatible DNA chip!
  • Equation 8 standard data D′ l, D′ 2 ⁇ ⁇ “D′ n” is obtained by a conversion equation as shown in Equation 8 below.
  • FIG. 18 shows the probe region of the DNA chip for the first main component in this embodiment. As shown in the figure, the probe region is provided from l to n.
  • the sensitivity of the RNA probe in each probe region is not the same.
  • the sensitivity of the probe is adjusted according to the coefficients ⁇ ⁇ , Pli corresponding to the genes in the region.
  • the sensitivity is adjusted so that the fluorescence density corresponding to the amount multiplied by ⁇ ⁇ ⁇ ⁇ is detected.
  • This preparation can be performed by adjusting the number of probe RNA or probe DNA provided in each probe region. It is preferable to determine the number of probes by measuring the relationship between the fluorescence concentration and the number of probes in advance.
  • the DNA chip for the second main component and the DNA chip for the fourth main component are formed in the same manner.
  • Equation 9 The sigma addition in Equation 9 below is automatically performed.
  • Second principal component ' ⁇ / 3 ⁇ 4.
  • the sensor readings for the DNA chip for the first principal component, the DNA chip for the second principal component, and the DNA for the fourth principal component are each obtained.
  • Predictive judgment can be made by obtaining and recording, and applying the following Equation 10 to these manually (using a calculator or the like).
  • prediction can be easily performed without a computer or without an expensive scanner.
  • the ability to discriminate about Algno-Ima disease and other central nervous system diseases such as frontotemporal dementia, dementia, Parkinson's disease, amyotrophic lateral sclerosis and prion disease. It can also be applied to discrimination. Furthermore, it can also be used to predict diseases that develop in sites other than the brain.
  • the skin tissue is collected and / or beaten. However, it may be a tissue other than the skin tissue such as mucosal tissue or blood as long as it is a tissue other than the site where the disease occurs.
  • the cross-reduction criterion is used as the "comparison analysis”.
  • “comparison analysis” refers to an analysis method that compares the gene expression data of two groups and evaluates the difference in gene expression between the groups.
  • an analysis method that performs comparison based on the information criterion. For example, t-test, F-test,% 2 test, rank sum test, etc. If it is an analysis method that can evaluate the difference in gene expression between two groups by applying it to the data, it is not limited to these! ,.
  • the analysis method may be constituted by a plurality of analysis methods rather than only one analysis method.
  • the configuration of multiple analysis methods may be, for example, a parallel configuration in which the analysis results obtained by the multiple analysis methods are combined into a final analysis result!
  • a serial configuration may be used in which an analysis result obtained by one analysis method is used as a variable, and an analysis result obtained by applying another analysis method is used as a final analysis result.
  • the strength of the relationship between gene expression and factors that induce physiological changes obtained by comparative analysis is, for example, the ratio of p-values and statistics, or the mean, median, and variance of expression signals, although it may be expressed as a difference, etc., it is not limited to these as long as the difference in gene expression between groups can be evaluated by a continuous amount, a discrete amount, a series, or the like.
  • the living body is usually a living body group that can be classified into two groups, such as a living body group having an element that induces a physiological change and a living body group having no such element.
  • a living body group having an element that induces a physiological change and a living body group having no such element.
  • the difference in gene expression between individual groups is evaluated separately by comparative analysis, and gene expression and By evaluating the strength of the association with the physiological change, it is possible to select a biological physiological change prediction marker gene corresponding to the physiological change between each group.
  • a certain standard is set for the magnitude of the relationship between gene expression and an element that induces physiological changes in living organisms. Genes that match can be selected.
  • the criteria for the magnitude of the relationship between gene expression for selection of a marker for predicting the expression of physiological changes in living organisms and the factors that induce physiological changes, and the number of genes to be selected are not limited. It is possible to select and adjust as appropriate.
  • the "information criterion” is a criterion for evaluating the magnitude of association between a variable and an element that classifies the two groups, and an expression of an individual gene and an element that induces a physiological change. Used to evaluate the magnitude of the association.
  • the living organisms are classified into two groups: a group with a high expression level and a group with a low expression level, and a group with and without an element that induces physiological changes.
  • a 2-row by 2-column contingency table containing the number of organisms that meet each classification criterion is created.
  • the methods for classifying living organisms into two groups, a group with a high expression level and a group with a low expression level, are classified according to whether or not the average value is greater than the average value, and the second between the maximum value and the minimum value, etc. Examples include, but are not limited to, a method for classification by classification, a method using% 2 test, and the like.
  • the difference in the expression level between the group with and without the element that induces physiological change is due to the pattern power by which the organism is classified according to the above two criteria. Compare whether the statistical model (subordinate model) is assumed to have some relationship or the statistical model (independent model) if it has no relationship.
  • the genes that are more compatible with the subordinate model are more closely related to the factors that induce expression and physiological changes.
  • the comparison of the information criterion that represents the fitness to the dependent model and the information criterion that represents the fitness to the independent model can be made, for example, by taking a ratio or difference.
  • the present invention is not limited to these as long as it can be evaluated by the amount of diffusion or series.
  • the information amount criterion Akaike's information amount criterion, Bayesian information amount criterion, Minimum Description Length (MDL) criterion, or Allen's cross-reduction criterion, etc. may be mentioned.
  • MDL Minimum Description Length
  • Allen's cross-reduction criterion etc.
  • Allen's Allen's Cross Validation Standard is mentioned.
  • Multivariate analysis in the above embodiment is a general term for statistical analysis methods that simultaneously analyze a plurality of variables, and refers to an analysis method that simultaneously analyzes expression data of a plurality of genes.
  • Multivariate analysis includes analysis methods such as principal component analysis, factor analysis, self-organizing map, cluster analysis, discriminant analysis, multiple regression analysis, and canonical correlation analysis. Any analysis technique can be used as long as it can discriminate gene expression between the two groups by applying to the above expression data.
  • the analysis method described above may be constituted by a plurality of analysis methods, not just those constituted by one analysis method.
  • the configuration of multiple analysis methods may be, for example, a parallel configuration in which each analysis result obtained by multiple analysis methods is combined into a final analysis result. It may be a serial configuration in which an analysis result obtained by one analysis method is a variable, and an analysis result obtained by applying another analysis method is a final analysis result.
  • the criteria for discriminating the two groups determined by multivariate analysis are the relational expression representing the characteristics of one group, the relational expression representing the characteristics of the other group, and the! Forces that may be obtained as points, curves, straight lines, curved surfaces, planes, hyperplanes, etc. that represent the boundary between one group and the other group. If it is possible to project individual organisms in space using their gene expression data, it is limited to these. It is not a thing.
  • a living body to which the prediction method of the present invention is applied is usually a living body that can be classified into two groups, a living body group having an element that expresses a certain physiological change and a living body group having no such element.
  • the gene expression between the individual groups is determined separately by multivariate analysis, and each group It is possible to obtain discrimination criteria corresponding to physiological changes between groups by defining discrimination criteria between living body groups that have such elements and living body groups that do not have such elements. It is.
  • Principal component analysis in the above embodiment is an analysis method for characterizing the relationship between individual samples using a principal component that is a new variable synthesized from a plurality of variable parameters. It is used to obtain a variable that can more clearly discriminate between a biological group having an element that induces a physical change and a biological group having no such element.
  • the "linear discriminant method" in the above embodiment is an analysis method for obtaining a boundary between two groups of samples using a plurality of variables, and discriminates whether or not a biological change will occur in the future. It is used to define a boundary between a living body group having an element that induces the physiological change as a reference and a living body group having no such element.
  • linear discrimination is performed on the principal components selected as variables for discriminating between the two groups obtained by the principal component analysis described above. Applying the method, it is possible to obtain points, straight lines, planes, or hyperplanes that serve as criteria for distinguishing the two groups.
  • force and other information criterion using Allen's cross-validation criterion may be used.
  • the Akaike information criterion, Bayesian information criterion, and Minimum Description Length (MDL) criterion may be used.
  • the information criterion is a criterion for evaluating the magnitude of the relationship between the variable and the elements that classify the two groups.
  • a comparative analysis other than the information amount criterion may be used.
  • an analysis method that can evaluate the difference in gene expression between two groups by applying to the expression data of one gene such as t test, F test, c 2 test, rank sum test, etc. can be used.
  • the analysis method may be a combination of a plurality of analysis methods.
  • fibroblasts were obtained from Neuroscience Letters, 220 9-12.
  • Aifymetrix oligonucleotide type DNA chip GeneChip for gene expression measurement HG-U133A Array was used. Specifically, preparation of cDNA from total RNA, preparation of labeled cRNA from the cDNA, fragmentation of labeled cRNA, fragmentation Hybridization of cRNA and DNA chip, fluorescent staining of hybridized cRNA, on DNA chip The method similar to the method described in Japanese Patent Application Laid-Open No. 2003-169687 was performed in the order of reading the fluorescence of the sample and measuring the gene expression level. Finally, the gene expression level was obtained by analyzing the fluorescence image of the HG-U133A Array using the analysis software Microarray Suite version 5.0.
  • FIG. 19a to 19f The expression level data thus obtained are shown in Figs. 19a to 19f.
  • the top column is the individual ID
  • the leftmost column is the gene ID.
  • the gene ID is indicated by a probe set number of Affymetritas.
  • Figures 19a to 19c show the gene expression levels of individuals with Alzheimer's disease (with etiological gene holder)
  • Figs. 19d to 19f show the gene expression levels of individuals with no Alzheimer's disease (with no etiological gene holder). Amount.
  • 22,238 kinds of genes have only the marker gene data in the power diagram, and the other genes are omitted.
  • the CV standard of the dependent model and the CV standard of the independent model were calculated from the number of samples stored in each section of the contingency table based on the following formula.
  • CV standards were performed using commercially available statistical analysis software “Visual Minng Studio ver. 3.0” (mathematical system).
  • n is the number of samples
  • n (i, j) is the number of samples stored in the section of the i-th row and the j-th column.
  • n is the number of samples
  • n (i) is the number of samples in the i-th row
  • n (j) is the number of samples in the j-th column
  • Probe sets corresponding to the marker genes are shown in FIGS. 20a and 20b.
  • item A represents the probe set identification number, and information on the corresponding gene is available from Affymetritas (http://www.alfymetrix.com/index.afik).
  • item B represents the value of “CV standard for dependent model, CV standard for independent model”.
  • Leave-One-Out cross-validation was performed on the 200 genes described above using a support vector machine (SVM). Specifically, remove one from 30 samples, and perform the discriminant analysis by SVM using the value of the expression signal of 200 probe sets for the remaining 29 samples! Above, we obtained a discriminant surface between the group with and without the familial Alzheimer's disease etiology gene (see Fig. 21). Then, only one sample was projected onto the discriminant space based on the value of the expression signal, and it was verified whether the presence or absence of the gene causing the familial Alzheimer's disease was correctly discriminated.
  • SVM support vector machine
  • an onset prediction formula serving as a criterion for determining whether or not to develop Alzheimer's disease in the future was set.
  • Equation 3 X, Y, and ⁇ ⁇ are expressed by Equation 3 and Equation 4, respectively.
  • ⁇ is 200.
  • Second principal component y >> ⁇ 2 / Di
  • ⁇ represents the value of the expression signal of each probe set. Also ⁇
  • li, ⁇ , and i 2i and P are elements of the eigenvectors of the individual probe sets that make up the marker gene set
  • ⁇ and ⁇ represent the mean ⁇ and standard deviation ⁇ of the expression values of 30 samples for each probe set. Specifically, the values were as shown in FIGS. 22a to 22d.
  • A> 0 Alzheimer's disease is predicted to develop, and A ⁇ 0 If present, it is predicted that Alzheimer's disease will not develop.
  • fibroblasts are obtained from the skin tissue provided by the subject, RNA is further extracted, and the expression level is measured by GeneChip HG-U133A Array.
  • Diagnosis is based on X, Y, and ⁇ values. If ⁇ ⁇ ⁇ ⁇ ⁇ 0, the subject will be near and will not develop Arno or Imah disease in the future! Predicted to develop if A> 0. Is done.
  • fibroblasts were isolated and cultured by the method described in Neuroscience Letters, 220 9-12 (1996), and 3 to 10 million fibroblasts per sample were obtained. Obtained.
  • the expression level of each gene was measured using total RNA extracted from fibroblasts.
  • an oligonucleotide type DNA chip GeneChip HG-U133A Array manufactured by Aifymetrix was used for the measurement of gene expression level. Specifically, preparation of cDNA from total RNA, preparation of labeled cRNA from the cDNA, fragmentation of labeled cRNA, fragmentation Hybridization of cRNA and DNA chip, fluorescent staining of hybridized cRNA, on DNA chip The method similar to the method described in Japanese Patent Application Laid-Open No. 2003-169687 was performed in the order of reading the fluorescence of the sample and measuring the gene expression level. Finally, the gene expression level was obtained by analyzing the fluorescence image of the HG-U133A Array using the analysis software Microarray Suite version 5.0.
  • a 2X2 contingency table shown in Fig. 8 was created based on the presence and absence of familial Alzheimer's disease etiology genes and their expression levels.
  • CV standard of the dependent model and the CV standard of the independent model were calculated from the number of samples contained in each section of the contingency table based on the following formula.
  • CV standards were performed using commercially available statistical analysis software “Visual Minng Studio ver. 3.0” (Mathematical System).
  • FIG. 23 shows a probe corresponding to the marker gene selected in this way.
  • item A represents the probe set identification number, and the corresponding gene information is available from Affymelitas (http: www.afiVmetrix.com/index.affx).
  • Item B represents the value of the “CV standard for the dependent model”.
  • the identification number of the probe set of Affymetritas corresponds to the accession number of the gene information database “Genbank” of the National Center for Biotechnology Information (NCBI) as shown in FIG. And!
  • a predictive expression formula was set as a criterion for determining whether or not it will develop Alzheimer's disease in the future.
  • the onset prediction formula was obtained by the following formula.
  • ⁇ represents the value of the expression signal of each probe set.
  • P is an element of the eigenvector of each probe set constituting the marker gene set
  • ⁇ i and ⁇ i represent the mean and standard deviation of the expression values of 30 samples for each probe set. Specifically, the values shown in Fig. 26 were obtained.
  • ⁇ is the skin tissue fibroblast power of the person who is the target of predicting the onset of Arno-Ima disease. Similar to 30 samples used to extract the marker gene set and to set the expression prediction formula. By inputting the expression signal value of each of 51 probe sets included in the marker gene set among the expression signal values obtained by hybridization with the DNA chip GeneChip HG-U133A Array by the method of X, Y, And the value of ⁇ is obtained, and further the value of ⁇ is obtained. [0271] If the value of A is A> 0, it is predicted that Alzheimer's disease will develop, and if A ⁇ 0, it is predicted that Alno and Imah's disease will not develop.
  • Example 4 3 ⁇ 4 30 woven sample donors in Example 4 contributed dermatofibrofibroma cells from 18 people, and 3 ⁇ 4 in Example 4 , the method of neck P, was used to introduce fibrosis from the dermis weed.
  • the RNA was extracted and the expression level was measured by GeneChiD HG-U133A Arrav.
  • the marker gene set shown in Example 4 51 Each expression signal value of the probe set was used to predict the onset marker gene Expression signal value of the set and individual vector elements of the individual probe sets constituting the marker gene set shown in Fig. 26 of Example 5 From the average expression values and standard deviations of the tissue sample donors in Example 4 in the past, according to the onset prediction formula (Formulas 11 and 12), if A ⁇ 0, the etiology of familial Arno-Ima disease It was predicted that the gene would be retained, and if A> 0, it was predicted to retain the etiological gene of familial Alzheimer's disease.
  • Figure 27 shows the prediction results.
  • fibroblasts are obtained from the skin tissue provided by the subject, RNA is further extracted, and the expression level is measured by GeneChip HG-U133A Array.
  • Example 4 51 Each expression signal value of the probe set was used to predict the onset marker gene Expression signal value of the set and individual vector elements of the individual probe sets constituting the marker gene set shown in Fig. 26 of Example 5 Tissue sample provider in Example 4
  • a method for predicting physiological changes in a living body based on the gene expression data using a sample collected from a site force different from the expression site, and effectively used for predicting physiological changes in the living body And a method for selecting a marker gene for predicting physiological changes in living organisms.
  • FIG. 1 is a diagram showing a flow of discrimination criterion creation processing according to an embodiment of the present invention.
  • FIG. 2 is a diagram showing a flow of a physiological change prediction process according to an embodiment of the present invention.
  • FIG. 3 is a functional block diagram of a discrimination criterion creating apparatus according to an embodiment of the present invention.
  • FIG. 4 This is a hardware configuration when the device of FIG. 3 is realized using a CPU.
  • FIG. 5 is a flowchart of a determination criterion generation program.
  • FIG. 6 is a flowchart of a judgment criterion generation program.
  • FIG. 7 is a diagram for showing the data structure of recorded expression data.
  • FIG. 9 is a diagram showing a data structure of CV values recorded for each gene.
  • FIG. 11 is a functional block diagram of a prediction device.
  • FIG. 12 This is a hardware configuration when the device of FIG. 11 is realized using a CPU.
  • FIG. 13 is a flowchart of a prediction program.
  • FIG. 14A is a cross-sectional view of a DNA chip according to another embodiment.
  • FIG. 14B is a diagram showing details of the processing circuit.
  • FIG. 15 is a configuration diagram of a prediction system.
  • FIG. 16 shows the hardware configuration of the server device.
  • FIG. 18 shows a probe
  • FIG. 19a is a diagram showing expression data.
  • FIG. 19b shows expression data
  • FIG. 19c is a diagram showing expression data.
  • FIG. 19d is a diagram showing expression data.
  • FIG. 19e shows expression data.
  • FIG. 19f is a diagram showing expression data.
  • FIG. 20a Data showing the relationship between the probe set and the CV value.
  • FIG. 20b Data showing the relationship between the probe set and the CV value.
  • FIG. 21 is a diagram showing a boundary surface based on a discriminant equation.
  • FIG. 22a is a diagram showing an average expression value, standard deviation ⁇ , eigenvector Pl, ⁇ 2, ⁇ 4, etc. of a marker gene group.
  • FIG. 22b is a diagram showing the mean expression value, standard deviation ⁇ , eigenvector Pl, ⁇ 2, ⁇ 4, etc. of the marker gene group.
  • FIG. 22c is a diagram showing the mean expression value, standard deviation ⁇ , eigenvector Pl, ⁇ 2, ⁇ 4, etc. of the marker gene group.
  • FIG. 22d is a diagram showing the mean expression value, standard deviation ⁇ , eigenvector Pl, ⁇ 2, ⁇ 4, etc. of the marker gene group.
  • FIG. 23 Data showing the relationship between the probe set and the CV value.
  • FIG. 24 is a diagram showing the correspondence between NCBI GenBank accession numbers and Affymetritas p lobe set numbers.
  • FIG. 25 shows the results of principal component analysis.
  • FIG. 26 is a diagram showing the mean expression value, standard deviation ⁇ , eigenvector Pl, ⁇ 2, ⁇ 3, etc. of the marker gene group.

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Abstract

[PROBLEMS] By using, for example, a sample collected from a site differing from a site showing a physiological change in a living body, the physiological change is estimated at a high accuracy. [MEANS FOR SOLVING PROBLEMS] A vital tissue is collected from each of individuals belonging to a first living body group (a group of individuals showing a physiological change) and a second living body group (a group of individuals showing no change) (Step P2). In this step, the vital tissue is collected at a site different from a site that is estimated as showing the physiological change. Next, hybridization is conducted on DNA chips by using the samples collected from the individuals (Step P4). The hybridized DNA chips are scanned and the density data in each probe region are obtained as gene expression data (Step P5). Based on the gene expression data for each of the individuals obtained above, genes showing a remarkable difference in expression data between the first living body group and the second living body group are selected (Step P6). Multivariate analysis is made on the gene expression data of the marker genes of each of the individuals so as to establish the criteria for differentiating the first living body group from the second living body group.

Description

明 細 書  Specification
生体の生理的変化の予測方法および装置  Method and apparatus for predicting physiological changes in living body
技術分野  Technical field
[0001] この発明は、遺伝子の発現データをもとに生体の生理的変ィ匕を予測する技術に関 するものである。  [0001] The present invention relates to a technique for predicting physiological changes in a living body based on gene expression data.
背景技術  Background art
[0002] 生体が何らかの生理的変ィ匕を起こす際に、それに先んじて単数または複数の遺伝 子の発現に何らかの変化が見られることがある。このような遺伝子は、しばしば生体の 生理的変化を予測するマーカー遺伝子として用いられる。  [0002] When a living body undergoes some physiological change, some change may be observed in the expression of one or more genes prior to that. Such genes are often used as marker genes for predicting physiological changes in the body.
[0003] 生体の生理的変化を予測しょうとする場合に用いる試料にっ 、ては、生理的変化 を生じる部位から、すなわち生理的変化が疾患の発症であれば、疾患の現れる部位 から採取した生体組織またはそれらから調製した生体試料を用 ヽることが一般的で ある (非特許文献 1)。  [0003] A sample used for predicting physiological changes in a living body was collected from a site where a physiological change occurred, that is, from a site where a disease appears if the physiological change occurred. In general, a biological tissue or a biological sample prepared from them is used (Non-patent Document 1).
[0004] —方、近年著しく進歩した DNAチップ解析技術や DNAアレイ解析技術により、膨 大な数の遺伝子の発現を一度に高い精度で調べることができるようになった。さらに は、上記解析方法などから得られた多数の遺伝子をマーカー遺伝子とし、それらの 発現の変化について統計解析等の数学的処理 (たとえば T検定、分散分析、クラスタ 一分析など)を行うことによって、生体の生理的変化を予測することが可能となった（ 非特許文献 2)。  [0004] On the other hand, DNA chip analysis technology and DNA array analysis technology that have made significant progress in recent years have made it possible to examine the expression of a large number of genes at once with high accuracy. Furthermore, by using many genes obtained from the above analysis methods as marker genes and performing mathematical processing such as statistical analysis (for example, T test, analysis of variance, cluster one analysis, etc.) on the changes in their expression, It has become possible to predict physiological changes in living bodies (Non-patent Document 2).
[0005] また、ある生理的変化、たとえば疾患の発症などを誘起する要素を有する生体群の 発現量と、力かる要素を有しない生体群の発現量とを比較し、明瞭な差異の認めら れる遺伝子を選抜して、マーカー遺伝子とすることが知られている (非特許文献 3)。  [0005] In addition, the expression level of a biological group having an element that induces a certain physiological change, for example, the onset of a disease, is compared with the expression level of a biological group that does not have a powerful element. It is known to select a gene to be a marker gene (Non-patent Document 3).
[0006] 非特許文献 1： Biochemical and Biophysical Research Communications 315, 1088 - 10 96(2004)  [0006] Non-Patent Document 1: Biochemical and Biophysical Research Communications 315, 1088-10 96 (2004)
[0007] 非特許文献 2 : Nature 415, 530-526(2002)  [0007] Non-Patent Document 2: Nature 415, 530-526 (2002)
[0008] 非特許文献 3： New England Journal of Medicine 347, 1999-2009  [0008] Non-Patent Document 3: New England Journal of Medicine 347, 1999-2009
発明の開示  Disclosure of the invention
差替え用紙（誦 6 発明が解決しょうとする課題 Replacement paper (誦 6 Problems to be solved by the invention
[0009] 上記従来技術においては、生理的変化の生じる部位力も試料を採取することが必 要であり、部位によってはこれが困難な場合もある。例えば生理的変化が中枢神経 系疾患の場合、生理的変化が現れる部位は脳であり、生命を維持している生体から 脳組織を採取することは極めて困難である。このように生体組織の採取に伴う生体へ の負荷や生命維持、あるいは技術上の問題から、当該生理的変化を生じる部位から 生体組織や生体試料を得ることができな 、場合も多 、。  [0009] In the above-described conventional technique, it is necessary to collect a sample for a site force at which a physiological change occurs, and this may be difficult depending on the site. For example, when the physiological change is a central nervous system disease, the site where the physiological change appears is the brain, and it is extremely difficult to collect brain tissue from a living organism. As described above, in many cases, a biological tissue or a biological sample cannot be obtained from a site where the physiological change occurs due to a burden on a living body, life support, or a technical problem associated with the collection of the biological tissue.
[0010] また、上記の従来技術では、 2群を比較する方法やマーカー遺伝子の選抜基準な どが標準化されておらず、より精度の高い予測を可能とするものではな力つた。  [0010] In addition, in the above-described prior art, methods for comparing the two groups, selection criteria for marker genes, and the like have not been standardized, and have not been able to make predictions with higher accuracy.
[0011] そこで、この発明は、生体の生理的変化が生じる部位とは異なる部位力 採取した 試料などを用いて、当該生理的変化を高い精度で予測する技術の提供を目的とする  Accordingly, an object of the present invention is to provide a technique for predicting a physiological change with high accuracy using a sample obtained by collecting a part force different from a part where a physiological change occurs in a living body.
[0012] また、当該予測方法に適したマーカー遺伝子の選抜技術を提供することを目的と する。 [0012] It is another object of the present invention to provide a marker gene selection technique suitable for the prediction method.
[0013] さらにまた、当該予測方法に適した予測基準の作成技術を提供することを目的とす る。  [0013] It is another object of the present invention to provide a technique for creating a prediction criterion suitable for the prediction method.
課題を解決するための手段  Means for solving the problem
[0014] (1)この発明に係る生体の生理的変化の予測方法は、当該生理的変化を生じる複数 の個体と当該生理的変化を生じな 、複数の個体を対象として、当該生理的変化予 測の対象部位とは異なる部位力も採取した生体組織にっ 、て、複数の遺伝子の遺 伝子発現量を検出するステップと、前記遺伝子のうち、前記生理的変化を生じる個 体と前記生理的変化を生じない個体との間において、統計的に発現量の差異が見 Vヽだされる遺伝子をマーカー遺伝子群として選抜するステップと、前記生理的変化を 生じる個体と前記生理的変化を生じない個体との間でマーカー遺伝子群の発現量 につ 、て多変量解析を行!、、マーカー遺伝子群の発現量に基づ!/、て前記発症の有 無を判別するための判別基準を生成するステップと、予測対象である個体にっ 、て、 前記生理的変化予測の対象部位とは異なる部位力 採取した生体組織について、 少なくともマーカー遺伝子群を含む遺伝子について遺伝子発現量を検出するステツ プと、予測対象個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に 前記判定基準を適用し、前記対象部位における生理的変化の有無を予測するステ ップとを備えている。 [0014] (1) A method for predicting physiological changes in a living body according to the present invention includes a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change. Detecting a gene expression level of a plurality of genes by means of a biological tissue from which a site force different from the measurement target site is also collected, and among the genes, the individual causing the physiological change and the physiological Selecting a gene that has a statistical difference in expression level as an marker gene group from an individual that does not cause a change, and an individual that produces the physiological change and the physiological change that does not occur Perform multivariate analysis on the expression level of marker gene groups with individuals !, and based on the expression level of marker gene groups! /, Generate discrimination criteria to determine the presence or absence of the onset And the individual to be predicted The step of detecting the gene expression level of at least a gene including a marker gene group in a biological tissue collected from a site force different from the target site of the physiological change prediction And a step of predicting the presence or absence of a physiological change in the target region by applying the determination criterion to the gene expression level of a gene including the marker gene group of the prediction target individual.
[0015] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。  [0015] Therefore, the presence or absence of a physiological change with high accuracy can be predicted based on the biological tissue collected from a site force different from the site causing the physiological change.
[0016] (2)この発明に係る生体の生理的変化の予測方法は、予測対象である個体について 、前記生理的変化予測の対象部位とは異なる部位力 採取した生体組織にっ 、て、 少なくともマーカー遺伝子群を含む遺伝子について遺伝子発現量を検出するステツ プと、予測対象個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に 判定基準を適用し、前記対象部位における生理的変化の有無を予測するステップと を備え、  [0016] (2) The method for predicting physiological changes in a living body according to the present invention includes at least a biological tissue obtained by collecting a site force different from the target site of the physiological change prediction for an individual to be predicted. Apply judgment criteria to the step of detecting gene expression level for genes including marker gene groups and the gene expression level for genes including marker gene groups of the target individuals to predict the presence or absence of physiological changes at the target site Comprising the steps of
前記マーカー遺伝子群は、当該生理的変化を生じる複数の個体と当該生理的変 化を生じない複数の個体を対象として、当該生理的変化予測の対象部位とは異なる 部位カゝら採取した生体組織について、複数の遺伝子の遺伝子発現量を検出し、前 記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個体と の間において、統計的に発現量の差異が見いだされる遺伝子を選抜したものであり 前記判定基準は、前記生理的変化を生じる個体と前記生理的変化を生じな!/、個体 との間でマーカー遺伝子群の発現量について多変量解析を行い、マーカー遺伝子 群の発現量に基づ 、て作成された判別基準であることを特徴として 、る。  The marker gene group consists of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue collected from a portion different from the target site for the physiological change prediction. The gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between the individual that produces the physiological change and the individual that does not produce the physiological change. The selection criterion is that a multivariate analysis is performed on the expression level of the marker gene group between the individual causing the physiological change and the physiological change! /, And the individual. Based on the expression level of the group, it is a discriminant criterion created based on the expression level.
[0017] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。  [0017] Therefore, it is possible to predict the presence or absence of a physiological change with high accuracy based on the biological tissue collected from a site force different from the site causing the physiological change.
[0018] (3)この発明に係る生体の生理的変化の予測に用いる判別基準を生成する方法は、 当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を対 象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織に ついて、複数の遺伝子の遺伝子発現量を検出するステップと、前記遺伝子のうち、 前記生理的変化を生じる個体と前記生理的変化を生じない個体との間において、統 計的に発現量の差異が見いだされる遺伝子をマーカー遺伝子群として選抜するステ ップと、前記生理的変化を生じる個体と前記生理的変化を生じな!/、個体との間でマ 一力一遺伝子群の発現量にっ 、て多変量解析を行 、、マーカー遺伝子群の発現量 に基づいて、前記対象部位における生理的変化の有無を判別するための判別基準 を生成するステップとを備えて 、る。 [0018] (3) A method for generating a discrimination criterion used for predicting physiological changes of a living body according to the present invention is intended for a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change. A step of detecting gene expression levels of a plurality of genes in a biological tissue collected from a site force different from the target site for the physiological change prediction, and among the genes, the individual that causes the physiological change and the physiological Select genes that are statistically found to be differentially expressed as marker gene groups from individuals that do not undergo genetic changes. Multivariate analysis is performed on the basis of the expression level of a single gene group between the individual that causes the physiological change and the physiological change! / Generating a discrimination criterion for discriminating the presence or absence of a physiological change in the target region based on the expression level of the target site.
[0019] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測するための判別基準を得ることができる。  [0019] Therefore, it is possible to obtain a discrimination criterion for predicting the presence / absence of a physiological change with high accuracy based on a body tissue obtained by collecting a site force different from a site causing a physiological change.
[0020] (4)この発明に係るマーカー遺伝子の選抜方法は、生理的変化を生じる複数の個体 と当該生理的変化を生じない複数の個体を対象として、当該生理的変化予測の対 象部位とは異なる部位力 採取した生体組織にっ 、て、複数の遺伝子の遺伝子発 現量を検出するステップと、前記遺伝子のうち、前記生理的変化を生じる個体と前記 生理的変化を生じな 、個体との間にお 、て、統計的に発現量の差異が見 、だされる 遺伝子をマーカー遺伝子群として選抜するステップとを備えている。  [0020] (4) The method for selecting a marker gene according to the present invention comprises a plurality of individuals that cause physiological changes and a plurality of individuals that do not cause the physiological changes. Different body force, the step of detecting the gene expression level of a plurality of genes using the collected biological tissue, and the individual of the gene that causes the physiological change and the individual that does not cause the physiological change. And a step of selecting genes to be expressed as marker gene groups when the difference in expression level is statistically observed.
[0021] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐマーカー遺伝子を選抜することができる。  [0021] Therefore, a marker gene with high accuracy can be selected based on the biological tissue collected from a site force different from the site causing the physiological change.
[0022] (5)この発明に係る生体の生理的変化の予測方法は、遺伝子発現量の検出は、遺伝 子発現検出素子を用いて行うことを特徴としている。  [0022] (5) The method for predicting physiological changes in a living body according to the present invention is characterized in that the detection of the gene expression level is performed using a gene expression detection element.
[0023] したがって、遺伝子発現量の取得が容易である。  [0023] Therefore, it is easy to obtain the gene expression level.
[0024] (6)この発明に係る判定基準作成プログラムは、生理的変化を生じる複数の個体と当 該生理的変化を生じない複数の個体を対象として、当該生理的変化予測の対象部 位とは異なる部位力 採取した生体組織にっ 、て、複数の遺伝子の遺伝子発現量 を検出するステップと、検出した個体ごとの遺伝子発現量を前記生理的変化の有無 と関連付けて基礎データとするステップと、前記基礎データに基づいて、前記遺伝子 のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個体との間にお いて、統計的に発現量の差異が見いだされる遺伝子をマーカー遺伝子群として選抜 するステップと、前記生理的変化を生じる個体と前記生理的変化を生じな!/、個体との 間でマーカー遺伝子群の発現量につ!、て多変量解析を行！ヽ、マーカー遺伝子群の 発現量に基づいて前記対象部位における生理的変化の有無を判別するための判別 基準を生成するステップとをコンピュータに実行させるための判定基準生成プロダラ ムである。 [0024] (6) The determination criterion creating program according to the present invention includes a plurality of individuals that cause a physiological change and a plurality of individuals that do not cause the physiological change, And a step of detecting gene expression levels of a plurality of genes by using different body forces, and a step of using the detected gene expression levels for each individual as basic data in association with the presence or absence of the physiological change. Based on the basic data, among the genes, a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is a marker gene. Multivariate analysis of the step of selecting as a group and the expression level of the marker gene group between the individual causing the physiological change and the physiological change! / The individual!判定, a determination criterion generation product for causing a computer to execute a determination criterion for determining the presence or absence of a physiological change in the target region based on the expression level of the marker gene group Is.
[0025] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測するための判定基準を作成できる。  [0025] Therefore, a criterion for predicting the presence or absence of a physiological change with high accuracy can be created based on a biological tissue collected from a part force different from a part that causes a physiological change.
[0026] (7)この発明に係るマーカー遺伝子選抜プログラムは、生理的変化を生じる複数の個 体と当該生理的変化を生じない複数の個体を対象として、当該生理的変化予測の 対象部位とは異なる部位力も採取した生体組織にっ 、て、複数の遺伝子の遺伝子 発現量を検出するステップと、遺伝子のうち、前記生理的変化を生じる個体と前記生 理的変化を生じな 、個体との間にお 、て、統計的に発現量の差異が見 、だされる遺 伝子をマーカー遺伝子群として選抜するステップとをコンピュータに実行させる。  [0026] (7) The marker gene selection program according to the present invention is intended for a plurality of individuals that cause physiological changes and a plurality of individuals that do not cause the physiological changes. Detecting a gene expression level of a plurality of genes by means of a biological tissue from which different site forces have been collected, and, among genes, between an individual that causes the physiological change and an individual that does not cause the physiological change. In this case, the computer is caused to execute a step of statistically seeing the difference in the expression level and selecting the gene to be expressed as a marker gene group.
[0027] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐマーカー遺伝子を選抜することができる。  [0027] Therefore, a marker gene with high accuracy can be selected based on a biological tissue collected from a site force different from the site causing the physiological change.
[0028] (8)この発明に係る予測プログラムは、予測対象である個体について、前記生理的変 化予測の対象部位とは異なる部位力 採取した生体組織について、少なくともマー カー遺伝子群を含む遺伝子について遺伝子発現量を検出するステップと、予測対象 個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に判定基準を適 用し、前記対象部位における生理的変化の有無を予測するステップとをコンピュータ に実行させるための予測プログラムであって、  [0028] (8) The prediction program according to the present invention relates to a gene including at least a marker gene group for a biological tissue obtained by collecting a site force different from the target site of the physiological change prediction for an individual to be predicted. The computer executes the steps of detecting the gene expression level and applying a judgment criterion to the gene expression level for the gene including the marker gene group of the individual to be predicted, and predicting the presence or absence of a physiological change in the target region. A prediction program for
前記マーカー遺伝子群は、当該生理的変化を生じる複数の個体と当該生理的変 化を生じない複数の個体を対象として、当該生理的変化の発症部位とは異なる部位 力も採取した生体組織にっ ヽて、複数の遺伝子の遺伝子発現量を検出し、 前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  The marker gene group is composed of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue in which a force different from the site where the physiological change occurs is collected. The gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between an individual that produces the physiological change and an individual that does not produce the physiological change among the genes. A selection of genes,
前記判定基準は、前記生理的変化を生じる個体と前記生理的変化を生じな!/、個体 との間でマーカー遺伝子群の発現量について多変量解析を行い、マーカー遺伝子 群の発現量に基づ 、て作成された判別基準であることを特徴として 、る。  The criterion is based on the expression level of the marker gene group by performing a multivariate analysis on the expression level of the marker gene group between the individual causing the physiological change and the physiological change not occurring! It is characterized by the discriminant criteria created by the above.
[0029] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。 [0030] (9)この発明に係るプログラムは、遺伝子発現量の検出は、遺伝子発現検出素子を用[0029] Therefore, the presence or absence of a physiological change with high accuracy can be predicted on the basis of the biological tissue collected from a site force different from the site causing the physiological change. [0030] (9) The program according to the present invention uses a gene expression detection element to detect the gene expression level.
V、て行うことを特徴として 、る。 V.
[0031] したがって、遺伝子発現量の取得が容易である。  [0031] Therefore, it is easy to obtain the gene expression level.
[0032] (10)この発明に係る遺伝子発現検出素子は、基板と、マーカー遺伝子群について、 それぞれの遺伝子発現量を検出するため、基板に形成されたプローブとを備え、 前記マーカー遺伝子群は、当該生理的変化を生じる複数の個体と当該生理的変 化を生じない複数の個体を対象として、当該生理的変化予測の対象部位とは異なる 部位カゝら採取した生体組織について、複数の遺伝子の遺伝子発現量を検出し、前 記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個体と の間において、統計的に発現量の差異が見いだされる遺伝子を選抜したものである ことを特徴としている。  [0032] (10) The gene expression detection element according to the present invention comprises a substrate and a probe formed on the substrate in order to detect each gene expression level for the marker gene group, and the marker gene group comprises: Targeting a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, a part of the tissue that is different from the target site for the physiological change prediction is collected from a plurality of genes. A gene expression level is detected, and a gene in which a difference in the expression level is statistically found between the individual causing the physiological change and the individual not causing the physiological change is selected from the genes described above. It is characterized by being.
[0033] したがって、生理的変化の有無を予測するために必要なマーカー遺伝子に対応す るプローブを有する遺伝子発現検出素子を提供することができる。  Therefore, it is possible to provide a gene expression detection element having a probe corresponding to a marker gene necessary for predicting the presence or absence of a physiological change.
[0034] (11)この発明に係る予測装置は、生体の生理的変化の予測を行うための予測装置で あって、  (11) A prediction device according to the present invention is a prediction device for predicting physiological changes in a living body,
基板と、マーカー遺伝子群について、それぞれの遺伝子発現量を検出するため、 基板に形成されたプローブと、プローブによって捉えられた遺伝子発現量を電気信 号に変換する変換部と、各遺伝子発現量に対応する電気信号を受け、判定基準に 基づいて生理的変化の有無を予測する予測部とを備え、  In order to detect the gene expression level of the substrate and marker gene group, the probe formed on the substrate, the conversion unit that converts the gene expression level captured by the probe into an electric signal, and the expression level of each gene A prediction unit that receives a corresponding electrical signal and predicts the presence or absence of a physiological change based on a criterion;
前記マーカー遺伝子群は、当該生理的変化を生じる複数の個体と当該生理的変 化を生じない複数の個体を対象として、当該生理的変化予測の対象部位とは異なる 部位カゝら採取した生体組織について、複数の遺伝子の遺伝子発現量を検出し、前 記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個体と の間において、統計的に発現量の差異が見いだされる遺伝子を選抜したものであり 前記判定基準は、前記生理的変化を発症する個体と前記生理的変化を発症しな The marker gene group consists of a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change, and a biological tissue collected from a portion different from the target site for the physiological change prediction. The gene expression level of a plurality of genes is detected, and a statistical difference in the expression level is found between the individual that produces the physiological change and the individual that does not produce the physiological change. A gene selected, and the criterion is that an individual who develops the physiological change and a person who does not develop the physiological change.
V、個体との間でマーカー遺伝子群の発現量にっ 、て多変量解析を行!、、マーカー 遺伝子群の発現量に基づ 、て作成された判別基準であることを特徴として！/、る。 V, multivariate analysis based on the expression level of the marker gene group between individuals, and the distinction criteria created based on the expression level of the marker gene group! /
[0035] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。 [0035] Therefore, a site force different from the site causing the physiological change is based on the collected biological tissue. Therefore, it is possible to predict the presence or absence of physiological changes depending on the accuracy.
[0036] (12)この発明に係るプログラムは、遺伝子発現検出素子は、 DNAチップまたは DNA アレイであることを特徴として 、る。  (12) The program according to the present invention is characterized in that the gene expression detection element is a DNA chip or a DNA array.
[0037] したがって、一度に大量の遺伝子発現量を取得することができる。 [0037] Accordingly, a large amount of gene expression can be obtained at one time.
[0038] (13)この発明に係る予測装置は、生体組織についての遺伝子の遺伝子発現量は、 当該生体組織またはそれから調製した生体試料に基づいて検出することを特徴とし ている。 [0038] (13) The prediction device according to the present invention is characterized in that the gene expression level of a gene for a living tissue is detected based on the living tissue or a biological sample prepared therefrom.
[0039] (14)この発明に係る予測装置は、生体組織が皮膚組織または粘膜組織であることを 特徴としている。  [0039] (14) The prediction device according to the present invention is characterized in that the living tissue is skin tissue or mucosal tissue.
[0040] したがって、困難性の低い部位力も容易に採取を行うことができる。  [0040] Therefore, it is possible to easily collect a site force with low difficulty.
[0041] (15)この発明に係る予測装置は、生体試料が繊維芽細胞であることを特徴としている  [0041] (15) The prediction device according to the present invention is characterized in that the biological sample is a fibroblast.
[0042] (16)この発明に係る予測装置は、生体試料が繊維芽細胞由来 RNAであることを特徴 としている。 [0042] (16) The prediction device according to the present invention is characterized in that the biological sample is fibroblast-derived RNA.
[0043] (17)この発明に係る予測装置は、発症部位が脳であることを特徴としている。  [0043] (17) The prediction device according to the present invention is characterized in that the onset site is the brain.
[0044] したがって、生体組織の採取が困難な脳について、生理的変化を予測することが できる。 [0044] Therefore, physiological changes can be predicted for a brain in which it is difficult to collect biological tissue.
[0045] (18)この発明に係る予測装置は、生理的変化が疾患の発症であることを特徴としてい る。  [0045] (18) The prediction device according to the present invention is characterized in that the physiological change is the onset of a disease.
[0046] したがって、疾患発症の有無を予測することができる。  [0046] Therefore, the presence or absence of disease onset can be predicted.
[0047] (19)この発明に係る予測装置は、疾患が中枢神経疾患であることを特徴としている。  [0047] (19) The prediction device according to the present invention is characterized in that the disease is a central nervous disease.
[0048] (20)この発明に係る予測装置は、中枢神経疾患が痴呆症、パーキンソン病、筋萎縮 性側索硬化症、またはプリオン病（クロイツフェルト一ヤコブ病）であることを特徴とし ている。 [0048] (20) The prediction device according to the present invention is characterized in that the central nervous system disease is dementia, Parkinson's disease, amyotrophic lateral sclerosis, or prion disease (Kreuzfeld-Jakob disease). .
[0049] (21)この発明に係る予測装置は、痴呆症がアルツハイマー病または前頭側頭型痴呆 であることを特徴として 、る。  [0049] (21) The prediction device according to the present invention is characterized in that the dementia is Alzheimer's disease or frontotemporal dementia.
[0050] したがって、アルツハイマー病の発症を予測することができる。 [0050] Therefore, the onset of Alzheimer's disease can be predicted.
[0051] (22)この発明に係る予測装置は、生理的変化を誘起する要素が Swedish変異、 Arctic 変異およびプレセリニン 1遺伝子 H136Y変異力も選ばれる 1種以上の要素であること を特徴としている。 [0051] (22) In the prediction device according to the present invention, the element that induces a physiological change is a Swedish mutation, Arctic Mutation and preserinin 1 gene It is characterized by being one or more elements selected for H136Y mutation.
[0052] (23)この発明に係る予測装置は、多変量解析は、主成分分析および線形判別分析を 含む解析方法であることを特徴として 、る。  [0052] (23) The prediction apparatus according to the present invention is characterized in that the multivariate analysis is an analysis method including principal component analysis and linear discriminant analysis.
[0053] (24)この発明に係る予測装置は、発現量の差異が見!、だされる遺伝子の選抜は、情 報量基準に基づ 、て行われることを特徴として 、る。 [0053] (24) The predicting apparatus according to the present invention is characterized in that the difference in the expression level is observed! Selection of the gene to be displayed is performed based on the information amount standard.
[0054] (25)この発明に係る予測装置は、情報量基準は、アレンのクロスバリデーシヨン基準 であることを特徴として 、る。 [0054] (25) The prediction device according to the present invention is characterized in that the information criterion is an Allen cross-validation criterion.
[0055] (26)この発明に係る予測装置は、遺伝子発現量の検出は、ハイブリダィゼーシヨンに よって遺伝子発現検出素子のプローブに結合した標識済みの遺伝子による光学的 特性の変化を検出することによって行うことを特徴としている。 [0055] (26) In the prediction device according to the present invention, the detection of the gene expression level is performed by detecting a change in optical characteristics due to the labeled gene bound to the probe of the gene expression detection element by hybridization. It is characterized by things.
[0056] (27)この発明に係る予測装置は、遺伝子発現量の検出は、ハイブリダィゼーシヨンに よって遺伝子発現検出素子のプローブに結合した遺伝子による電気的特性の変化 を検出することによって行うことを特徴としている。 [0056] (27) In the prediction device according to the present invention, the detection of the gene expression level is performed by detecting a change in electrical characteristics due to the gene bound to the probe of the gene expression detection element by hybridization. It is characterized by that.
[0057] (28)この発明に係る予測装置は、生体の生理的変化の予測を行うために用いる遺伝 子発現検出素子であって、 (28) A prediction apparatus according to the present invention is a gene expression detection element used for predicting physiological changes in a living body,
基板と、マーカー遺伝子群について、それぞれの遺伝子発現量を検出するため、 基板に形成されたプローブとを備え、  A substrate and a probe formed on the substrate for detecting the gene expression level of each marker gene group,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、前記遺伝子のうち、前記生理的 変化を生じる個体と前記生理的変化を生じない個体との間において、統計的に発現 量の差異が見いだされる遺伝子を選抜したものであり、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. And a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
前記各マーカー遺伝子のためのプローブは、  The probe for each marker gene is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量について主成分分析を行い、当該主成分に係る合成変量の係数 に応じて、各遺伝子に対応するプローブの検出感度を設定したものであることを特徴 としている。 Principal component analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and corresponds to each gene according to the coefficient of the synthetic variable related to the principal component. The detection sensitivity of the probe to be set is set. It is said.
[0058] (29)この発明に係る生体の生理的変化の予測システムは、サーバ装置と端末装置を 備えており、  (29) A biological physiological change prediction system according to the present invention includes a server device and a terminal device,
前記端末装置は、予測対象である個体について、前記生理的変化予測の対象部 位とは異なる部位力 採取した生体組織について、少なくともマーカー遺伝子群を含 む遺伝子につ!、て検出した遺伝子発現量を示す情報を送信する送信手段と、サー バ装置からの予測結果データを受信する受信手段と、受信した予測結果データを出 力する出力手段とを備え、  The terminal device is configured to detect a gene expression level detected for a gene including at least a marker gene group in a biological tissue collected from a site force different from the target site of the physiological change prediction for an individual to be predicted. Transmission means for transmitting the information indicating the reception, reception means for receiving the prediction result data from the server device, and output means for outputting the received prediction result data,
サーバ装置は、前記端末装置からの遺伝子発現量を示す情報を受信する受信手 段と、当該遺伝子発現量に判定基準を適用し、前記対象部位における生理的変化 の有無を予測する予測手段と、前記予測手段による予測結果データを前記端末装 置に送信する送信手段とを備えており、  A server device that receives information indicating the gene expression level from the terminal device; a prediction unit that applies a determination criterion to the gene expression level and predicts the presence or absence of a physiological change in the target site; Transmission means for transmitting the prediction result data by the prediction means to the terminal device,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、前記遺伝子のうち、前記生理的 変化を生じる個体と前記生理的変化を生じない個体との間において、統計的に発現 量の差異が見いだされる遺伝子を選抜したものであり、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. And a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ V、て作成された判別基準であることを特徴として!/、る。  A multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and V is created based on the expression level of the marker gene group. It is characterized by being a discriminant criterion!
[0059] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。さらに、端末装置側に予 測のための装置がなくとも、サーバ装置に接続できる環境であれば予測結果を得る ことができる。 [0059] Accordingly, it is possible to predict the presence / absence of a physiological change with high accuracy based on a body tissue collected from a site force different from a site causing a physiological change. Furthermore, even if there is no prediction device on the terminal device side, the prediction result can be obtained in an environment where the server device can be connected.
[0060] (30)この発明に係るサーバ装置は、予測対象である個体について、前記生理的変化 予測の対象部位とは異なる部位力も採取した生体組織にっ 、て、少なくともマーカ 一遺伝子群を含む遺伝子について検出した遺伝子発現量を示す情報を前記端末 装置から受信する受信手段と、当該遺伝子発現量に判定基準を適用し、前記対象 部位における生理的変化の有無を予測する予測手段と、前記予測手段による予測 結果データを前記端末装置に送信する送信手段とを備えており、 [0060] (30) The server device according to the present invention provides at least a marker for a biological tissue in which a part force different from the physiological change prediction target part is collected for an individual to be predicted. A receiving means for receiving information indicating a gene expression level detected for a gene including one gene group from the terminal device, and a prediction for predicting the presence or absence of a physiological change in the target site by applying a criterion to the gene expression level Means, and transmission means for transmitting the prediction result data by the prediction means to the terminal device,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、前記遺伝子のうち、前記生理的 変化を生じる個体と前記生理的変化を生じない個体との間において、統計的に発現 量の差異が見いだされる遺伝子を選抜したものであり、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. And a gene in which a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change is selected from the genes,
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ V、て作成された判別基準であることを特徴として!/、る。  A multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and V is created based on the expression level of the marker gene group. It is characterized by being a discriminant criterion!
[0061] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。  [0061] Therefore, the presence or absence of a physiological change with high accuracy can be predicted based on the collected body tissue from a site force different from the site causing the physiological change.
[0062] (31)この発明に係る端末装置は、予測対象である個体について、前記生理的変化予 測の対象部位とは異なる部位力も採取した生体組織にっ 、て、少なくともマーカー 遺伝子群を含む遺伝子について検出した遺伝子発現量を示す情報を送信する送信 手段と、サーバ装置力 の予測結果データを受信する受信手段と、受信した予測結 果データを出力する出力手段とを備え、  [0062] (31) A terminal device according to the present invention includes at least a marker gene group for a biological tissue in which a site force different from the target site for the physiological change prediction is collected for an individual to be predicted A transmission means for transmitting information indicating the gene expression level detected for the gene, a reception means for receiving the prediction result data of the server device power, and an output means for outputting the received prediction result data,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、前記遺伝子のうち、前記生理的 変化を生じる個体と前記生理的変化を生じない個体との間において、統計的に発現 量の差異が見 、だされる遺伝子を選抜したものであることを特徴として 、る。  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Among the genes, the genes that are found to be statistically different in the expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected. It is characterized by that.
[0063] したがって、生理的変化を生じる部位とは異なる部位力 採取した生体組織に基づ いて、精度よぐ生理的変化の有無を予測することができる。さらに、端末装置側に予 測のための装置がなくとも、サーバ装置に接続できる環境であれば予測結果を得る ことができる。 [0063] Therefore, a site force different from the site causing the physiological change is based on the collected biological tissue. Therefore, it is possible to predict the presence or absence of physiological changes depending on the accuracy. Furthermore, even if there is no prediction device on the terminal device side, the prediction result can be obtained in an environment where the server device can be connected.
[0064] (32)この発明に係る予測装置は、マーカー遺伝子に、米国生物工学情報センター (N ational Center for Biotechnology Informationゝ（NCBI) )の遺伝子情報データベース「 Genbank」のァクセシヨン番号によって特定される、少なくとも以下の 51個の遺伝子が 含まれることを特徴として！/、る：  [0064] (32) In the prediction device according to the present invention, the marker gene is specified by an accession number of the gene information database "Genbank" of the National Center for Biotechnology Information (NCBI), It is characterized by at least the following 51 genes included! /, Ru:
BC006249, NM_000454、 NM_001780、 BG531983、 NM_000177、 NM_000801、 NM_0 03197、 NM— 006389、 NM— 004446、 NM— 007178、 NM— 002414、 NM— 004092、 NM— 00365 1、 NM— 003022、 NM— 004528、 NM— 005614、 NM— 004730、 BC004467, NM— 001483、 NM —003365、 NM— 007214、 AI927770, NM— 001685、 NM— 005493、 NM— 001753、 NM— 00296 1、 NM_001157、 NM_004545、 NM_003915、 AF208850、 AW510696、 AF312393、 BC00 2977、 AF313911、 AF000974, L18964、 U76833, M55580、 U43430, BC005911、 AU1 47399、 AL523310、 AI144075, AL117593、 AA650558、 AI123426, NM— 005051、 NM— 0 14380、 NM— 015920、 NM— 017821、 AK001105。  BC006249, NM_000454, NM_001780, BG531983, NM_000177, NM_000801, NM_0 03197, NM—006389, NM—004446, NM—007178, NM—002414, NM—004092, NM—00365 1, NM—003022, NM—004528, NM—004528 005614, NM—004730, BC004467, NM—001483, NM—003365, NM—007214, AI927770, NM—001685, NM—005493, NM—001753, NM—00296 1, NM_001157, NM_004545, NM_003915, AF208850, AW510696, AF3393 , BC00 2977, AF313911, AF000974, L18964, U76833, M55580, U43430, BC005911, AU1 47399, AL523310, AI144075, AL117593, AA650558, AI123426, NM—005051, NM—014380, NM—015920, NM—017821, AK001105.
[0065] したがって、正確にアルツハイマー病の発症を予測することができる。 [0065] Therefore, the onset of Alzheimer's disease can be accurately predicted.
[0066] この発明にお 、て「生体の生理的変化」とは、細胞、組織、器官などの生命体の一 部あるいは個体全体に生じる、観察可能な変化をいう。たとえば、形、色、大きさ、温 度、エネルギー消費、物質産生や運動'行動の変化、疾患の発症などを含む概念で ある。なお、「生理的変化を発現する要素」は、上述の生体の生理的変化を誘起し得 る、物質的、または非物質的なあらゆる事物を含む。具体的には、遺伝子、環境 (気 温、水温、湿度、浸透圧、音及び振動等)、栄養状態、薬物投与、ストレス、性格、性 癖及び嗜好等を挙げることができるが、これらに限定されない。また「生理的変化を 誘起する要素」も同義である。 [0066] In the present invention, "physiological change of a living body" refers to an observable change that occurs in a part of an organism such as a cell, tissue, organ, or the entire individual. For example, it is a concept that includes shape, color, size, temperature, energy consumption, substance production and changes in movement and behavior, and the onset of disease. The “element that expresses physiological change” includes any material or non-material matter that can induce the physiological change of the living body. Specific examples include genes, environment (temperature, water temperature, humidity, osmotic pressure, sound, vibration, etc.), nutritional status, drug administration, stress, personality, personality, and preferences. Not. “Elements that induce physiological changes” are also synonymous.
[0067] 「生理的変化の予測」とは、将来に生じるであろう生理的変化を予測する場合だけ でなぐ直接的に観測することが困難な部位について現在の生理的変化の有無を予 測する場合も含む概念である。  [0067] “Prediction of physiological change” is the prediction of the presence or absence of a current physiological change in a site that is difficult to observe directly, not only when predicting a physiological change that will occur in the future. It is a concept that includes cases where
[0068] 「生体の生理的変化予測マーカー」とは生体の生理的変化を予測するために、直 接または間接的に利用されるものをいう。これには生体の生理的変化に関連して、生 体内において発現が変動する遺伝子、ヌクレオチド、ポリヌクレオチドまたはタンパク 質、ポリペプチド、及びそれらを特異的に認識し、また結合することのできるポリヌクレ ォチドまたは抗体が包含される。これらのヌクレオチド、ポリヌクレオチド及び抗体は、 上述の性質に基づいて、生体内で発現した上述の遺伝子及びタンパク質を検出す るためのプローブとして、またヌクレオチド及びポリヌクレオチドは生体内で発現した 上述の遺伝子を増幅するためのプライマーとして、さらにタンパク質は結合する物質 のスクリーニングに有効に利用することができる。また、「生理的変化予測マーカー」 や「予測マーカー」、「マーカー」も同義である。 [0068] "Biological physiological change prediction marker" is a direct marker for predicting physiological changes in the living body. It is used directly or indirectly. This includes genes, nucleotides, polynucleotides or proteins, polypeptides, and polynucleotides capable of specifically recognizing and binding to them whose expression varies in the body in relation to physiological changes in the body. Or an antibody is included. Based on the above properties, these nucleotides, polynucleotides and antibodies are used as probes for detecting the above-described genes and proteins expressed in vivo, and nucleotides and polynucleotides are expressed in vivo. As a primer for amplifying the protein, the protein can be effectively used for screening a substance to be bound. “Physiological change prediction marker”, “prediction marker”, and “marker” are also synonymous.
[0069] 「遺伝子」には、 RNAや DNAなどの塩基配列によって示される遺伝情報を 、うちの である。ヒト、マウス、ラットなどの生物種間で保存されるォーソログ遺伝子なども含ま れる。遺伝子は、タンパク質をコードするものだけでなぐ RNAや DNAとして機能す るものであってもよい。遺伝子は、その塩基配列にしたがうタンパク質をコードするの が一般的であるが、当該タンパク質と生物学的機能が同等であるタンパク質 (たとえ ば同族体 (ホモログゃスプライスバリアントなど)や変異体や誘導体)をコードするもの であってもよい。たとえば、遺伝情報による塩基配列によって示されるタンパク質とは わずかに塩基配列が異なるタンパク質であって、その塩基配列が遺伝子情報による 塩基配列の相補配列とハイブリダィズするようなタンパク質をコードするような遺伝子 であってもよい。  [0069] "Gene" includes genetic information represented by a base sequence such as RNA or DNA. Also included are orthologous genes that are conserved among species such as humans, mice, and rats. A gene may function as RNA or DNA in addition to those that encode proteins. A gene generally encodes a protein according to its base sequence, but a protein having a biological function equivalent to the protein (for example, a homologue (such as a homologue splice variant), a mutant or a derivative). May be used. For example, a protein that encodes a protein whose base sequence is slightly different from the protein indicated by the base sequence based on genetic information, and whose base sequence hybridizes with a complementary sequence of the base sequence based on the genetic information. May be.
[0070] 「DNA」とは、 2本鎖 DNAだけでなぐそれを構成するセンス鎖およびアンチセンス 鎖といった各 1本鎖 DNAも含む概念である。 DNAは、ヒトゲノム DNAを含む 2本鎖 DNAだけでなく、 cDNAを含む 1本鎖 DNA (正鎖)ならびに当該正鎖と相補的な配 列を有する 1本鎖 DNA (相補鎖）およびこれらの断片の 、ずれをも含む概念である。 また、 DNAはその全体だけでなぐ発現制御領域、コード領域、ェキソン、イントロン などの各機能領域も含む概念である。また、 cDNA、ゲノム DNA、合成 DNA等を含 む概念である。  [0070] "DNA" is a concept that includes each single-stranded DNA such as a sense strand and an antisense strand that constitutes a double-stranded DNA alone. DNA includes not only double-stranded DNA containing human genomic DNA, but also single-stranded DNA (positive strand) containing cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof. This is a concept that includes deviations. In addition, DNA is a concept that includes functional regions such as expression control region, coding region, exon, intron, and so on. It is also a concept that includes cDNA, genomic DNA, synthetic DNA, and so on.
[0071] 「RNA」とは、 1本鎖 RNAだけでなぐこれに相補的な配列を有する 1本鎖 RNAや これらから構成される 2本差 RNAを含む概念である。また、 totalRNA、 mRNA、 rR NAを含む概念である。 [0071] "RNA" is a concept including single-stranded RNA having a complementary sequence to single-stranded RNA and double-difference RNA composed thereof. TotalRNA, mRNA, rR It is a concept that includes NA.
[0072] 「遺伝子発現検出素子」とは、遺伝子発現の有無または発現量を検出する素子を いい、光学的に発現量を検出するものだけでなぐ電気的に発現量を検出するもの を含み、発現の有無や発現量やを物理量に変換するものをいう。ガラス面、プラスチ ックのゥエルやチューブの側面や底面、微少ビーズの表面などにプローブ DNAを配 置させたものを含み、 DNAチップや DNAアレイを含む概念である。  [0072] "Gene expression detection element" refers to an element that detects the presence or absence or expression level of gene expression, and includes an element that electrically detects the expression level in addition to the one that optically detects the expression level. This refers to the presence / absence of expression and the expression level converted into physical quantities. This concept includes DNA chips and DNA arrays, including those in which probe DNA is placed on the glass surface, plastic wells, side and bottom surfaces of tubes, and the surface of microbeads.
[0073] 「DNAチップ」「DNAアレイ」とは、プローブ DNAを基板上に配した構造を有し、ハ イブリダィゼーシヨンにより、複数の遺伝子の発現を測定するものである。光学的に発 現量を計測するためのものだけでなぐ電気的に発現量を出力するものも含む。「D NAチップ」としては、たとえば、ァフィメトリタス社の GeneChip (商標）を用いることがで きる。 「DNAアレイ」としては、アマシャムバイオサイエンス社の CodeLink Expression Bioarray (商標）を用いることができる。なお、 DNAアレイには、 DNAマイクロアレイだ けでなく DNAマクロアレイも含む。  The “DNA chip” and “DNA array” have a structure in which probe DNA is arranged on a substrate, and measure the expression of a plurality of genes by hybridization. This includes not only optically measuring the expression level but also outputting the expression level electrically. For example, GeneChip (trademark) manufactured by Affymetritas can be used as the “DNA chip”. As the “DNA array”, CodeLink Expression Bioarray (trademark) of Amersham Biosciences can be used. DNA arrays include not only DNA microarrays but also DNA macroarrays.
[0074] 「発現量」とは、遺伝子の発現量を直接的に測定した値だけでなぐ所定の計算や 統計学的手法によって変換された値も含む概念である。また、「遺伝子発現量」や「 発現シグナル」、「遺伝子発現シグナル」、「発現シグナル値」、「遺伝子発現シグナル 値」、「遺伝子発現データ」、「発現データ」等も個々の遺伝子の発現を反映する値を 指すものとして同義である。  [0074] "Expression level" is a concept that includes a value calculated by a predetermined calculation or a statistical technique, in addition to a value obtained by directly measuring the expression level of a gene. In addition, “gene expression level”, “expression signal”, “gene expression signal”, “expression signal value”, “gene expression signal value”, “gene expression data”, “expression data”, etc. It is synonymous to indicate the value to be reflected.
[0075] 「遺伝子発現」とは遺伝子の発現量により表現される生体の遺伝子発現の態様を指 し、 1個の遺伝子の発現量により表される場合及び複数の遺伝子の発現量により表さ れる場合のいずれもが含まれる。また、「発現」も生体の遺伝子発現の態様を指すも のとして同義である。  [0075] "Gene expression" refers to an aspect of gene expression in a living body expressed by the expression level of a gene, and is expressed by the expression level of one gene or the expression level of a plurality of genes. Any of the cases are included. “Expression” is also synonymous with the expression of gene expression in a living body.
[0076] 「生体組織について遺伝子の発現量を検出する」とは、生体組織そのものを用いて 発現量を検出する場合だけでなぐ生体組織に基づいて調製した生体試料を用いて 発現量を検出する場合を含む概念である。  [0076] “Detecting the expression level of a gene in a living tissue” means detecting the expression level using a biological sample prepared based on the living tissue, which is not only when detecting the expression level using the living tissue itself. It is a concept that includes cases.
[0077] 「生体試料」とは、採取した組織から調製した試料を! ヽ、たとえば、細胞、繊維芽 細胞、赤血球、白血球、リンパ球、核酸、繊維芽細胞由来 RNAなどである。  [0077] "Biological sample" refers to a sample prepared from collected tissues, such as cells, fibroblasts, erythrocytes, leukocytes, lymphocytes, nucleic acids, fibroblast-derived RNA, and the like.
[0078] 「プログラム」とは、とは、 CPUにより直接実行可能なプログラムだけでなぐソース 形式のプログラム、圧縮処理がされたプログラム、暗号ィ匕されたプログラム等を含む 概念である。 [0078] "Program" refers to a source consisting of only a program that can be directly executed by a CPU. The concept includes a format program, a compressed program, an encrypted program, and the like.
発明を実施するための形態  BEST MODE FOR CARRYING OUT THE INVENTION
[0079] 1.生理的変化の予測方法  [0079] 1. Method of predicting physiological changes
図 1、図 2にこの発明の一実施形態による生体の生理的変化を予測する方法にお ける処理の流れを示す。図 1は判別基準の生成を示し、図 2は当該判別基準を用い た予測を示している。  1 and 2 show the flow of processing in a method for predicting physiological changes in a living body according to an embodiment of the present invention. Figure 1 shows the generation of discrimination criteria, and Fig. 2 shows the prediction using the discrimination criteria.
[0080] 図 1において、まず、予測対象となる生理的変化について、当該生理的変化を生じ る生体群 (第 1生体群とする）と生じな!/ヽ生体群 (第 2生体群とする）を特定する (ステ ップ Pl)。次に、第 1生体群および第 2生体群に属する各個体から生体組織を採取 する (ステップ P2)。この際、生理的変化を予測する対象部位とは異なる部位の生体 組織を採取する。たとえば、脳における生理的変化を予測する場合であれば、ヒトの 上腕部の皮膚等の組織を採取する。  [0080] In FIG. 1, first, regarding the physiological change to be predicted, the biological group that produces the physiological change (referred to as the first biological group) and the! / ヽ biological group (referred to as the second biological group). ) (Step Pl). Next, a biological tissue is collected from each individual belonging to the first biological group and the second biological group (step P2). At this time, a biological tissue at a site different from the target site for which a physiological change is predicted is collected. For example, when predicting physiological changes in the brain, tissue such as human upper arm skin is collected.
[0081] 次に、各個体力も採取した生体組織に基づ 、て試料を調製する (ステップ P3)。た とえば、採取した生体組織から繊維芽細胞を調製する。  [0081] Next, a sample is prepared on the basis of the collected biological tissue for each individual force (step P3). For example, fibroblasts are prepared from the collected biological tissue.
[0082] 次に、この試料を用いて、 DNAチップによるハイブリダィゼーシヨンを行う（ステップ P4)。たとえば、試料から mRNAを取り出し、この mRNAの cDNA (相補性 DNA)を 複製する。この cDNAを蛍光処理する。さらに蛍光処理された cDNAを含む水溶液 を DNAチップのプローブに滴下し、ハイブリダィゼーシヨン（2重鎖形成反応）を行う  [0082] Next, hybridization using a DNA chip is performed using this sample (step P4). For example, mRNA is removed from a sample, and cDNA (complementary DNA) of this mRNA is replicated. This cDNA is fluorescently treated. Furthermore, an aqueous solution containing the fluorescently treated cDNA is dropped onto the probe of the DNA chip to perform hybridization (duplex formation reaction).
[0083] DNAチップには、縦横に多くのプローブ領域が設けられ、各プローブ領域には D NAプローブが多数設けられている。各プローブ領域ごとに、 DNAプローブの塩基 配列は異なるものとなっている。 [0083] The DNA chip is provided with a large number of probe regions in the vertical and horizontal directions, and a large number of DNA probes are provided in each probe region. The DNA probe has a different base sequence for each probe region.
[0084] 上記のハイブリダィゼーシヨンにより、蛍光処理された cDNAは対応する塩基配列 を有する DNAプローブと相互作用する。したがって、各プローブ領域の色濃度を測 定することによって、 mRNAの発現量を検出することができる。  [0084] By the hybridization described above, the fluorescence-treated cDNA interacts with a DNA probe having a corresponding base sequence. Therefore, the expression level of mRNA can be detected by measuring the color density of each probe region.
[0085] 次に、ハイブリダィゼーシヨンした DNAチップをスキャナで撮像し、 mRNAの発現 量に応じた色濃度の画像を得る。さら〖こ、画像解析ソフトウェアにより、この画像に基 づいて、各プローブ領域ごとの濃度データを遺伝子発現データとして取得する (ステ ップ P5)。 [0085] Next, the hybridized DNA chip is imaged with a scanner to obtain an image having a color density corresponding to the expression level of mRNA. Sarako, based on this image by image analysis software Next, obtain concentration data for each probe region as gene expression data (step P5).
[0086] 上記のようにして得た各個体ごとの遺伝子発現データに基づいて、第 1生体群と第 2生体群の遺伝子発現データを比較することによって、第 1生体群と第 2生体群にお いて顕著に発現データの異なる遺伝子を選抜する (ステップ P6)。このようにして選 抜した遺伝子群を、マーカー遺伝子群とする。たとえば、クロスノくリデーシヨン基準な どの情報量基準を用いて、マーカー遺伝子群の選抜を行うことができる。  [0086] Based on the gene expression data for each individual obtained as described above, by comparing the gene expression data of the first biological group and the second biological group, the first biological group and the second biological group Then select genes with significantly different expression data (Step P6). The gene group selected in this way is defined as a marker gene group. For example, a marker gene group can be selected by using an information criterion such as a cross-reduction criterion.
[0087] 次に、各個体におけるマーカー遺伝子群の遺伝子発現データについて多変量解 析を行い、第 1生体群と第 2生体群を判別する基準を生成する。たとえば、主成分分 析を行い、判別基準を得ることができる。  [0087] Next, multivariate analysis is performed on the gene expression data of the marker gene group in each individual to generate a reference for discriminating between the first biological group and the second biological group. For example, a principal component analysis can be performed to obtain a discrimination criterion.
[0088] 以上のようにして生理的変化の予測に用いる判別基準を生成することができる。  As described above, a discrimination criterion used for predicting physiological changes can be generated.
[0089] 図 2に、生理的変化の予測を示す。まず、ステップ P11において、予測対象である 個体から生体組織を採取する。この際、生理的変化を予測する対象部位とは異なる 部位の生体組織を採取する。  [0089] Figure 2 shows the prediction of physiological changes. First, in step P11, a biological tissue is collected from the individual that is the prediction target. At this time, a biological tissue of a site different from the target site for which physiological change is predicted is collected.
[0090] 次に、採取した生体組織から生体試料を調整する (ステップ P12)。なお、生体試料 は、判別基準の作成の際に用いたと同じ部位の生体組織力も採取した同じ種類の生 体試料であることが好まし 、。  [0090] Next, a biological sample is prepared from the collected biological tissue (step P12). It is preferable that the biological sample is the same type of biological sample from which the tissue strength of the same part as that used in creating the discrimination criterion is also collected.
[0091] この試料を用いて、 DNAチップによるハイブリダィゼーシヨンを行う（ステップ P13) 。たとえば、試料から mRNAを取り出し、この mRNAの cDNA (相補性 DNA)を複製 する。この cDNAを蛍光処理する。さらに蛍光処理された cDN Aを含む水溶液を DN Aチップのプローブに滴下し、ハイブリダィゼーシヨン（2重鎖形成反応）を行う。なお 、ここで用 、る DN Aチップは判別基準の生成の際に用いたものと同様の DNAチッ プでもよ!/、が、マーカー遺伝子群に対応するプローブだけを設けた専用の DNAチッ プの方が好ましい。  [0091] Using this sample, hybridization using a DNA chip is performed (step P13). For example, mRNA is removed from a sample, and cDNA (complementary DNA) of this mRNA is replicated. This cDNA is fluorescently treated. Furthermore, an aqueous solution containing fluorescently treated cDNA is dropped onto the probe of the DNA chip, and hybridization (duplex formation reaction) is performed. The DNA chip used here can be the same DNA chip used to generate the discrimination criteria! /, But a dedicated DNA chip with only probes corresponding to the marker gene group. Is preferred.
[0092] 次に、ハイブリダィゼーシヨンした DNAチップをスキャナで撮像し、 mRNAの発現 量に応じた色濃度の画像を得る。さらに、画像解析ソフトウェアにより、この画像に基 づいて、各プローブ領域ごとの濃度データを遺伝子発現データとして取得する (ステ ップ P14)。 [0093] 続いて、取得した遺伝子発現データに判別基準を適用し、生理的変化の有無を予 測し (ステップ P 15)、予測結果を得る (ステップ P 16)。上記のようにして生体の生理 的変化を予測することができる。 [0092] Next, the hybridized DNA chip is imaged with a scanner to obtain an image with a color density corresponding to the expression level of mRNA. Further, based on this image, image analysis software acquires concentration data for each probe region as gene expression data (step P14). [0093] Subsequently, a discrimination criterion is applied to the acquired gene expression data to predict the presence or absence of a physiological change (Step P15), and obtain a prediction result (Step P16). As described above, physiological changes in the living body can be predicted.
[0094] なお、上記方法にお 、て、マーカー遺伝子の選抜方法、判別基準の作成方法、判 別基準に基づく予測方法は、それぞれ独立して実施することができる。たとえば、図 1 に示す判別基準の作成だけを本発明に従って実施し、その判別基準を他の予測方 法に用いたり、予測方法以外に用いることができる。  [0094] In the above method, the marker gene selection method, the discrimination criterion creation method, and the prediction method based on the discrimination criterion can be performed independently. For example, only the discrimination criteria shown in FIG. 1 can be created according to the present invention, and the discrimination criteria can be used for other prediction methods or other than the prediction method.
[0095] また、本発明以外の方法によって作成された判別基準を用いて、図 2に示す予測 方法を実施することが可能である。  [0095] Further, it is possible to implement the prediction method shown in FIG. 2 using the discrimination criterion created by a method other than the present invention.
[0096] さらに、図 1のステップ P1〜P6によって示されるマーカー遺伝子の選抜方法を実 施し、選抜されたマーカー遺伝子に基づき本発明以外の方法によって判別基準を生 成したり、選抜されたマーカー遺伝子を判別基準の生成以外に用いることも可能で ある。  [0096] Further, the method for selecting a marker gene indicated by steps P1 to P6 in Fig. 1 is performed, and based on the selected marker gene, a discrimination criterion is generated by a method other than the present invention, or the selected marker gene is selected. Can be used for purposes other than generating discrimination criteria.
[0097] 2.装置として構築した例  [0097] 2. Example constructed as a device
図 1に示す判別基準の生成および図 2示す生理的変化の予測は、コンピュータを 用いずに実施することができる。しかし、大量のデータ処理を考慮すると、以下に示 すように装置として実現することが好ま 、。  The generation of discrimination criteria shown in Fig. 1 and the prediction of physiological changes shown in Fig. 2 can be performed without using a computer. However, considering a large amount of data processing, it is preferable to implement as a device as shown below.
[0098] 2.1判別基準生成装置 [0098] 2.1 Discrimination criterion generation device
判別基準生成装置の機能ブロック図を図 3に示す。発現量検出手段 22により、ハイ ブリダィゼーシヨン済みの DNAチップ Dl、 D2 - - 'Dnから、各個体ごと遺伝子ごとの 発現量データが得られる。さらに、この発現量データと各個体の生理的変化の有無 データとが組み合わされて、基礎データ生成手段 24により基礎データが生成される  Figure 3 shows a functional block diagram of the discrimination criterion generator. The expression level detection means 22 can obtain expression level data for each gene from the DNA chips Dl and D2 − −Dn that have been hybridized. Furthermore, the basic data is generated by the basic data generation means 24 by combining the expression level data with the physiological change presence / absence data of each individual.
[0099] マーカー選抜手段 26は、第 1生体群と第 2生体群の発現データを比較して、マー カー遺伝子を選抜する。判別基準生成手段 28は、マーカー遺伝子群の発現量デー タに基づいて多変量解析を行い、第 1生体群と第 2生体群を判別する基準を演算す る。これによつて、判別基準 30が記録される。 [0099] The marker selection means 26 selects the marker gene by comparing the expression data of the first biological group and the second biological group. The discrimination criterion generation means 28 performs multivariate analysis based on the expression level data of the marker gene group, and calculates a criterion for discriminating between the first biological group and the second biological group. As a result, the discrimination criterion 30 is recorded.
[0100] 2.1.1コンピュータを用いて構築した例 判別基準生成装置をコンピュータによって実現した場合のハードウェア構成を図 4 に示す。 CPU2には、ディスプレイ 4、スキャナ 6、メモリ 8、 CD— ROMドライブ 10、ハ ードディスク 12が接続されている。スキャナ 6は、ハイブリダ一ゼーシヨンを行った DN Aチップのプローブ領域を画像として読み取るものである。本実施形態では、スキヤ ナ 6は CPU2に接続されており直接的にデータの取り込みが可能となっている。しか し、スキャナ 6で読み取った画像データを、可搬性記録媒体 (CD— RWなど）に記録 し、 CD— ROMドライブ 10から読み取るようにしてもよ!、。 [0100] 2.1.1 Example constructed using a computer Figure 4 shows the hardware configuration when the discrimination criterion generator is realized by a computer. Connected to CPU 2 are display 4, scanner 6, memory 8, CD-ROM drive 10, and hard disk 12. The scanner 6 reads the probe area of the DNA chip on which hybridization has been performed as an image. In the present embodiment, the scanner 6 is connected to the CPU 2 and can directly capture data. However, the image data read by the scanner 6 may be recorded on a portable recording medium (CD-RW, etc.) and read from the CD-ROM drive 10!
[0101] メモリ 8は、 CPU2のワーク領域として用いられる。ノ、ードディスク 12には、オペレー ティングシステム 16および判別基準生成プログラム 18が記録されて 、る。これらプロ グラムは、 CD— ROM14に記録されていたものを、 CD— ROMドライブ 10を介して ハードディスク 12にインストールされたものである。判別基準生成プログラム 18は、ォ ペレ一ティングシステム 16と協働してその機能を発揮する。なお、判別基準生成プロ グラム 18単独で機能するプログラムとしてもよ 、。  [0101] Memory 8 is used as a work area of CPU2. The operating disk 16 and the discriminant reference generation program 18 are recorded on the memory disk 12. These programs are recorded on the CD-ROM 14 and installed on the hard disk 12 via the CD-ROM drive 10. The discrimination criterion generation program 18 performs its function in cooperation with the operating system 16. Note that the discrimination criterion generation program 18 may be a program that functions alone.
[0102] 2.1.2判別基準生成プログラム  [0102] 2.1.2 Discrimination criteria generation program
図 5、図 6に、判別基準生成プログラムのフローチャートを示す。ここでは、一例を具 体的に示すため、予測対象とする生体の生理的変化をアルツハイマー病として説明 する。  5 and 6 show flowcharts of the discrimination criterion generation program. Here, in order to show an example concretely, the physiological change of the living body to be predicted will be described as Alzheimer's disease.
[0103] なお、各個体の皮膚組織から、繊維芽細胞を Neuroscience Letters, 220 9-12(199 6)に記載の方法で単離、培養し、 1試料あたり 300〜 1000万個の繊維芽細胞を得て 、これを生体試料とした。さら〖こ、この繊維芽細胞力も TotalRNAを抽出した。抽出に は、 Rneasy Mini kit(Qiagen, Valencia, CA)を用いることができる。  [0103] From the skin tissue of each individual, fibroblasts were isolated and cultured by the method described in Neuroscience Letters, 220 9-12 (199 6), and 3 to 10 million fibroblasts per sample. This was used as a biological sample. Sarako, this fibroblastic force also extracted TotalRNA. For extraction, Rneasy Mini kit (Qiagen, Valencia, CA) can be used.
[0104] TotalRNAからの cDNAの調製、 cDNAからのラベル化 cRNAの調製、ラベル化 c RNAのフラグメント化、フラグメント化 cRNAと DNAとのハイブリダィズ、ハイブリダィ ズした cRNAの蛍光染色については、特開 2003— 169867に記載の方法を用いる ことができる。なお、この実施形態では、 DNAチップとして、 Aifymetrix社製オリゴヌク レオチド型 DNAチップ GeneChip HG- U133A Arrayを用いた。  Preparation of cDNA from TotalRNA, Labeling from cDNA cRNA Preparation, Labeling cRNA Fragmentation, Fragmentation Hybridization of cRNA and DNA, and fluorescent staining of hybridized cRNA are disclosed in JP 2003- The method described in 169867 can be used. In this embodiment, an oligonucleotide DNA chip GeneChip HG-U133A Array manufactured by Aifymetrix was used as the DNA chip.
[0105] まず、ステップ S1において、 CPU2は、スキャナ 6にセットされた、最初の個体の生 体試料によってハイブリダィゼーシヨンされた DNAチップの画像を読み取る。この画 像は、各遺伝子の発現量に応じた蛍光濃度を有している。次に、 CPU2は、当該画 像の各プローブ領域の蛍光濃度に基づいて、各遺伝子ごとの発現量データを生成 する。これにより発現量データを取得することができる (ステップ S 2)。これらについて も、特開 2003— 169867に記載の方法を用いることができる。また、発現量データ取 得の部分は、ァフィメトリタス社の解析ソフトウェア Microarray Suite version 5.0を用い て実現することができる。 [0105] First, in step S1, the CPU 2 reads an image of a DNA chip that is set in the scanner 6 and hybridized with a biological sample of the first individual. This picture The image has a fluorescence concentration corresponding to the expression level of each gene. Next, CPU2 generates expression level data for each gene based on the fluorescence concentration of each probe region of the image. Thereby, the expression level data can be acquired (step S2). Also for these, the method described in JP-A-2003-169867 can be used. In addition, the expression level data acquisition part can be realized by using analysis software Microarray Suite version 5.0 of Affymetritas.
[0106] さらに、 CPU2は、当該個体についてのアルツハイマー病を発症するか否かのデ ータを取得する（ステップ S3)。なお、アルツハイマー病を発病している力 あるいは 将来発病することが確実な個体を、「アルツハイマー病を発症する」個体として扱った 。これは、キーボードなど（図示せず)から入力されたものであってもよいし、予め各個 体ごとにハードディスク 12に記録されたデータから取得するようにしてもよい。後者の 場合には、各個体ごとに IDを付してデータを記録しておき、 DNAチップの画像読み 取りの際に当該 IDを入力することで対応するデータを取得できるようにしておくとよい [0106] Furthermore, the CPU 2 acquires data on whether or not Alzheimer's disease develops for the individual (step S3). In addition, an individual who has the ability to develop Alzheimer's disease or an individual who is certain to develop it in the future was treated as an individual who “develops Alzheimer's disease”. This may be input from a keyboard or the like (not shown), or may be acquired from data recorded in advance on the hard disk 12 for each individual. In the latter case, it is advisable to record the data by attaching an ID to each individual so that the corresponding data can be obtained by inputting the ID when reading the image of the DNA chip.
[0107] なお、家族性アルツハイマー病病因遺伝子保持者である Swedish変異保持者、 Arc tic変異保持者、プレセ-リン 1遺伝子 H163Y変異保持者をアルッノヽイマ一を発症す る者とした。また、上記病因遺伝子の非保持者をアルツハイマーを発症しない者とし た。したがって、この実施形態では、生理的変化を生じる者の中には現実に生じてい る者だけでなぐ将来生じることが確実視されるものも含む。 [0107] In addition, Swedish mutation holders, Arctic mutation holders, and preserin 1 gene H163Y mutation holders who are familial Alzheimer's disease etiology genes holders were those who developed Arno-Ima. In addition, non-carriers of the above-mentioned pathogenic genes were those who did not develop Alzheimer's. Therefore, in this embodiment, those who undergo physiological changes include those that are surely expected to occur in the future, not just those who actually occur.
[0108] 上記のようにして、対象とした個体につ!ヽて、アルッノ、イマ一発病の有無と各遺伝 子ごとの発現量を基礎データとして得て、ハードディスク 12に記録する (ステップ S4)  [0108] As described above, the target individual is obtained, and the presence or absence of Arnno and Imah disease and the expression level for each gene are obtained as basic data and recorded on the hard disk 12 (step S4).
[0109] 次に、 CPU2は、全ての個体について上記の処理を行ったかどうかを判断する（ス テツプ S5)。未処理の個体があれば、上記ステップ S1〜S5の処理を繰り返す。 Next, CPU 2 determines whether or not the above processing has been performed for all individuals (step S5). If there is an unprocessed individual, the above steps S1 to S5 are repeated.
[0110] 全ての個体について基礎データを記録すれば、ステップ S6以下のマーカー遺伝 子の選抜処理に移る。図 7に、ハードディスク 12に記録された基礎データの一部を示 す。図において、最上欄は個体 IDである。各個体毎に、発症の有無、遺伝子 1の発 現データ、遺伝子 2の発現データ · · '遺伝子 nの発現データが記録される。 [0111] なお、この実施形態において用いた DNAチップ (HG- U133A)は、 22, 283種のプ ローブを有している。したがって、この実施形態において、ハードディスク 12に記録さ れる遺伝子の数 nは 22、 283種である。 [0110] Once the basic data is recorded for all individuals, the process proceeds to the marker gene selection process in step S6 and subsequent steps. Figure 7 shows a part of the basic data recorded on the hard disk 12. In the figure, the top column is the individual ID. For each individual, the presence / absence of onset, gene 1 expression data, gene 2 expression data ··· gene n expression data are recorded. [0111] Note that the DNA chip (HG-U133A) used in this embodiment has 22,283 types of probes. Therefore, in this embodiment, the number n of genes recorded on the hard disk 12 is 22,283.
[0112] 次に、 CPU2は、ハードディスク 12に記録された基礎データに基づいて、マーカー 遺伝子を選抜する。まず、 CPU2は、発現していない遺伝子および発現量の小さい（ シグナルが 44未満)遺伝子を検討外とする。次に、最初の遺伝子を対象遺伝子とし（ ステップ S6)、対象遺伝子について全個体中から最大の発現量と最小の発現量を抽 出する (ステップ S7)。次式に基づき、最大値と最小値の中間値を算出する。  Next, CPU 2 selects a marker gene based on the basic data recorded on hard disk 12. First, CPU2 excludes genes that are not expressed and genes with low expression levels (signal less than 44). Next, the first gene is set as a target gene (step S6), and the maximum expression level and the minimum expression level are extracted from all individuals for the target gene (step S7). Based on the following formula, an intermediate value between the maximum value and the minimum value is calculated.
[0113] 中間値 = (最大値 +最小値) Z2  [0113] Intermediate value = (Maximum value + Minimum value) Z2
次に、この中間値を境として、発現量大のグループと発現量小のグループに分ける（ ステップ S8)。さらに、各個体のアルツハイマー病発症の有無に基づき、 2つのグルー プに分ける (ステップ S9)。このようなグループ分けにより、対象遺伝子の発現量デー タは、図 8に示すように 4つのグループに分けられる。領域 1, 1は「発症あり」かつ「発 現大」の領域、領域 1, 2は「発症あり」かつ「発現小」の領域、領域 2, 1は「発症なし」 かつ「発現大」の領域、領域 2, 2は「発症なし」かつ「発現小」の領域である。  Next, using this intermediate value as a boundary, it is divided into a large expression level group and a low expression level group (step S8). Further, based on the presence or absence of Alzheimer's disease in each individual, it is divided into two groups (Step S9). By such grouping, the expression level data of the target gene is divided into four groups as shown in FIG. Regions 1 and 1 are “onset” and “high expression” regions, regions 1 and 2 are “onset” and “low expression”, regions 2 and 1 are “onset” and “high expression” Regions 2 and 2 are “no onset” and “small expression” regions.
[0114] 続いて、 CPU2は、この遺伝子の発現量が「発症あり」と「発症なし」に関連を持たな い度合い (独立モデル)と、関連を持つ度合い (従属モデル)を算出する。この実施形 態では、アレンのクロスバリデーシヨン (CV)基準により、従属モデルの最大対数尤度 Ldeと、独立モデルの最大対数尤度 Linを、下式に従って算出する (ステップ S10)。 なお、この算出処理部分については、統計解析ソフトウェア「Visual Mining Studio ve r. 3.0」（数理システム社)を用いることができる。  [0114] Subsequently, the CPU 2 calculates the degree that the expression level of this gene is not related to "onset" and "no onset" (independent model) and the degree of relation (dependent model). In this embodiment, the maximum log likelihood Lde of the dependent model and the maximum log likelihood Lin of the independent model are calculated according to the following equation based on Allen's cross validation (CV) standard (step S10). For this calculation processing part, statistical analysis software “Visual Mining Studio ver. 3.0” (Mathematical Systems Inc.) can be used.
[0115] [数 1]  [0115] [Equation 1]
Lde- 7 , 7 , n(iJ)log(n(i,j)-l)-nlog(n-l) Lde-7, 7, n (iJ) log (n (i, j) -l) -nlog (n-l)
[0116] ここで、 nは試料の数 (全個体数）、 n (i,j)は図 8の領域 i, jに入る試料 (個体)の数であ る。 [0116] Here, n is the number of samples (total number of individuals), and n (i, j) is the number of samples (individuals) that fall within regions i and j in FIG.
[0117] [数 2]
Figure imgf000022_0001
[0117] [Equation 2]
Figure imgf000022_0001
i-l ト 1  i-l G 1
[0118] ここで、 n(i)は図 8の i列目の領域に入る試料の数である。 i=lであれば、領域 1, 1と領 域 1, 2に入る試料の数である。 i=2であれば、領域 2, 1と領域 2, 2に入る試料の数で ある。 n(j)は図 8の j列目の領域に入る試料の数である。 j=lであれば、領域 1, 1と領域 2, 1に入る試料の数である。 j=2であれば、領域 1, 2と領域 2, 2に入る試料の数であ る。 Here, n (i) is the number of samples entering the region in the i-th column in FIG. If i = l, then it is the number of samples that fall into regions 1, 1 and regions 1, 2. If i = 2, then it is the number of samples that fall into Regions 2, 1 and Regions 2, 2. n (j) is the number of samples entering the region in the j-th column in FIG. If j = l, it is the number of samples that fall into Region 1, 1 and Region 2, 1. If j = 2, it is the number of samples that fall in Regions 1 and 2 and Regions 2 and 2.
[0119] 最大対数尤度 Ldeおよび最大対数尤度 Linは、発現量と発症が関連する（Lde)Z 関連しない (Lin)という仮定のもっともらしさを示す値である。そこで、この実施形態で は、 CPU2は、下式によって対象遺伝子の CV値を算出するようにした (ステップ S 11)  [0119] Maximum log likelihood Lde and maximum log likelihood Lin are values indicating the plausibility of the assumption that expression level and onset are related (Lde) Z not related (Lin). Therefore, in this embodiment, CPU2 calculates the CV value of the target gene by the following equation (step S11).
[0120] CV値 =Lde—Lin [0120] CV value = Lde—Lin
したがって、 CV値が大きいほど、対象遺伝子の発現量と発症との関連が大きいとい える。 CPU2は、算出した CV値を当該遺伝子と対応付けてハードディスク 12に記録 する。  Therefore, the higher the CV value, the greater the relationship between the expression level of the target gene and the onset. CPU2 records the calculated CV value in the hard disk 12 in association with the gene.
[0121] 次に、 CPU2は、 DNAチップのプローブを構成する全ての遺伝子について CV値 を算出したかどうかを判断する (ステップ S 12)。未算出の遺伝子があれば、次の遺伝 子を対象遺伝子とし (ステップ S 14)、ステップ S7以下を繰り返して実行する。  [0121] Next, CPU 2 determines whether or not CV values have been calculated for all genes constituting the probe of the DNA chip (step S12). If there is an uncalculated gene, the next gene is the target gene (step S14), and step S7 and subsequent steps are repeated.
[0122] 全ての遺伝子について算出した CV値は、図 9に示すようにハードディスク 12に記 録される。  [0122] The CV values calculated for all genes are recorded on the hard disk 12 as shown in FIG.
[0123] 全ての遺伝子について CV値を算出すると、 CV値の大きい遺伝子カゝら所定個をマ 一力一遺伝子として選抜する (ステップ S 13)。この実施形態では、 CV値が 3以上の 遺伝子のうち、上位 200個をマーカー遺伝子として選抜した。なお、マーカー遺伝子 は本実施形態のように CV値と個数を組み合わせて選抜してもよいが、 CV値のみ、 個数のみで選抜するようにしてもよ!ヽ。  [0123] When the CV value is calculated for all genes, a predetermined number of genes having a large CV value are selected as one gene (step S13). In this embodiment, the top 200 genes having a CV value of 3 or more were selected as marker genes. The marker gene may be selected by combining the CV value and the number as in this embodiment, but may be selected only by the CV value or by the number!
[0124] また、 CV値に基づいてマーカー遺伝子を選択する場合には、図 10に示すように、 CPU2は、しきい値とする CV値を変化させたときの、サポートベクターマシン（SVM) による LOOCV(Leave- One- Out Cross Validation)の正答率が最大となる CV値を選 択するようにしてもよい。たとえば、全試料 (全個体)から 1個（1個体)を取り除き、残つ た試料 (個体)っ 、て、選抜したマーカー遺伝子の発現量を用いて SVMによる判別 分析を行い、判別空間上に発症の有無を有する第 1群と第 2群との判別面を得る。取 り除！、た 1個の試料にっ 、ての発現量をもとに判別空間に投影し、正 、判別が行 われるかどうかを判断した。この操作を、取り除く試料を変えて全ての試料について 行う。これにより、正答率を算出する。 [0124] When selecting a marker gene based on the CV value, as shown in Fig. 10, the CPU 2 changes the support vector machine (SVM) when the CV value as a threshold is changed. The CV value that maximizes the correct answer rate of LOOCV (Leave-One-Out Cross Validation) may be selected. For example, one sample (one individual) is removed from all samples (all individuals), and the remaining sample (individual) is subjected to discriminant analysis by SVM using the expression level of the selected marker gene. The discriminant plane between the first group and the second group having the presence or absence of onset is obtained. Remove! Based on the expression level of only one sample, it was projected onto the discriminant space to determine whether or not discrimination was performed correctly. Repeat this procedure for all samples with different samples to be removed. Thereby, the correct answer rate is calculated.
[0125] さらに、これらの操作を、しきい値とする CV値を変化させて行う。これにより、図 10 に示すように、選抜基準である CV値を変えた場合の正答率を得ることができる。図 1 0の場合であれば、 CV値を 3または 4とすればよ!、ことが判明する。  [0125] Furthermore, these operations are performed by changing the CV value as a threshold value. As a result, as shown in Fig. 10, the correct answer rate can be obtained when the CV value, which is the selection criterion, is changed. In the case of Figure 10, it turns out that the CV value should be 3 or 4!
[0126] なお、上記の SVMによる LOOCV交差検証の部分は、統計解析ソフトウェア「R」 および「R」用統計解析パッケージ「el071」（http：〃 www.cran.us.r-project.org/)を用 いて実施することができる。  [0126] The LOOCV cross-validation part by SVM is the statistical analysis software “R” and “R” statistical analysis package “el071” (http: 〃 www.cran.us.r-project.org/) This can be done using.
[0127] 次に、 CPU2は、各マーカー遺伝子の発現量 ε 1、 ε 2 · · · ε ηを下式に基づいて標 準化する (ステップ S 15)。  Next, CPU 2 normalizes the expression levels ε 1, ε 2... Ε η of each marker gene based on the following equation (step S 15).
[0128] [数 3]  [0128] [Equation 3]
ε2-μ2 ε2-μ 2
2=  2 =
On On
[0129] ここで、 μ ί、 ι^ · · · μ ηは、各マーカー遺伝子の発現量 ε 1、 ε 2 · · · ε ηについての 全個体における平均値である。 σ 1、 σ 2 · · · σ ηは、各マーカー遺伝子の発現量 ε 1 、 ε 2 · · · ε ηについての全個体における標準偏差である。本実施形態においては、 η は 200である。 Here, μ ί, ι ^... Μ η is the average value of all the markers for the expression levels ε 1, ε 2. σ 1, σ 2 ··· σ η are standard deviations in all individuals with respect to the expression levels ε 1, ε 2 ··· ε η of each marker gene. In this embodiment, η is 200.
[0130] 続いて、 CPU2は、上記で算出した各マーカー遺伝子の標準化発現量 Dl、 Ό2 · · · Dnについて、主成分分析を行い、第 1主成分 X、第 2主成分 Y、第 4主成分 Ζを算出 する（ステップ S 16)。 [0130] Subsequently, CPU2 calculates the standardized expression level Dl of each marker gene calculated above, Ό2 · · · For Dn, a principal component analysis is performed to calculate a first principal component X, a second principal component Y, and a fourth principal component Ζ (step S16).
[0131] [数 4] 第 1主成分 AT=》 ' Pu - Di  [0131] [Equation 4] 1st principal component AT = >> 'Pu-Di
第 2主成分 y-》 P2i D, Second principal component y->> P2i D,
第 4主成分 Z= 2 P4t Di Fourth principal component Z = 2 P4t Di
i=l  i = l
[0132] ここで、 Pliは第 1主成分の i番目のマーカー遺伝子についての固有ベクトルである。 [0132] Here, Pli is the eigenvector for the i-th marker gene of the first principal component.
P2iは第 2主成分の i番目のマーカー遺伝子についての固有ベクトルである。 P4iは第 4主成分の i番目のマーカー遺伝子についての固有ベクトルである。  P2i is the eigenvector for the i-th marker gene of the second principal component. P4i is the eigenvector for the i-th marker gene of the fourth principal component.
[0133] さらに、 CPU2は、アルツハイマーを発症する第 1個体群の第 1、第 2、第 4主成分と 、アルッノヽイマ一を発症しない第 2個体群の第 1、第 2、第 4主成分に基づいて、線形 判別分析により、第 1群と第 2群を判別するための判別式を算出する。具体的には、 下式の a、 b、 c、 dを算出する。  [0133] Furthermore, CPU2 is the first, second, and fourth principal components of the first population that develops Alzheimer, and the first, second, and fourth major components of the second population that does not develop the Arnno Based on the components, a discriminant for discriminating between the first group and the second group is calculated by linear discriminant analysis. Specifically, a, b, c, and d in the following formula are calculated.
[0134] [数 5]  [0134] [Equation 5]
A = a -X+ b- Y+ c Z+ d A = a -X + b- Y + c Z + d
[0135] したがって、判別式は下式のように表される, [0135] Therefore, the discriminant is expressed as:
[0136] [数 6]
Figure imgf000024_0002
Figure imgf000024_0001
[0136] [Equation 6]
Figure imgf000024_0002
Figure imgf000024_0001
[0137] 上式の値 A力^より大きければアルツハイマー病を発症すると予測され、 0より小さけ ればアルッノ、イマ一病を発症しないと予測することができる。 [0137] The value of the above formula is predicted to develop Alzheimer's disease if it is greater than A force ^, and less than 0 If so, it can be predicted that Arno and Imah disease will not develop.
[0138] なお、上記実施形態では、 3つの主成分を用いている力 2つ以下、 4つ以上の主 成分を用いるようにしてもよい。また、上記実施形態では、第 1、第 2、第 4主成分を用 いているが、これは、第 1、第 2、第 3主成分を用いた場合よりも、第 1、第 2、第 4主成 分を用いた場合の方が予測精度が高力つた力 である。予測対象である生理的変化 によっては、第 1、第 2、第 3主成分を用いた場合の方が予測精度が高い場合も多い 。力かる場合には、第 1、第 2、第 3主成分を用いることが好ましい。  [0138] In the above embodiment, two or less forces using three main components, or four or more main components may be used. In the above embodiment, the first, second, and fourth principal components are used, but this is more than the case where the first, second, and third principal components are used. When the four main components are used, the prediction accuracy is higher. Depending on the physiological change to be predicted, the prediction accuracy is often higher when the first, second, and third principal components are used. In the case where power is applied, it is preferable to use the first, second and third main components.
[0139] 2.2予測装置  [0139] 2.2 Predictor
予測装置の機能ブロック図を図 11に示す。この予測装置には、上記の判別式が判 別基準として記録されて ヽる。  Figure 11 shows the functional block diagram of the prediction device. In this prediction device, the above discriminant is recorded as a discrimination criterion.
[0140] 発現量検出手段 32により、ハイブリダィゼーシヨン済みの DNAチップ Dから、予測 対象である個体の遺伝子ごとの発現量データが得られる。なお、この実施形態では、 マーカー遺伝子に対応するプローブのみを有する DNAチップを用いたが、それ以 外の遺伝子プローブも有するものを用いてもょ 、。  [0140] The expression level detection means 32 obtains the expression level data for each gene of the individual to be predicted from the DNA chip D that has been hybridized. In this embodiment, a DNA chip having only a probe corresponding to a marker gene is used, but a DNA chip having other gene probes may also be used.
[0141] 予測手段 34は、記録された判別式に基づいて数値 Aを算出し、 0より大きければァ ルツハイマー病を発症すると予測する。また、 0より小さければアルッノ、イマ一病を発 症しないと予測する。出力手段 36は、この予測結果をディスプレイ、プリンタなどに出 力する。  [0141] The predicting means 34 calculates a numerical value A based on the recorded discriminant, and predicts that if it is greater than 0, it will develop Alzheimer's disease. Moreover, if it is smaller than 0, it is predicted that Alcno and Imah's disease will not occur. The output means 36 outputs this prediction result to a display, a printer or the like.
[0142] 2.2.1コンピュータを用いて構築した例  [0142] 2.2.1 Example using computer
予測装置をコンピュータによって実現した場合のハードウェア構成を図 12に示す。 CPU2には、ディスプレイ 4、スキャナ 6、メモリ 8、 CD— ROMドライブ 10、ハードディ スク 12が接続されている。スキャナ 6は、ハイブリダィゼーシヨンを行った DN Aチップ のプローブ領域を画像として読み取るものである。本実施形態では、スキャナ 6は CP U2に接続されており直接的にデータの取り込みが可能となっている。しかし、スキヤ ナ 6で読み取った画像データを、可搬性記録媒体 (CD— RWなど）に記録し、 CD- ROMドライブ 10から読み取るようにしてもょ ヽ。  Figure 12 shows the hardware configuration when the prediction device is implemented by a computer. A display 4, a scanner 6, a memory 8, a CD-ROM drive 10, and a hard disk 12 are connected to the CPU 2. The scanner 6 reads the probe area of the DNA chip that has been hybridized as an image. In this embodiment, the scanner 6 is connected to the CPU 2 and can directly capture data. However, the image data read by the scanner 6 may be recorded on a portable recording medium (CD-RW, etc.) and read from the CD-ROM drive 10.
[0143] メモリ 8は、 CPU2のワーク領域として用いられる。ノ、ードディスク 12には、オペレー ティングシステム 16、予測プログラム 17、判別式 19が記録されている。なお、判別式 19は予測プログラム 17のプログラムの一部として記述されて!、てもよ!/、。これらプロ グラムは、 CD— ROM14に記録されていたものを、 CD— ROMドライブ 10を介して ハードディスク 12にインストールされたものである。予測プログラム 17は、オペレーテ イングシステム 16と協働してその機能を発揮する。なお、予測プログラム 17単独で機 會するプログラムとしてちょ 、。 [0143] The memory 8 is used as a work area of the CPU2. The operating disk 16, the prediction program 17, and the discriminant 19 are recorded on the memory disk 12. Discriminant formula 19 is described as part of the program of prediction program 17! These programs are recorded on the CD-ROM 14 and installed on the hard disk 12 via the CD-ROM drive 10. The forecast program 17 performs its function in cooperation with the operating system 16. The forecast program 17 is a program that works alone.
[0144] 2.2.2予測プログラム  [0144] 2.2.2 Prediction Program
図 13に、予測プログラムのフローチャートを示す。ここでは、一例を具体的に示す ため、予測対象とする生体の生理的変化をアルッノ、イマ一病として説明する。  FIG. 13 shows a flowchart of the prediction program. Here, in order to show an example concretely, a physiological change of a living body to be predicted will be described as Arno and Imah's disease.
[0145] なお、予測対象である個体の皮膚組織から、フラグメント cRNAを調製する方法や ハイブリダィズ、 cRNAの蛍光染色については、判別基準生成と同様である。ただし 、 DNAチップとしては、判別基準生成の際に選抜された 200個の遺伝子に対応する プローブのみを有するものを用いた。  [0145] The method for preparing fragment cRNA from the skin tissue of the individual to be predicted, the hybridization, and the fluorescence staining of cRNA are the same as in the generation of the discrimination criterion. However, as the DNA chip, a chip having only probes corresponding to 200 genes selected at the time of generating the discrimination standard was used.
[0146] まず、ステップ S51において、 CPU2は、スキャナ 6にセットされた、予測対象である 個体の生体試料によってハイブリダィゼーシヨンされた DNAチップの画像を読み取 る。この画像は、各マーカー遺伝子の発現量に応じた蛍光濃度を有している。次に、 CPU2は、当該画像の各プローブ領域の蛍光濃度に基づいて、各マーカー遺伝子 ごとの発現量データを生成する。これにより、発現量データを取得することができる（ ステップ S 52)。  [0146] First, in step S51, the CPU 2 reads an image of a DNA chip that is set in the scanner 6 and hybridized with a biological sample of an individual to be predicted. This image has a fluorescence concentration corresponding to the expression level of each marker gene. Next, the CPU 2 generates expression level data for each marker gene based on the fluorescence concentration of each probe region of the image. Thereby, expression level data can be acquired (step S52).
[0147] さらに、 CPU2は、ハードディスク 12に記録されている判別式（上記数式 6)を読み 出し、各マーカー遺伝子の発現量に基づいて、数値 Aを算出する (ステップ S53)。 算出した数値 Aが 0より小さければ、予測対象個体は、アルツハイマー病を発症しな いと予測し、予測結果をノヽードディスク 12に記録する (ステップ S55)。算出した数値 A力 ^以上であれば、予測対象個体は、アルッノヽイマ一病を発症すると予測し、予測 結果をノヽードディスク 12に記録する (ステップ S 56)。  [0147] Furthermore, CPU 2 reads the discriminant (recorded above in Equation 6) recorded on hard disk 12, and calculates numerical value A based on the expression level of each marker gene (step S53). If the calculated numerical value A is smaller than 0, the prediction target individual predicts that Alzheimer's disease will not occur, and records the prediction result on the node disk 12 (step S55). If the calculated numerical value A force is greater than or equal to the predicted value, the prediction target individual predicts that the Arnotnoima disease will develop, and the prediction result is recorded on the node disk 12 (step S56).
[0148] CPU2は、数値 Aおよび予測結果をディスプレイ 4やプリンタ（図示せず)から出力 する（ステップ S 57)。  CPU 2 outputs numerical value A and the prediction result from display 4 or a printer (not shown) (step S 57).
[0149] 3.ネットワークを介しての予測処理  [0149] 3. Prediction processing via network
図 15に、ネットワークを介して行う予測システムの構成を示す。端末装置 (コンビュ ータ） 50は、インターネット 52を介して、サーバ装置 54 (コンピュータ）と通信可能で ある。 Figure 15 shows the configuration of the prediction system performed via the network. Terminal device (Comb 50) can communicate with the server device 54 (computer) via the Internet 52.
[0150] サーバ装置 54のハードウェア構成を図 16に示す。図 12の予測装置とほぼ同様の 構成である力 スキャナ 6が設けられていない。また、インターネット 52を介して端末 装置 50と通信するための通信回路 7が設けられて 、る。  [0150] The hardware configuration of the server device 54 is shown in FIG. The force scanner 6 that has almost the same configuration as the prediction device in FIG. 12 is not provided. Further, a communication circuit 7 for communicating with the terminal device 50 via the Internet 52 is provided.
[0151] 端末装置 50のハードウェア構成を図 17に示す。図 12の予測装置とほぼ同様の構 成である力 インターネット 52を介してサーバ装置 54と通信するための通信回路 7が 設けられている。また、ハードディスク 12には、データ取得プログラム 15が記録され ている。  [0151] The hardware configuration of the terminal device 50 is shown in FIG. A communication circuit 7 is provided for communicating with the server device 54 via the force Internet 52, which has almost the same configuration as the prediction device of FIG. A data acquisition program 15 is recorded on the hard disk 12.
[0152] 予測対象である個体の生体試料についてハイブリダィズした DNAチップ Dを、端 末装置 50のスキャナで読み込む。データ取得プログラム 15により、 CPU2は、図 13 のステップ S51を実行する。画像が取得できると、 CPU2は、この画像データを、通 信回路 7によってサーバ装置 54に送信する。  [0152] The DNA chip D hybridized with respect to the biological sample of the individual to be predicted is read by the scanner of the terminal device 50. With the data acquisition program 15, the CPU 2 executes step S51 in FIG. When the image can be acquired, the CPU 2 transmits this image data to the server device 54 through the communication circuit 7.
[0153] サーバ装置 54の CPU2は、予測プログラム 17にしたがって、図 13のステップ S52 〜S57を実行する。つまり、この画像データから発現量データを取得し、発症の有無 の予測を行う。ステップ S57において、 CPU2は、数値 Aおよび予測結果を、端末装 置 50に送信する。端末装置 50では、これを受けてディスプレイ 4に表示する。  CPU 2 of server device 54 executes steps S 52 to S 57 of FIG. 13 according to prediction program 17. That is, expression level data is acquired from this image data, and the presence or absence of onset is predicted. In step S57, the CPU 2 transmits the numerical value A and the prediction result to the terminal device 50. The terminal device 50 receives this and displays it on the display 4.
[0154] この実施形態によれば、端末装置 50の側に予測プログラムが無くとも、発症の有無 を予測することができる。  [0154] According to this embodiment, the presence / absence of onset can be predicted without a prediction program on the terminal device 50 side.
[0155] なお、上記実施形態では画像データをサーバ装置 54に送信するようにしているが 、端末装置 50において発現量データを得て、これをサーバ装置 54に送信するように してちよい。  In the above embodiment, the image data is transmitted to the server device 54. However, the expression level data may be obtained by the terminal device 50 and transmitted to the server device 54.
[0156] 4. DN Aチップ型予測装置  [0156] 4. DN A chip type prediction device
図 14Aに示すように、 DNAチップ内に予測のための処理回路 42と判定結果を表 示する表示装置 44を組み込んだ予測装置 40を構築することもできる。プローブ領域 46には、マーカー遺伝子に対応するプローブが設けられている。各プローブは、生 体試料と結合すると電気信号を発する。この電気信号は、トランジスタなどで増幅さ れ発現量信号として出力する。この発現量信号は、処理回路 42に与えられる。電子 式 DNAチップの詳細は、 Analytical andBioanalytical Chemistry, 377(3)521-527, 20 03や The Analyst, 130(5), 687-693, 2005などを参照のこと。 As shown in FIG. 14A, a prediction device 40 in which a processing circuit 42 for prediction and a display device 44 for displaying a determination result are incorporated in a DNA chip can be constructed. In the probe region 46, a probe corresponding to the marker gene is provided. Each probe emits an electrical signal when bound to a biological sample. This electrical signal is amplified by a transistor or the like and output as an expression level signal. This expression level signal is given to the processing circuit 42. Electronic See Analytical and Bioanalytical Chemistry, 377 (3) 521-527, 20 03, The Analyst, 130 (5), 687-693, 2005, etc. for details of the expression DNA chip.
[0157] 処理回路 42は、 CPU,メモリを備えており、図 13のステップ S 52力ら S57を実行す るプログラムを有している。処理回路 42の CPUには、 LCDなどによるディスプレイ 44 が接続されており、 CPUはステップ S57にお!/、て数値 Aをディスプレイ 44に表示させ る。なお、判定結果を表示させるようにしてもよい。この DNAチップ型予測装置を用 いれば、簡易に予測を行うことができる。  [0157] The processing circuit 42 includes a CPU and a memory, and has a program for executing steps S52 and S57 in FIG. A display 44 such as an LCD is connected to the CPU of the processing circuit 42, and the CPU displays the numerical value A on the display 44 in step S57! Note that the determination result may be displayed. If this DNA chip type prediction device is used, prediction can be performed easily.
[0158] なお、上記においては処理回路 42に CPUを用いた力 図 14Bに示すように判別 式の演算を実行するハードウェア回路を用いてもょ 、。各プローブからの発現量信 号を AZD変換器（図示せず）によってディジタルデータに変換された発現量データ μ ΐ、 2 · · · μ ηは、マルチプレクサ 60を介して減算器 62に与えられる。一方、定数 データ ε 1、 ε 2 · · · ε η (上記数式 3における平均値)も、マルチプレクサ 64を介して 減算器 62に与えられる。  [0158] Note that, in the above, the power using the CPU for the processing circuit 42 may be a hardware circuit that executes a discriminant operation as shown in Fig. 14B. The expression level data μ 2, 2... Μ η obtained by converting the expression level signal from each probe into digital data by an AZD converter (not shown) is given to the subtractor 62 via the multiplexer 60. On the other hand, constant data ε 1, ε 2... Ε η (average value in the above equation 3) is also given to the subtractor 62 via the multiplexer 64.
[0159] マルチプレクサ 60、 64は、タイミングパルス ΤΡ1 ,ΤΡ2 · · · ΤΡηによって、発現量デ ータ μ ί、 2 · · · ηと、定数データ ε 1、 ε 2 · · · ε ηを切り換えながら減算器 62に与 える。したがって、減算器 62には、順次、発現量データ/ ζ 1と定数データ ε 1の組み 合わせ、発現量データ μ 2と定数データ ε 2の組み合わせ · · · '発現量データ μ ηと定 数データ ε ηの組み合わせが出力される。したがって、減算器 62は、順次、定数デー タ ε 1から発現量データ/ ζ 1を減算する演算、定数データ ε 2から発現量データ； ζ 2を 減算する演算 · · ·定数データ ε ηから発現量データ/ ζ ηを減算する演算を行う。そして 、タイミングノ《ルスに従って、減算結果を、乗算器 66、 68、 70に与える。  [0159] Multiplexers 60 and 64 switch the expression data μ ί, 2 ··· η and constant data ε 1, ε 2 ··· ε η by timing pulses ΤΡ1, ΤΡ2 ··· ΤΡη Apply to subtractor 62. Therefore, the subtracter 62 sequentially includes the combination of the expression level data / ζ 1 and the constant data ε 1 and the combination of the expression level data μ 2 and the constant data ε 2. A combination of ε η is output. Therefore, the subtractor 62 sequentially subtracts the expression level data / ζ 1 from the constant data ε 1, the expression level data from the constant data ε 2; the operation to subtract ζ 2 ··· Expression from the constant data ε η Performs subtraction of quantity data / ζ η. Then, the subtraction result is given to the multipliers 66, 68 and 70 in accordance with the timing rule.
[0160] 乗算器 66は、タイミングパルスに従って、これに P11Z σ 1を乗算する演算、 P12Z σ 2を乗算する演算 · · · Ρ1η/ σ ηを乗算する演算を順次行う（数式 3および数式 4参 照)。その出力は、タイミングパルスに従って加算器 72に与えられる。加算器 72は、 順次送られてくる演算結果を累積的に加算する。したがって、タイミングパルスが TP1 〜ΤΡηまで進むと、加算器 72からは第 1主成分データ Xが出力される。同様に、加算 器 74、加算器 76からは、第 2主成分データ Υ、第 4主成分データ Ζが出力される。  [0160] The multiplier 66 sequentially performs an operation of multiplying this by P11Z σ 1 and an operation of multiplying this by P12Z σ 2 ··· Multiplication of Ρ1η / ση (see Equation 3 and Equation 4). See). The output is given to the adder 72 according to the timing pulse. The adder 72 cumulatively adds the calculation results sent sequentially. Accordingly, when the timing pulse advances from TP1 to ΤΡη, the first principal component data X is output from the adder 72. Similarly, the adder 74 and adder 76 output the second principal component data Υ and the fourth principal component data Ζ.
[0161] 第 1主成分データ Xは乗算器 78にて係数 a (数式 5参照）が乗じられ、第 2主成分デ ータ Yは乗算器 80にて係数 bが乗じられ、第 4主成分データ Zは乗算器 82にて係数 c が乗じられた後、それぞれ、加算器 84にて加算される。したがって、加算器 84より、 数値 Aを得ることができる。 [0161] The first principal component data X is multiplied by a coefficient a (see Equation 5) by a multiplier 78 to obtain the second principal component data. The data Y is multiplied by the coefficient b by the multiplier 80, and the fourth principal component data Z is multiplied by the coefficient c by the multiplier 82 and then added by the adder 84, respectively. Therefore, the numerical value A can be obtained from the adder 84.
[0162] 5.他の DN Aチップ構築例 [0162] 5. Other DN A chip construction examples
上記の DNAチップ型予測装置では、 DNAチップ内に処理回路を内蔵することで In the above DNA chip type prediction device, a processing circuit is built in the DNA chip.
、コンピュータやスキャナのない環境でも、予測を行うことができるというメリットがある。 しかし、処理回路を内蔵することはチップの構造を複雑にし、 DNAチップを高価なも のにする可能性がある。 There is an advantage that prediction can be performed even in an environment without a computer or a scanner. However, incorporating processing circuitry can complicate the chip structure and make the DNA chip expensive.
[0163] そこで、以下の実施形態では、コンピュータや高価なスキャナが無い環境において も予測を行うことができるだけでなぐ比較的安価に製造コストを抑えることのできる D[0163] Therefore, in the following embodiment, the manufacturing cost can be suppressed relatively inexpensively as well as being able to perform prediction even in an environment without a computer or an expensive scanner.
NAチップを示した。 NA chip was shown.
[0164] なお、この実施形態では、第 1主成分のための DNAチップ、第 2主成分のための D NAチップ、第 4主成分のための DNAチップというように、必要とする主成分に対応し た DNAチップを設けて!/、る。  [0164] In this embodiment, the necessary main components such as a DNA chip for the first main component, a DNA chip for the second main component, and a DNA chip for the fourth main component are used. Install a compatible DNA chip!
[0165] この実施形態では、数式 3に代えて、下記数式 8に示すような変換式にて標準デー タ D'l、 D'2 - - 'D'nを取得するようにしている。  In this embodiment, instead of Equation 3, standard data D′ l, D′ 2 − − “D′ n” is obtained by a conversion equation as shown in Equation 8 below.
[0166] [数 8] η, ει  [0166] [Equation 8] η, ει
D'i = ―。ι D'i = ―. ι
Figure imgf000029_0001
Figure imgf000029_0002
Figure imgf000029_0001
Figure imgf000029_0002
[0167] これにより、標準データ D'l、 D'2 - - 'D'nは必ず正の値となる。 [0167] As a result, the standard data D'l, D'2--'D'n are always positive values.
[0168] この実施形態における第 1主成分のための DNAチップのプローブ領域を図 18に 示す。図に示すように、プローブ領域は l〜nまで設けられている。  [0168] FIG. 18 shows the probe region of the DNA chip for the first main component in this embodiment. As shown in the figure, the probe region is provided from l to n.
[0169] さらに、各プローブ領域における RNAプローブの感度は同じではなぐ各プローブ 領域の遺伝子に対応する係数 σ ί、 Pliに対応して、プローブの感度が調整されてい る。この感度調整によって、 ΡΙίΖ σ ίを乗じた量に相当する蛍光濃度が検出されるよ うに調製されている。この調製は、各プローブ領域に設けるプローブ RNAやプロ一 ブ DNAの数を調整することによって行うことができる。なお、蛍光濃度とプローブの 数との関係を予め測定しておいて、プローブの数を決定することが好ましい。 [0169] Furthermore, the sensitivity of the RNA probe in each probe region is not the same. The sensitivity of the probe is adjusted according to the coefficients σ ί, Pli corresponding to the genes in the region. The sensitivity is adjusted so that the fluorescence density corresponding to the amount multiplied by ΡΙίΖ σ 検 出 is detected. This preparation can be performed by adjusting the number of probe RNA or probe DNA provided in each probe region. It is preferable to determine the number of probes by measuring the relationship between the fluorescence concentration and the number of probes in advance.
[0170] 第 2の主成分のための DNAチップ、第 4の主成分のための DNAチップも同様にし て形成される。 [0170] The DNA chip for the second main component and the DNA chip for the fourth main component are formed in the same manner.
[0171] この DNAチップによって予測を行う場合には、ハイブリダィズさせた DNAチップの プローブ領域全体を読み取る蛍光センサがあればょ 、。全体を読み取ることによって [0171] When making predictions using this DNA chip, there should be a fluorescence sensor that reads the entire probe region of the hybridized DNA chip. By reading the whole
、下記数式 9におけるシグマ加算が自動的になされることとなる。 The sigma addition in Equation 9 below is automatically performed.
[0172] [数 9] η [0172] [Equation 9] η
第 1主成分 ΛΓ'=^ ΡΛ · A First principal component ΛΓ '= ^ Ρ Λ
i=l  i = l
n  n
第 2主成分 '= ^ /¾ . Di  Second principal component '= ^ / ¾. Di
· Di· Di
Figure imgf000030_0001
Figure imgf000030_0001
[0173] このようにして、第 1の主成分のための DNAチップ、第 2の主成分のための DNAチ ップ、第 4の主成分のための DNAのそれぞれについて、センサによる読み取り値を 取得して記録しておき、手作業 (電卓などを用いて）によって、これらに下記の数式 1 0を適用すれば、予測判定を行うことができる。 [0173] In this manner, the sensor readings for the DNA chip for the first principal component, the DNA chip for the second principal component, and the DNA for the fourth principal component are each obtained. Predictive judgment can be made by obtaining and recording, and applying the following Equation 10 to these manually (using a calculator or the like).
[0174] [数 10] n [0174] [Equation 10] n
x =χ' - ypn^
Figure imgf000031_0001
x = χ '-ypn ^
Figure imgf000031_0001
n  n
z = z E¾ z = z E¾
[0175] なお、上式では、∑?1 /^7び0を 'から減ずることにょって、 X'を数式 4の Xに変 換している。 Y、Zについても同様である。ここで、∑Pli iZai)、 ΣΡ2ί(^ί/σί) 、 ΣΡ4ί(/ζίΖσί)は、いずれも予め数値として算出しておくことが可能である。したが つて、対応する DNAチップに印刷などの方法によって表示しておくことが好ま 、。 [0175] In the above equation, X 'is converted to X in Equation 4 by subtracting ∑? 1 / ^ 7 and 0 from'. The same applies to Y and Z. Here, ∑Pli iZai), ΣΡ2ί (^ ί / σί), and ΣΡ4ί (/ ζίΖσί) can be calculated in advance as numerical values. Therefore, it is preferable to display on the corresponding DNA chip by printing or other methods.
[0176] 上記のようにして算出した X、 Υ、 Ζを数式 5に適用すれば、予測判定を行うことがで きる。  [0176] By applying X, Υ, and 算出 calculated as described above to Equation 5, prediction determination can be performed.
[0177] また、係数 a、 b、 cも考慮して、蛍光濃度を調整するようにすれば、値 A算出のため の計算処理が容易となる。  [0177] If the fluorescence density is adjusted in consideration of the coefficients a, b, and c, the calculation process for calculating the value A becomes easy.
[0178] この実施形態によれば、コンピュータが無くとも、また、高価なスキャナが無くとも簡 易に予測を行うことができる。 [0178] According to this embodiment, prediction can be easily performed without a computer or without an expensive scanner.
[0179] なお、センサでの読み取りは全体として行うので、図 18に示したように各プローブ領 域を分離しておく必要はない。また、一つの DNAチップ上に、第 1主成分のためのプ ローブ群、第 2主成分のためのプローブ群、第 3主成分のためのプローブ群を設けて ちょい。 [0179] Since reading by the sensor is performed as a whole, it is not necessary to separate each probe area as shown in FIG. Also, provide a probe group for the first principal component, a probe group for the second principal component, and a probe group for the third principal component on one DNA chip.
[0180] 6.その他の実施形態  [0180] 6. Other Embodiments
上記各実施形態では、アルッノヽイマ一病についての判別を行っている力 その他 の前頭側頭型痴呆、痴呆症、パーキンソン病、筋萎縮性側索硬化症、プリオン病な どの中枢神経疾患についての判別にも適用できる。さらに、脳以外の部位において 発症する疾患についての予測の用いることもできる。  In each of the above-described embodiments, the ability to discriminate about Algno-Ima disease and other central nervous system diseases such as frontotemporal dementia, dementia, Parkinson's disease, amyotrophic lateral sclerosis and prion disease. It can also be applied to discrimination. Furthermore, it can also be used to predict diseases that develop in sites other than the brain.
[0181] 上記実施形態では、皮膚組織を採取して!/ヽるが、疾患の発症部位以外の組織であ れば、粘膜組織、血液など皮膚組織以外の組織であってもよい。 [0182] 上記実施形態では「比較解析」としてクロスノくリデーシヨン基準を用いて 、る。ここで 、「比較解析」とは 2群の遺伝子発現データを比較して、群間の遺伝子発現の差異を 評価する解析手法を指す。 [0181] In the above embodiment, the skin tissue is collected and / or beaten. However, it may be a tissue other than the skin tissue such as mucosal tissue or blood as long as it is a tissue other than the site where the disease occurs. [0182] In the above-described embodiment, the cross-reduction criterion is used as the "comparison analysis". Here, “comparison analysis” refers to an analysis method that compares the gene expression data of two groups and evaluates the difference in gene expression between the groups.
[0183] 比較解析には、情報量基準に基づいた比較を行う解析方法を用いることが好ましく 例えば、 t検定、 F検定、％2検定、順位和検定等が挙げられる力 1遺伝子の発現デ ータに適用することで、 2群間の遺伝子発現の差異を評価することができる解析手法 であればこれらに限定されな！、。  [0183] For the comparative analysis, it is preferable to use an analysis method that performs comparison based on the information criterion. For example, t-test, F-test,% 2 test, rank sum test, etc. If it is an analysis method that can evaluate the difference in gene expression between two groups by applying it to the data, it is not limited to these! ,.
[0184] 2群間の遺伝子発現の差異を評価する場合、解析手法によっては、数値パラメータ や分類パターンを設定する必要があるが、これらは当業者であれば適宜選択し、調 整することが可能である。  [0184] When evaluating the difference in gene expression between two groups, it is necessary to set numerical parameters and classification patterns depending on the analysis method, but those skilled in the art can select and adjust them appropriately. Is possible.
[0185] また、解析手法はひとつの解析手法により構成されるものだけではなぐ複数の解 析手法により構成されるものでもよい。複数の解析手法の構成は、例えば、複数の解 析手法によって得られたそれぞれの解析結果を総合して、最終的な解析結果とする ような並列的な構成であってもよ!/ヽし、ある解析手法で得られた解析結果を変数とし て、さらに別の解析手法を適用して得られた解析結果を最終的な解析結果とするよう な直列的な構成であってもよ ヽ。  [0185] Further, the analysis method may be constituted by a plurality of analysis methods rather than only one analysis method. The configuration of multiple analysis methods may be, for example, a parallel configuration in which the analysis results obtained by the multiple analysis methods are combined into a final analysis result! A serial configuration may be used in which an analysis result obtained by one analysis method is used as a variable, and an analysis result obtained by applying another analysis method is used as a final analysis result.
[0186] 比較解析により得られる、遺伝子発現と生理的変化を誘起する要素との関連の強さ は、例えば、 p値や統計量、あるいは発現シグナルの平均値や中央値、分散等の比 や差等として表される場合があるが、群間の遺伝子発現の差異を連続量や離散量、 級数等により評価することが可能であれば、これらに限定されない。  [0186] The strength of the relationship between gene expression and factors that induce physiological changes obtained by comparative analysis is, for example, the ratio of p-values and statistics, or the mean, median, and variance of expression signals, Although it may be expressed as a difference, etc., it is not limited to these as long as the difference in gene expression between groups can be evaluated by a continuous amount, a discrete amount, a series, or the like.
[0187] また生体は通常、ある生理的変化を誘起する要素を有する生体群と、そのような要 素を有しない生体群等の 2群に分類可能な生体であるが、両群の中間的な要素を保 持している生体群が 1群あるいは複数群存在する場合は、個々の群間の遺伝子発現 の差異を別個に比較解析により評価し、それぞれの群間で、遺伝子発現と該する生 理的変化との関連の強さを評価することで、それぞれの群間の生理的変化に対応し た生体の生理的変化予測マーカー遺伝子を選抜することが可能である。  [0187] In addition, the living body is usually a living body group that can be classified into two groups, such as a living body group having an element that induces a physiological change and a living body group having no such element. When there are one or more biological groups that hold various elements, the difference in gene expression between individual groups is evaluated separately by comparative analysis, and gene expression and By evaluating the strength of the association with the physiological change, it is possible to select a biological physiological change prediction marker gene corresponding to the physiological change between each group.
[0188] 生体の生理的変化、例えば疾患の発症等の予測マーカー遺伝子には、上述の遺 伝子の発現と生体の生理的変化を誘起する要素との関連の大きさが上位である遺伝 子を選抜する。 [0188] For predictive marker genes such as physiological changes in living organisms, for example, the onset of disease, genetics with the highest magnitude of association between the above-described gene expression and factors that induce physiological changes in living organisms. Select a child.
[0189] 具体的には、遺伝子発現と生体の生理的変化を誘起する要素との関連の大きさに 一定の基準、例えば、 p値であれば、 0.05未満等を設定し、設定された基準に合致す る遺伝子を選抜することができる。生体の生理的変化の発現予測マーカー遺伝子の 選抜のための遺伝子発現と生理的変化を誘起する要素との関連の大きさの基準、及 び選抜される遺伝子の数は限定されず、当業者が適宜選択し、調整することが可能 である。  [0189] Specifically, a certain standard is set for the magnitude of the relationship between gene expression and an element that induces physiological changes in living organisms. Genes that match can be selected. The criteria for the magnitude of the relationship between gene expression for selection of a marker for predicting the expression of physiological changes in living organisms and the factors that induce physiological changes, and the number of genes to be selected are not limited. It is possible to select and adjust as appropriate.
[0190] 上記実施形態において「情報量基準」とは、変量と 2群を分類する要素と関連の大 ききを評価する基準であり、個々の遺伝子の発現と生理的変化を誘起する要素との 関連の大きさを評価するために用いられる。  [0190] In the above embodiment, the "information criterion" is a criterion for evaluating the magnitude of association between a variable and an element that classifies the two groups, and an expression of an individual gene and an element that induces a physiological change. Used to evaluate the magnitude of the association.
[0191] 例えば、上記実施形態でマーカー遺伝子を選抜する場合には、情報量基準を用 いて個々の遺伝子の発現と生理的変化を誘起する要素との関連の強さを評価するこ とがでさる。 [0191] For example, in the case of selecting a marker gene in the above-described embodiment, it is possible to evaluate the strength of the relationship between the expression of each gene and an element that induces a physiological change using an information criterion. Monkey.
[0192] 具体的には、個々の遺伝子毎に、生体を発現量の大きい群と発現量の小さい群の 2群に分類し、生理的変化を誘起する要素を有する群と有しない群の分類と併せて、 それぞれの分類基準に合致する生体の数を収めた 2行 X 2列の分割表を作成する。  [0192] Specifically, for each gene, the living organisms are classified into two groups: a group with a high expression level and a group with a low expression level, and a group with and without an element that induces physiological changes. In addition, a 2-row by 2-column contingency table containing the number of organisms that meet each classification criterion is created.
[0193] 生体を発現量の大きい群と発現量の小さい群の 2群に分類する方法には、平均値 より大きいか否かにより分類する方法や、最大値と最小値との間を 2等分することで分 類する方法、％ 2検定を用いる方法等が挙げられるが、これらに限定されない。  [0193] The methods for classifying living organisms into two groups, a group with a high expression level and a group with a low expression level, are classified according to whether or not the average value is greater than the average value, and the second between the maximum value and the minimum value, etc. Examples include, but are not limited to, a method for classification by classification, a method using% 2 test, and the like.
[0194] 個々の遺伝子における、生理的変化を誘起する要素を有する群と有しない群との 間の発現量の違いは、生体が上述の 2つの基準により分類されるパターン力 これら 2つの分類基準が何らかの関連を有すると仮定した場合の統計モデル (従属モデル) と、何の関連も有しな 、と仮定した場合の統計モデル (独立モデル)の何れによりょく 適合しているかを比較することにより評価することができ、従属モデルによりょく適合 する遺伝子ほど、発現と生理的変化を誘起する要素との関連の大きい遺伝子となる  [0194] In each gene, the difference in the expression level between the group with and without the element that induces physiological change is due to the pattern power by which the organism is classified according to the above two criteria. Compare whether the statistical model (subordinate model) is assumed to have some relationship or the statistical model (independent model) if it has no relationship. The genes that are more compatible with the subordinate model are more closely related to the factors that induce expression and physiological changes.
[0195] 具体的には、従属モデルへの適合度を表す情報量基準と、独立モデルへの適合 度を表す情報量基準の大小を比較することで、個々の遺伝子の発現と生理的変化 を誘起する要素との関連の大きさを評価することができる。 [0195] Specifically, by comparing the amount of information that represents the degree of fitness for the dependent model and the amount of information that represents the degree of fitness for the independent model, the expression and physiological changes of individual genes are compared. It is possible to evaluate the magnitude of the relationship with the element that induces.
[0196] 従属モデルへの適合度を表す情報量基準と、独立モデルへの適合度を表す情報 量基準の大小の比較は、例えば、比や差をとることにより可能であるが、連続量や離 散量、あるいは級数等により評価することが可能であれば、これらに限定されない。  [0196] The comparison of the information criterion that represents the fitness to the dependent model and the information criterion that represents the fitness to the independent model can be made, for example, by taking a ratio or difference. However, the present invention is not limited to these as long as it can be evaluated by the amount of diffusion or series.
[0197] 情報量基準として、赤池の情報量基準、ベイズの情報量基準、 Minimum Descriptio n Length (MDL)基準または、アレンのクロスノくリデーシヨン基準等が挙げられる。好ま しくは、アレンのアレンのクロスバリデーシヨン基準が挙げられる。  [0197] As the information amount criterion, Akaike's information amount criterion, Bayesian information amount criterion, Minimum Description Length (MDL) criterion, or Allen's cross-reduction criterion, etc. may be mentioned. Preferably, Allen's Allen's Cross Validation Standard is mentioned.
[0198] 上記実施形態における「多変量解析」とは、複数の変量を同時に解析する統計解 析手法の総称であり、複数の遺伝子の発現データを同時に解析する解析手法を指 す。  [0198] "Multivariate analysis" in the above embodiment is a general term for statistical analysis methods that simultaneously analyze a plurality of variables, and refers to an analysis method that simultaneously analyzes expression data of a plurality of genes.
[0199] 「多変量解析」として、主成分分析、因子分析、自己組織化地図、クラスター分析、 判別分析、重回帰分析及び正準相関分析等の解析手法が挙げられるが、複数の遺 伝子の発現データに適用することで、 2群間の遺伝子発現を判別することができる解 析手法であれば、これらに限定されない。  [0199] "Multivariate analysis" includes analysis methods such as principal component analysis, factor analysis, self-organizing map, cluster analysis, discriminant analysis, multiple regression analysis, and canonical correlation analysis. Any analysis technique can be used as long as it can discriminate gene expression between the two groups by applying to the above expression data.
[0200] 多変量解析を用いて 2群間の遺伝子発現を判別する場合、解析手法によっては、 数値パラメータや分類パターンを設定する必要があるが、これらは当業者であれば 適宜選択し、調整することが可能である。  [0200] When determining gene expression between two groups using multivariate analysis, it may be necessary to set numerical parameters and classification patterns depending on the analysis method, but those skilled in the art can select and adjust them appropriately. Is possible.
[0201] 上述の解析手法はひとつの解析手法により構成されるものだけではなぐ複数の解 析手法により構成されるものでもよい。複数の解析手法の構成は、例えば、複数の複 数の解析手法によって得られたそれぞれの解析結果を総合して、最終的な解析結 果とするような並列的な構成であってもよぐある解析手法で得られた解析結果を変 数として、さらに別の解析手法を適用して得られた解析結果を最終的な解析結果と するような直列的な構成であってもよ 、。  [0201] The analysis method described above may be constituted by a plurality of analysis methods, not just those constituted by one analysis method. The configuration of multiple analysis methods may be, for example, a parallel configuration in which each analysis result obtained by multiple analysis methods is combined into a final analysis result. It may be a serial configuration in which an analysis result obtained by one analysis method is a variable, and an analysis result obtained by applying another analysis method is a final analysis result.
[0202] 多変量解析により定められる、 2群を判別する基準は、一方の群の特徴を表す関係 式と他方の群の特徴を表す関係式と!ヽぅ形で得られる場合や、一方の群と他方の群 との境界を表す点、曲線、直線、曲面、平面及び超平面等として得られる場合等があ る力 2群の差異を示すことが可能であり、判別基準が表される空間に、個々の生体 を、その遺伝子発現データを用いて投影することが可能であればこれらに限定される ものではない。 [0202] The criteria for discriminating the two groups determined by multivariate analysis are the relational expression representing the characteristics of one group, the relational expression representing the characteristics of the other group, and the! Forces that may be obtained as points, curves, straight lines, curved surfaces, planes, hyperplanes, etc. that represent the boundary between one group and the other group. If it is possible to project individual organisms in space using their gene expression data, it is limited to these. It is not a thing.
[0203] また本発明の予測方法をを適用する生体は通常、ある生理的変化を発現する要素 を有する生体群と、そのような要素を有しない生体群等の 2群に分類可能な生体であ るが、両群の中間的な要素を保持している生体群力 S1群あるいは複数群存在する場 合は、個々の群間の遺伝子発現を別個に多変量解析により判別し、それぞれの群間 で、該する要素を有する生体群と、そのような要素を有しない生体群等との判別基準 を定めることで、それぞれの群間の生理的変化に対応した判別基準を得ることが可 能である。  [0203] In addition, a living body to which the prediction method of the present invention is applied is usually a living body that can be classified into two groups, a living body group having an element that expresses a certain physiological change and a living body group having no such element. However, when there are biological group forces S1 or multiple groups that hold intermediate elements between the two groups, the gene expression between the individual groups is determined separately by multivariate analysis, and each group It is possible to obtain discrimination criteria corresponding to physiological changes between groups by defining discrimination criteria between living body groups that have such elements and living body groups that do not have such elements. It is.
[0204] 上記実施形態では、多変量解析の具体例として、「主成分分析および判別分析」を 用いて説明したが、本発明の多変量解析の手法は前記に限られるものではない。  [0204] In the above embodiment, “principal component analysis and discriminant analysis” has been described as a specific example of multivariate analysis. However, the multivariate analysis method of the present invention is not limited to the above.
[0205] 上記実施形態における「主成分分析」は、複数の変量カゝら合成された新たな変量 である主成分を用いて、個々の試料間の関連を特徴づける解析手法であり、ある生 理的変化を誘起する要素を有する生体群と、そのような要素を有しない生体群を、よ り明瞭に判別することのできる変量を得るために用いられる。  [0205] "Principal component analysis" in the above embodiment is an analysis method for characterizing the relationship between individual samples using a principal component that is a new variable synthesized from a plurality of variable parameters. It is used to obtain a variable that can more clearly discriminate between a biological group having an element that induces a physical change and a biological group having no such element.
[0206] 主成分分析を用いて生体の生理的変化の発現を予測する場合には、まず、個々の 生体について、 2群を明瞭に判別できる主成分を得る。解析に用いた遺伝子数と同 数の主成分を得ることができるが、通常は比較的上位の主成分を数個から十数個を 取得すればよい。次に、これらの主成分から、 2群を明瞭に判別可能なものを選択し 、 2群を判別するための変量とする。選択される主成分の数は限定されない。  [0206] When predicting the development of physiological changes in a living body using principal component analysis, first, for each living body, a principal component that can clearly distinguish the two groups is obtained. Although the same number of principal components as the number of genes used in the analysis can be obtained, it is usually sufficient to obtain several to a dozen or so of the higher-order principal components. Next, from these principal components, the one that can clearly discriminate the two groups is selected and used as a variable for discriminating the two groups. The number of main components selected is not limited.
[0207] 王成分分析 ίま Principal component Analysis (1986、 bpnnger- Verlag, Berlinノに己 載の方法に従って行うことができ、市販の統計解析ソフトウェア例えば「Spotfire Ver. 7.2」、 Spotfire DecisionSite (Spotfire社製）等で実行可能である。  [0207] Wang Component Analysis ί Principal component Analysis (1986, bpnnger-Verlag, Berlin) ) Or the like.
[0208] 上記実施形態における「線形判別法」とは、複数の変量を用いて 2群の試料の間の 境界を得る解析方法であり、生体がある生理的変化を将来起こすのか否かを判別す る基準とする、該する生理的変化を誘起する要素を有する生体群と、そのような要素 を有しな 、生体群との境界を定めるために用いられる。  [0208] The "linear discriminant method" in the above embodiment is an analysis method for obtaining a boundary between two groups of samples using a plurality of variables, and discriminates whether or not a biological change will occur in the future. It is used to define a boundary between a living body group having an element that induces the physiological change as a reference and a living body group having no such element.
[0209] 線形判別法を用いて生体の生理的変化を予測する場合には、上述の主成分分析 により得られ、 2群を判別するための変量として選択された主成分に対して線形判別 法を適用し、 2群を判別する基準となる点、直線、平面または超平面を得ることができ る。 [0209] When predicting physiological changes in a living body using a linear discriminant method, linear discrimination is performed on the principal components selected as variables for discriminating between the two groups obtained by the principal component analysis described above. Applying the method, it is possible to obtain points, straight lines, planes, or hyperplanes that serve as criteria for distinguishing the two groups.
[0210] 上記実施形態では、アレンのクロスバリデーシヨン基準を用いている力 その他の情 報量基準を用いてもよい。たとえば、赤池の情報量基準、ベイズの情報量基準、 Mini mum Description Length(MDL)基準を用いてもよい。ここで、情報量基準とは、変量と 2群を分類する要素との関連の大きさを評価する基準をいう。また、従属モデルへの 適合度を表す情報量基準と、独立モデルへの適合度を表す情報量基準の大小の比 較は、たとえば、比や差をとることにより可能であるが、連続量や離散量、あるいは級 数などによって評価してもよい。また、情報量基準以外の比較解析を用いてもよい。 たとえば、 t検定、 F検定、 c 2検定、順位和検定など、 1遺伝子の発現データに適用 することで、 2群間の遺伝子発現の差異を評価できる解析手法を用いることができる。 [0210] In the above embodiment, force and other information criterion using Allen's cross-validation criterion may be used. For example, the Akaike information criterion, Bayesian information criterion, and Minimum Description Length (MDL) criterion may be used. Here, the information criterion is a criterion for evaluating the magnitude of the relationship between the variable and the elements that classify the two groups. In addition, it is possible to compare the information criterion that indicates the degree of fitness with the dependent model and the information criterion that indicates the degree of fitness with the independent model, for example, by taking a ratio or a difference. Evaluation may be made using discrete quantities or series. Further, a comparative analysis other than the information amount criterion may be used. For example, an analysis method that can evaluate the difference in gene expression between two groups by applying to the expression data of one gene such as t test, F test, c 2 test, rank sum test, etc. can be used.
[0211] また、解析手法は、複数の解析手法を組み合わせたものであってもよい。  [0211] Further, the analysis method may be a combination of a plurality of analysis methods.
[0212] 7.実施例  [0212] 7. Examples
実施例 1  Example 1
[0213] Swedish変異、 Arctic変異及びプレセ二リン 1遣伝子 H163Y変異保持者皮膚組織 繊維芽細朐を用いたアルツハイマー病発症予沏 Iマーカー遺伝子の撰  [0213] Swedish mutation, Arctic mutation and presenilin 1 gene H163Y mutation carrier skin tissue Alzheimer's disease onset using fibroblasts I marker gene mutation
30人から皮膚組織線維芽細胞の提供を受け、発症予測マーカー遺伝子の選抜を 行った。資料提供者の内訳は、家族性アルツハイマー病病因遺伝子保持者として、 S wedish変異保持者 7人、 Arctic変異保持者 7人及びプレセ二リン 1遺伝子 H163Y変異 保持者 5人、家族性アルッノ、イマ一病病因遺伝子非保持者として、上述の病因遺伝 子保持者の兄弟あるいは姉妹であって病因遺伝子を非保持の 11人であった。  We received skin tissue fibroblasts from 30 individuals and selected onset predictive marker genes. The breakdown of the donors is as follows. Eleven non-pathogenic genes were siblings or sisters of the above-described pathogenic genes and no pathogenic genes.
[0214] 提供された皮膚組織生体試料から、線維芽細胞を Neuroscience Letters, 220 9-12  [0214] From the provided biological sample of skin tissue, fibroblasts were obtained from Neuroscience Letters, 220 9-12.
(1996)に記載の方法で単離、培養し、 1試料あたり 300〜1000万個の線維芽細胞を 得た。  (1996), and the cells were isolated and cultured to obtain 3 to 10 million fibroblasts per sample.
[0215] Rneasy Mini Kit (Qiagen, Valencia, CA)を用い、添付の手順に従って線維芽細胞 力 Total RNAを抽出した。  [0215] Using a Rneasy Mini Kit (Qiagen, Valencia, Calif.), Fibroblast force total RNA was extracted according to the attached procedure.
[0216] 線維芽細胞より抽出した Total RNAを用いて個々の遺伝子の発現量を測定した。 [0216] The expression level of each gene was measured using Total RNA extracted from fibroblasts.
遺伝子発現量の測定には Aifymetrix社製オリゴヌクレオチド型 DNAチップ GeneChip HG-U133A Arrayを使用した。具体的には、 Total RNAからの cDNAの調製、該 cDNA からラベル化 cRNAの調製、ラベル化 cRNAのフラグメント化、フラグメント化 cRNAと DN Aチップとのハイブリダィズ、ハイブリダィズした cRNAの蛍光染色、 DNAチップ上の蛍 光の読み取り、及び遺伝子発現量の測定、の順に、特開 2003— 169687に記載の方 法と同様の方法で行った。最終的に、遺伝子発現量は HG-U133A Arrayの蛍光ィメ ージを解析ソフトウェア Microarray Suite version 5.0より解析することにより得た。 Aifymetrix oligonucleotide type DNA chip GeneChip for gene expression measurement HG-U133A Array was used. Specifically, preparation of cDNA from total RNA, preparation of labeled cRNA from the cDNA, fragmentation of labeled cRNA, fragmentation Hybridization of cRNA and DNA chip, fluorescent staining of hybridized cRNA, on DNA chip The method similar to the method described in Japanese Patent Application Laid-Open No. 2003-169687 was performed in the order of reading the fluorescence of the sample and measuring the gene expression level. Finally, the gene expression level was obtained by analyzing the fluorescence image of the HG-U133A Array using the analysis software Microarray Suite version 5.0.
[0217] このようにして得られた発現量データを図 19a〜図 19fに示す。図において、最上 欄は個体 IDであり、最左欄は遺伝子 IDである。図において、遺伝子 IDは、ァフィリメ トリタス社のプローブセット番号にて示している。図 19a〜図 19cはアルツハイマーを 発病する個体 (病因遺伝子保持者)の各遺伝子発現量であり、図 19d〜図 19fはァ ルツハイマーを発病しな ヽ個体 (病因遺伝子非保持者)の各遺伝子発現量である。 なお、この実施形態では、遺伝子の種類は 22,238種ある力 図においてはマーカー 遺伝子のデータのみを示し、他の遺伝子については省略した。  [0217] The expression level data thus obtained are shown in Figs. 19a to 19f. In the figure, the top column is the individual ID, and the leftmost column is the gene ID. In the figure, the gene ID is indicated by a probe set number of Affymetritas. Figures 19a to 19c show the gene expression levels of individuals with Alzheimer's disease (with etiological gene holder), and Figs. 19d to 19f show the gene expression levels of individuals with no Alzheimer's disease (with no etiological gene holder). Amount. In this embodiment, 22,238 kinds of genes have only the marker gene data in the power diagram, and the other genes are omitted.
[0218] HG-U133A Arrayで測定可能な 22,238種のヒト RNA量を計測するプローブセットの うち、 MAS 5.0による解析で、発現していない（Absent)と判断された 9,752個及び発現 シグナルの値が 44に満たない 1,348個を除いた 11, 138個のプローブセットについて比 較解析を行い、個々の遺伝子の発現と家族性アルツハイマー病病因遺伝子の有無 との関連の強さを評価した。  [0218] Among the probe sets that measure the amount of 22,238 human RNAs that can be measured with the HG-U133A Array, 9,752 that were determined to be non-expressed (Absent) by analysis with MAS 5.0 and the value of the expression signal was A comparative analysis was performed on 11,138 probe sets, excluding 1,348, which was less than 44, to evaluate the strength of the relationship between the expression of individual genes and the presence or absence of familial Alzheimer's disease etiology genes.
[0219] 評価の基準には、情報量基準のひとつ、アレンのクロスバリデーシヨン基準（CV基 準)を用いい、発現量の大きい群と発現量の小さい群との分類は、最大値と最小値と の間を 2等分することにより行った。  [0219] One of the information criteria, Allen's cross-validation criterion (CV criterion), was used as the evaluation criterion. The classification of the group with high expression level and the group with low expression level is the maximum and minimum values. This was done by dividing the value into two equal parts.
[0220] 11, 138個のプローブセットについて、家族性アルツハイマー病病因遺伝子の有無と 発現量の大小に基づいて、図 8に示す 2 X 2分割表を作成した。  [0220] Based on the presence and absence of familial Alzheimer's etiology gene and the level of expression of 11,138 probe sets, the 2 X 2 contingency table shown in Fig. 8 was created.
[0221] 11, 138個のプローブセットついて、分割表の各区画に収められる試料数から、従属 モデルの CV基準と独立モデルの CV基準を以下の計算式に基づ 、て計算した。 CV 基準は市販の統計解析ソフトウェア「Visual Minng Studio ver. 3.0」（数理システム）を 用いて行った。  [0221] For the 11,138 probe sets, the CV standard of the dependent model and the CV standard of the independent model were calculated from the number of samples stored in each section of the contingency table based on the following formula. CV standards were performed using commercially available statistical analysis software “Visual Minng Studio ver. 3.0” (mathematical system).
[0222] ·従属モデルの CV基準（L) [0223] [数 1] [0222] · CV standard of dependent model (L) [0223] [Equation 1]
Lde=乙 _t n(i,j)l g(n(i,j)-l)-nlog(n-l) Lde = B _t n (i, j) l g (n (i, j) -l) -nlog (n-l)
μΐ ϊΊ  μΐ ϊΊ
[0224] ここにおいて nは試料の数、 n (i,j)は i行目かつ j列目の区画に収められた試料数を 表す。 [0224] Here, n is the number of samples, and n (i, j) is the number of samples stored in the section of the i-th row and the j-th column.
[0225] ·独立モデルの CV基準（L)  [0225] · CV standard for independent model (L)
[0226] [数 2]
Figure imgf000038_0001
[0226] [Equation 2]
Figure imgf000038_0001
[0227] ここにおいて nは試料の数、 n(i)は i行目に、 n (j)は j列目に収められた試料数を表す [0227] where n is the number of samples, n (i) is the number of samples in the i-th row, and n (j) is the number of samples in the j-th column
[0228] 11, 138個のプローブセットついての従属モデルの CV基準及び独立モデルの CV基 準から「従属モデルの CV基準 独立モデルの CV基準」が 3.0より大きいプローブセッ ト 200個を選抜し、アルツハイマー病発症予測マーカー遺伝子セットとした。 [0228] From the CV criteria of the dependent model and the independent model CV criteria for 11,138 probe sets, we selected 200 probe sets whose "CV criteria of the dependent model CV criteria of the independent model" is greater than 3.0. The marker gene set for predicting the onset of Alzheimer's disease was used.
[0229] マーカー遺伝子に対応するプローブセットを図 20a、図 20bに示す。図中、項目 A はプローブセットの識別番号を表し、対応する遺伝子の情報はァフィメトリタス社 (http ：〃 www.alfymetrix.com/index.afik)より入手可能である。また項目 Bは「従属モデルの CV基準 独立モデルの CV基準」の値を表す。  [0229] Probe sets corresponding to the marker genes are shown in FIGS. 20a and 20b. In the figure, item A represents the probe set identification number, and information on the corresponding gene is available from Affymetritas (http://www.alfymetrix.com/index.afik). Item B represents the value of “CV standard for dependent model, CV standard for independent model”.
[0230] 上述の 200遺伝子につ!、て、サポートベクターマシン（SVM)による Leave-One-Out 交差検証を行った。具体的には、 30個の試料から 1個を取り除き、残った 29個の試料 につ!/、て、 200プローブセットの発現シグナルの値を用いて SVMによる判別分析を行 Vヽ、判別空間上にに家族性アルツハイマー病病因遺伝子を有する群と無有しな!/ヽ群 との判別面を得た（図 21参照)。取り除 、た 1個の試料をその発現シグナルの値をも とに判別空間に投影し、家族性アルツハイマー病病因遺伝子の有無が正しく判別さ れるかどうかを検証した。この検証を 30個の試料全てについて行ったところ、 30人中 2 9人 (96.7%)の試料提供者の家族性アルッノ、イマ一病病因遺伝子の有無が正しく分 類された。 SVMによる Leave-One-Out交差検証は、統計解析ソフトウェア「R」及び「R 」用統計解析パッケージ「el071」 (http://cran.us.r-project.org/)を用いて行った。 実施例 2 [0230] Leave-One-Out cross-validation was performed on the 200 genes described above using a support vector machine (SVM). Specifically, remove one from 30 samples, and perform the discriminant analysis by SVM using the value of the expression signal of 200 probe sets for the remaining 29 samples! Above, we obtained a discriminant surface between the group with and without the familial Alzheimer's disease etiology gene (see Fig. 21). Then, only one sample was projected onto the discriminant space based on the value of the expression signal, and it was verified whether the presence or absence of the gene causing the familial Alzheimer's disease was correctly discriminated. When this verification was performed on all 30 samples, the presence or absence of genes related to familial Arno and Imah's pathogenesis of 29 (96.7%) of the 30 donors was correctly identified. It was similar. Leave-One-Out cross-validation by SVM was performed using statistical analysis software “R” and statistical analysis package “el071” (http://cran.us.r-project.org/) for “R”. Example 2
[0231] Swedish 1 Arctic 虽及びプレセ二リン 1遣伝早 HI 63Y栾虽保持者皮膚鉬.織線 維莽細qにおける遣伝早 現解析により;巽 されたアルツハイマー病 症予沏 Iマ 一力一遣伝早 用いたアルツハイマー病の 症予測式の設定  [0231] Swedish 1 Arctic 虽 and presenilin 1 early transmission HI 63Y 栾 虽 holder skin 鉬. Early transmission in woven line fibrosis q; analysis of Alzheimer's disease prognosis I Establishing a prediction formula for Alzheimer's disease
実施例 1により選抜されたアルッノ、イマ一病発症予測マーカー遺伝子を用いて、将 来アルツハイマー病を発症する力否かの判別基準となる発症予測式を設定した。  Using the Arnno and Imah 1 disease onset prediction marker genes selected according to Example 1, an onset prediction formula serving as a criterion for determining whether or not to develop Alzheimer's disease in the future was set.
[0232] 30人の試料提供者のアルッノ、イマ一病発症予測マーカー遺伝子セットである 200 プローブセットの発現シグナル値を用いて主成分分析を行い、第一、第二、第四の 各主成分の主成分を用いることで、家族性アルツハイマー病病因遺伝子保持者と該 遺伝子非保持者を一平面で区分できることを確認した (図 21参照)。主成分分析は 統計解析ソフトウェア「Spotfire Ver. 7.2」（Spotfie社）を用いて行った。  [0232] Principal component analysis was performed using the expression signal values of the 200 probe sets, which are the gene markers for predicting the onset of Arnno and Imah's disease of 30 sample providers, and the first, second and fourth principal components were analyzed. By using the main component, it was confirmed that a person with familial Alzheimer's disease etiology gene can be distinguished from a person who does not have the gene on one plane (see Fig. 21). Principal component analysis was performed using statistical analysis software “Spotfire Ver. 7.2” (Spotfie).
[0233] そこでこれら第一、第二、第四の各主成分の主成分に対して線形判別分析を適用 し、以下に示す家族性アルツハイマー病病因遺伝子保持群と該遺伝子非保持群の 境界を示す発症予測式を得た。線形判別分析は統計解析ソフウェア「R」 (http://cra n. us. r-project.org/)を用いてィ丁つた。  [0233] Therefore, linear discriminant analysis is applied to the principal components of the first, second, and fourth principal components, and the boundary between the familial Alzheimer's disease pathogenic gene holding group and the gene non-holding group shown below is defined. The onset prediction formula shown was obtained. Linear discriminant analysis was performed using statistical analysis software “R” (http://cran.us.r-project.org/).
[0234] ，発症予測式  [0234], Prediction formula
[0235] [数 7]  [0235] [Equation 7]
A=1.319X+1. 322Y+2.11Z+3.29 A = 1.319X + 1.322Y + 2.11Z + 3.29
[0236] ここで、 X、 Y、および Ζは数式 3、数式 4によって示される。この実施例では、 ηは 200 である。 [0236] Here, X, Y, and 数 式 are expressed by Equation 3 and Equation 4, respectively. In this example, η is 200.
[0237] [数 3]
Figure imgf000040_0001
[0237] [Equation 3]
Figure imgf000040_0001
D2=—^— D 2 = — ^ —
ひ2  2
Figure imgf000040_0002
Figure imgf000040_0002
[0238] [数 4] [0238] [Equation 4]
n n
Figure imgf000040_0003
Figure imgf000040_0003
H H
第 2主成分 y=》 ^2/ Di  Second principal component y = >> ^ 2 / Di
w  w
n n
Di Di
Figure imgf000040_0004
Figure imgf000040_0004
[0239] ここで ε は個々のプローブセットの発現シグナルの値を表す。また、 Ρ [0239] Here, ε represents the value of the expression signal of each probe set. Also Ρ
li、 Ρ、およ i 2i び P はマーカー遺伝子セットを構成する個々のプローブセットの固有ベクトルの要素  li, Ρ, and i 2i and P are elements of the eigenvectors of the individual probe sets that make up the marker gene set
4i  4i
、 μ .及び σは個々のプローブセットについての 30試料の発現値の平均 μ及び標準 偏差 σを表す。具体的には図 22a〜図 22dに示すような値となった。  , Μ and σ represent the mean μ and standard deviation σ of the expression values of 30 samples for each probe set. Specifically, the values were as shown in FIGS. 22a to 22d.
[0240] ここにお 、て ε iにアルッノ、イマ一病発症予測の対象とする者の皮膚組織線維芽細 胞力 抽出した RNA試料をマーカー遺伝子セットの抽出及び発症予測式の設定に 用いた 30試料と同様の方法で DNAチップ GeneChip HG- U133A Arrayとハイブリダィ ゼーシヨンさせて得られた発現シグナル値のうちの、マーカー遺伝子セットに含まれ る 200プローブセットそれぞれの発現シグナル値を入力することで、 X、 Y、および Ζの 値が得られ、さらに Αの値が得られることとなる。  [0240] Here, Arno and epsilon i, skin tissue fibroblasts of the person who is the target of the onset of Ima's disease, and the extracted RNA sample were used to extract the marker gene set and to set the expression prediction formula By inputting the expression signal value of each of the 200 probe sets included in the marker gene set from the expression signal values obtained by hybridization with the DNA chip GeneChip HG-U133A Array in the same manner as for 30 samples, The values of X, Y, and 得 are obtained, and the value of Α is obtained.
[0241] 上記 Aの値が、 A>0であれば、アルツハイマー病を発症すると予測され、 A< 0で あればアルツハイマー病を発症しないと予測される。 [0241] If the value of A above is A> 0, then Alzheimer's disease is predicted to develop, and A <0 If present, it is predicted that Alzheimer's disease will not develop.
実施例 3  Example 3
[0242] Swedish 1 Arctic 虽及びプレセ二リン 1遣伝早 HI 63Y栾虽保持者皮膚鉬.織線 維莽細qにおける遣伝早 現解析により;巽 されたアルツハイマー病 症予沏 Iマ 一力一遣伝早 用いたアルツハイマー病の 症予沏 I  [0242] Swedish 1 Arctic 虽 and presenilin 1 early transmission HI 63Y 栾 虽 holder skin 鉬. Early transmission in woven line fibrosis q; analysis of Alzheimer's disease prognosis I Preliminary prediction of Alzheimer's disease
アルッノ、イマ一病の臨床症状の観察されておらず、家族性アルッノヽイマ一病病因 遺伝子を有しな!/、被験者の皮膚組織線維芽細胞の DNAチップ解析データから、ァ ルツハイマー病の発症予測を行う。  No clinical manifestations of Arno and Imah's disease have been observed and no pathogenic genes for familial Arno's disease have been found! Make a prediction.
[0243] 実施例 1に記載の方法で、被験者より提供された皮膚組織より線維芽細胞を得、さ らに RNAを抽出し、 GeneChip HG- U133A Arrayによる発現量の測定を行う。  [0243] By the method described in Example 1, fibroblasts are obtained from the skin tissue provided by the subject, RNA is further extracted, and the expression level is measured by GeneChip HG-U133A Array.
[0244] 発症予測マーカー遺伝子セットの発現シグナル値及び、図 22a〜図 22dに示され るマーカー遺伝子セットを構成する個々のプローブセットの固有ベクトルの要素、個 々のプローブセットについての試料の発現値の平均及び標準偏差の数値から、発症 予測式 (数式 3、 4、 7)より、アルツハイマー病を発病しているかどうかを診断できる。  [0244] The expression signal value of the onset prediction marker gene set, the eigenvector elements of the individual probe sets constituting the marker gene set shown in Figs. 22a to 22d, and the expression value of the sample for each probe set. From the average and standard deviation values, it is possible to diagnose whether Alzheimer's disease has occurred or not from the prediction formula (Formulas 3, 4, and 7).
[0245] 診断は、 X、 Y、 Ζの値より Α< 0であれば被験者は近 、将来アルッノ、イマ一病を発 症しな!ヽと予測され、 A > 0であれば発症すると予測される。  [0245] Diagnosis is based on X, Y, and Ζ values. If で あ れ ば <0, the subject will be near and will not develop Arno or Imah disease in the future! Predicted to develop if A> 0. Is done.
実施例 4  Example 4
[0246] Swedish栾虽、 Arctic 虽及びプレセ二リン 1遣伝早 HI 63Y栾虽保持者皮膚鉬.織線維 糸田 H q 用いたアルツハイマー 予測マーカー遣伝早の;巽  [0246] Swedish Arc, Arctic 虽 and Presenilin 1 chuden early HI 63Y 栾 虽 holder skin 鉬. Woven fiber Itoda H q used Alzheimer predictive marker wa
[0247] 30人から皮膚組織線維芽細胞の提供を受け、発症予測マーカー遺伝子の選抜を行 つた。資料提供者の内訳は、家族性アルツハイマー病病因遺伝子保持者として、 Sw edish変異保持者 7人、 Arctic変異保持者 7人及びプレセ二リン 1遺伝子 H163Y変異 保持者 5人、家族性アルッノ、イマ一病病因遺伝子非保持者として、上述の病因遺伝 子保持者の兄弟あるいは姉妹であって病因遺伝子を非保持の 11人であった。  [0247] We received cutaneous tissue fibroblasts from 30 individuals and selected onset marker genes. The breakdown of the donors is as follows: 7 persons with a Swedish mutation, 7 persons with an Arctic mutation, 5 persons with a presenilin 1 gene H163Y mutation, 5 persons with familial Arno and Imah Eleven non-pathogenic genes were siblings or sisters of the above-described pathogenic genes who did not retain the pathogenic gene.
[0248] 提供された皮膚組織生体試料から、線維芽細胞を Neuroscience Letters, 220 9-12 ( 1996)に記載の方法で単離、培養し、 1試料あたり 300〜1000万個の線維芽細胞を得 た。  [0248] From the provided skin tissue biological sample, fibroblasts were isolated and cultured by the method described in Neuroscience Letters, 220 9-12 (1996), and 3 to 10 million fibroblasts per sample were obtained. Obtained.
[0249] Rneasy Mini Kit (Qiagen, Valencia, CA)を用い、添付の手順に従って線維芽細胞か ら Total RNAを抽出した。 [0249] Using the Rneasy Mini Kit (Qiagen, Valencia, CA) Extracted total RNA.
[0250] 線維芽細胞より抽出した Total RNAを用いて個々の遺伝子の発現量を測定した。遺 伝子発現量の測定には Aifymetrix社製オリゴヌクレオチド型 DNAチップ GeneChip H G-U133A Arrayを使用した。具体的には、 Total RNAからの cDNAの調製、該 cDNA からラベル化 cRNAの調製、ラベル化 cRNAのフラグメント化、フラグメント化 cRNAと DN Aチップとのハイブリダィズ、ハイブリダィズした cRNAの蛍光染色、 DNAチップ上の蛍 光の読み取り、及び遺伝子発現量の測定、の順に、特開 2003— 169687に記載の方 法と同様の方法で行った。最終的に、遺伝子発現量は HG-U133A Arrayの蛍光ィメ ージを解析ソフトウェア Microarray Suite version 5.0より解析することにより得た。  [0250] The expression level of each gene was measured using total RNA extracted from fibroblasts. For the measurement of gene expression level, an oligonucleotide type DNA chip GeneChip HG-U133A Array manufactured by Aifymetrix was used. Specifically, preparation of cDNA from total RNA, preparation of labeled cRNA from the cDNA, fragmentation of labeled cRNA, fragmentation Hybridization of cRNA and DNA chip, fluorescent staining of hybridized cRNA, on DNA chip The method similar to the method described in Japanese Patent Application Laid-Open No. 2003-169687 was performed in the order of reading the fluorescence of the sample and measuring the gene expression level. Finally, the gene expression level was obtained by analyzing the fluorescence image of the HG-U133A Array using the analysis software Microarray Suite version 5.0.
[0251] HG- U133A Arravで測定可能な 22.238糠のヒト RNA量を計測するプローブセットのう ち、まず MAS 5.0による解析で発現していない (Absent) 判断された 9.752個を除外 しさらに 30人の発現シグナルの平均値力 200に満たない 9.288個を除外した  [0251] Of the probe set that measures the amount of 22.238 RNA human RNA that can be measured with HG-U133A Arrav, it was not expressed in the analysis by MAS 5.0 (Absent). The average value of the expression signal of 9.288 which was less than 200 was excluded
[0252] 残った 3.198個のプローブセットについて比較解析を行った  [0252] A comparative analysis was performed on the remaining 3.198 probe sets.
[0253] まず、家族件アルツハイマー病病 W遣伝早保持者 19人 病 W遣伝早非保持者 1 1人 の間で Welchの T枪定を行い、 D値が 0.05より大きい 2979個を除外した。  [0253] First, 19 patients with Alzheimer's disease who had an early history of Alzheimer's disease We performed a T test for Welch among 1 person who had no prevalence of W disease, and excluded 2979 items with a D value greater than 0.05 did.
[0254] さらに、個々の遺伝子の発現と家族性アルツハイマー病病因遺伝子の有無との関連 の強さを評価した。評価の基準には、情報量基準のひとつ、アレンのクロスバリデー シヨン基準 (CV基準）を用い 、、発現量の大き 、群と発現量の小さ!/、群との分類は、 最大値と最小値との間を 2等分することにより行った。  [0254] Furthermore, the strength of the relationship between the expression of individual genes and the presence or absence of familial Alzheimer's disease etiology genes was evaluated. For the evaluation criteria, one of the information criteria, Allen's cross-validation criteria (CV criteria) is used, the expression level is large, the group and expression level are small! /, And the group classification is the maximum and minimum This was done by dividing the value into two equal parts.
[0255] 残った 219個のプローブセットについて、家族性アルツハイマー病病因遺伝子の有無 と発現量の大小に基づ ヽて、図 8に示す 2 X 2分割表を作成した。  [0255] For the remaining 219 probe sets, a 2X2 contingency table shown in Fig. 8 was created based on the presence and absence of familial Alzheimer's disease etiology genes and their expression levels.
[0256] のプローブセットそれぞれついて、分割表の各区画に収められる試料数から、 従属モデルの CV基準と独立モデルの CV基準を以下の計算式に基づいて計算した 。 CV基準は市販の統計解析ソフトウェア「Visual Minng Studio ver. 3.0」（数理システ ム）を用いて行った。  For each probe set of [0256], the CV standard of the dependent model and the CV standard of the independent model were calculated from the number of samples contained in each section of the contingency table based on the following formula. CV standards were performed using commercially available statistical analysis software “Visual Minng Studio ver. 3.0” (Mathematical System).
[0257] CV値の算出は、実施例 1と同様とした。  [0257] The CV value was calculated in the same manner as in Example 1.
[0258] 219個のプローブセットつ 、ての従属モデルの CV基準及び独立モデルの CV基準か ら「従属モデルの CV基準 独立モデルの CV基準」が 3.0より大きいプローブセット 51 璧を選抜し、アルッノヽイマ一病発症予測マーカー遺伝子セットとした。 [0258] From 219 probe sets, the CV standard of the dependent model and the CV standard of the independent model. From the CV standard of the dependent model, the CV standard of the independent model is greater than 3.0. 51 Mitsu was selected and used as a marker gene set for predicting the onset of Arnno-Ima disease.
[0259] このようにして選抜したマーカー遺伝子に対応するプローブを図 23に示す。図中、 項目 Aはプローブセットの識別番号を表し、対応する遺伝子の情報はァフィメトリタス 社（http：〃 www.afiVmetrix.com/index.affx)より入手可能である。また項目 Bは「従属 モデルの CV基準 独立モデルの CV基準」の値を表す。  [0259] FIG. 23 shows a probe corresponding to the marker gene selected in this way. In the figure, item A represents the probe set identification number, and the corresponding gene information is available from Affymelitas (http: www.afiVmetrix.com/index.affx). Item B represents the value of the “CV standard for the dependent model”.
[0260] このァフィメトリタス社のプローブセットの識別番号は、図 24に示すように、米国生物 工学情報センター（National Center for Biotechnology Information, (NCBI) )の遺伝 子情報データベース「Genbank」のァクセシヨン番号に対応して!/、る。  [0260] The identification number of the probe set of Affymetritas corresponds to the accession number of the gene information database “Genbank” of the National Center for Biotechnology Information (NCBI) as shown in FIG. And!
[0261] 上述の 51プローブセットについて、サポートベクターマシン（SVM)による Leave-One -Out交差検証を行った。具体的には、 30個の試料から 1個を取り除き、残った 29個の
Figure imgf000043_0001
、て、 51プローブセットの発現シグナルの値を用いて SVMによる判別分析 を行 ヽ、判別空間上にに家族性アルツハイマー病病因遺伝子を有する群と無有しな V、群との判別面を得た。取り除 、た 1個の試料をその発現シグナルの値をもとに判別 空間に投影し、家族性アルツハイマー病病因遺伝子の有無が正しく判別されるかど うかを検証した。この検証を 30個の試料全てについて行ったところ、 30人中 26人（86. [0261] Leave-One-Out cross-validation was performed on the 51 probe set described above using a support vector machine (SVM). Specifically, one was removed from 30 samples and the remaining 29
Figure imgf000043_0001
The discriminant analysis by SVM was performed using the value of the expression signal of 51 probe set, and the discriminant plane between the group with and without V and the group with familial Alzheimer's etiology gene in the discriminant space was obtained. It was. Then, only one sample was projected onto the discriminant space based on the value of its expression signal, and it was verified whether the presence or absence of the gene causing the familial Alzheimer's disease was correctly discriminated. When this verification was performed on all 30 samples, 26 of 30 (86.
Z 1の試料提供者の家族性アルツハイマー病病因遺伝子の有無が正しく分類され た。 SVMによる Leave-One-Out交差検証は、統計解析ソフトウェア「R」及び「R」用統 計解析パッケージ「el071」 (http://cran.us.r-project.org/)を用いて行った。  The presence or absence of a familial Alzheimer's etiology gene in the Z 1 sample provider was correctly classified. Leave-One-Out cross-validation using SVM was performed using statistical analysis software “R” and statistical analysis package “el071” (http://cran.us.r-project.org/) for “R”. .
実施例 5  Example 5
[0262] Swedish栾 ¾、 Arctic^ 及びプレセ-リン 1遺伝子 H163Y^¾保持者皮膚組織線維 細胞における遣伝早 現,解析により撰 されたアルツハイマー病 症予沏 Iマーカ 一遣伝子を用いたアルツハイマー病の発症予測式の設定  [0262] Swedish ¾, Arctic ^ and preserin 1 gene H163Y ^ ¾ Prevalence of Alzheimer's disease predicted by analysis of holder tissue tissue cells Alzheimer's disease using a marker Of disease expression prediction formula
より選抜されたアルツハイマー病発症予測マーカー遺伝子を用いて、将 来アルツハイマー病を発症する力否かの判別基準となる発症予測式を設定した。  Using the selected Alzheimer's disease predictive marker gene, a predictive expression formula was set as a criterion for determining whether or not it will develop Alzheimer's disease in the future.
[0263] 30人の試料提供者のアルッノヽイマ一病発症予測マーカー遺伝子セットである Sl^H z^i^hの発現シグナル値を用いて主成分分析を行い、第一、第二、第三の各主 成 >の 成 > 用いるこ で、家族件アルツハイマー病病 W遣伝早保持者 ^ 眚伝 早非保持者 概ね^:分で る 維認した (阅 25参照 [0264] 主成分分析は統計解析ソフトウェア「Spotfire Ver. 7.2」（Spotfie社）を用いて行った。 [0263] Principal component analysis was performed using the expression signal values of Sl ^ H z ^ i ^ h, which is a marker gene set for predicting the onset of Arno-Ima disease in 30 sample providers. Each of the three main components>>> By using it, the family case Alzheimer's disease [0264] Principal component analysis was performed using statistical analysis software "Spotfire Ver. 7.2" (Spotfie).
[0265] そこでこれら第一、第二、第三の各主成分の主成分に対して線形判別分析を適用し 、以下に示す家族性アルツハイマー病病因遺伝子保持群と該遺伝子非保持群の境 界を示す発症予測式を得た。線形判別分析は統計解析ソフウエア「R」 (http://cran. us .r— project . org/)を用 ヽ飞ィ丁っ 7こ [0265] Therefore, a linear discriminant analysis is applied to the principal components of these first, second, and third principal components, and the boundary between the familial Alzheimer's disease etiology gene holding group and the gene non-holding group shown below. The onset prediction formula was obtained. Linear discriminant analysis uses statistical analysis software “R” (http: // cran. Us .r— project. Org /) 7
発症予測式は、下式にて得られた。  The onset prediction formula was obtained by the following formula.
[0266] [数 11]  [0266] [Equation 11]
A=0.71X+0. 933Y-0.311Z+0.81 A = 0.71X + 0.933Y-0.311Z + 0.81
[0267] ここで、 X、 Y、および Ζは以下に示す式により得られる。 [0267] Here, X, Y, and Ζ are obtained by the following equations.
[0268] [数 12]  [0268] [Equation 12]
Figure imgf000044_0001
一 oi
Figure imgf000044_0001
One oi
n=l  n = l
[0269] ここで ε は個々のプローブセットの発現シグナルの値を表す。また、 P、P 、および [0269] Here, ε represents the value of the expression signal of each probe set. And P, P, and
i li 2i  i li 2i
P はマーカー遺伝子セットを構成する個々のプローブセットの固有ベクトルの要素、 P is an element of the eigenvector of each probe set constituting the marker gene set,
3i 3i
μ i及び σ iは個々のプローブセットについての 30試料の発現値の平均及び標準偏差 を表す。具体的には図 26に示す値となった。  μ i and σ i represent the mean and standard deviation of the expression values of 30 samples for each probe set. Specifically, the values shown in Fig. 26 were obtained.
[0270] ここにおいて ε にアルッノヽイマ一病発症予測の対象とする者の皮膚組織線維芽細 胞力 抽出した RNA試料をマーカー遺伝子セットの抽出及び発症予測式の設定に 用いた 30試料と同様の方法で DNAチップ GeneChip HG- U133A Arrayとハイブリダィ ゼーシヨンさせて得られた発現シグナル値のうちの、マーカー遺伝子セットに含まれ る 51プローブセットそれぞれの発現シグナル値を入力することで、 X、 Y、および Ζの値 が得られ、さらに Αの値が得られることとなる。 [0271] 上記 Aの値が、 A>0であれば、アルツハイマー病を発症すると予測され、 A< 0であ ればアルッノ、イマ一病を発症しないと予測される。 [0270] Here, ε is the skin tissue fibroblast power of the person who is the target of predicting the onset of Arno-Ima disease. Similar to 30 samples used to extract the marker gene set and to set the expression prediction formula. By inputting the expression signal value of each of 51 probe sets included in the marker gene set among the expression signal values obtained by hybridization with the DNA chip GeneChip HG-U133A Array by the method of X, Y, And the value of Ζ is obtained, and further the value of Ζ is obtained. [0271] If the value of A is A> 0, it is predicted that Alzheimer's disease will develop, and if A <0, it is predicted that Alno and Imah's disease will not develop.
実施例 6  Example 6
[0272] Swedish栾虽、 Arctic 虽及びプレセ二リン 1遣伝早 HI 63Y栾虽保持者皮膚鉬.織線維 莽細qにおける遣伝早 現解析により;巽 されたアルツハイマー病 症予沏 Iマーカ 一遣伝早 用いた家族件アルツハイマー病の病 遣伝早保持者予沏 I  [0272] Swedish 栾 虽, Arctic 虽 and presenilin 1 early transmission HI 63Y 栾 虽 holder skin 鉬. Woven fiber 莽 fine q Early analysis; Alzheimer's disease prognosis I marker 1 Premature remedy used for family members Alzheimer's disease
¾施例 4における 織試料提他者 30人 は虽なる 18人から皮膚 織線維莽細胞の 提 ffi け、さらに ¾施例 4に首 P, の 法で、提他された皮膚 織より線維莽細 得、 RNAを柚出し GeneChiD HG- U133A Arravによる発現量の測定を行った。 ¾ 30 woven sample donors in Example 4 contributed dermatofibrofibroma cells from 18 people, and ¾ in Example 4 , the method of neck P, was used to introduce fibrosis from the dermis weed. The RNA was extracted and the expression level was measured by GeneChiD HG-U133A Arrav.
[0273] 18人提供者の内訳は、家族件アルツハイマー病病 W遣伝早保持者 して、 Swedish [0273] The breakdown of the 18 donors is Swedish, who has a family history of Alzheimer's disease.
虽保持者 1人、 Arctic 虽保持者 1人、アミロイ 前,駆体 (APP)遣伝子 V717ド 虽保 持者 1人、プレセ二リン l (PSENl)遣伝子 M146V^¾保持者 2人、プレセ二リン 2 (PSE N2)遣伝早 M239V^¾保持者 2人及び病 遣伝早不明の家族件アルツハイマー病 発症者 3人の合 f 10人、家族件アルツハイマー病病 W遺伝子非保持者 して、 PSEN 1遣伝子 M146V変異保持者の兄弟または姉妹 5人、 PSEN2遣伝子 M239V変異保持 者の兄弟または姉妹 2人、 PSEN1遣伝子 H163Y変異保持者の兄弟または姉妹 1人の 合 f 8人であった  1 虽 holder, 1 Arctic 虽 holder, Amiroy Pre-Deliver (APP) Genesis V717 ド 1 holder, Presenilin l (PSENl) Genesis M146V ^ ¾ holder 2 , Presenilin 2 (PSE N2) delivery early 2 M239V ^ ¾ holders and family with Alzheimer's disease with unknown early history f 10 patients, family case Alzheimer's disease W gene non-carrier PSEN 1 transfer M146V mutation carrier siblings or sisters, PSEN2 transfer M239V mutation carrier brothers or sisters 2 PSEN1 transfer H163Y mutation carrier siblings or sisters f was 8 people
[0274] GeneChip HG-U133A Arrayとのハイブリダィゼーシヨンにより得られた発現シグナル 値のうちの実施例 4に示すマーカー遺伝子セット 51プローブセットのそれぞれの発現 シグナル値を用いて、発症予測マーカー遺伝子セットの発現シグナル値及び、実施 例 5の図 26に示したマーカー遺伝子セットを構成する個々のプローブセットの固有 ベクトルの要素、個々
Figure imgf000045_0001
ヽての実施例 4における組織試料提供者 の発現値の平均及び標準偏差の数値から、発症予測式 (数式 11、数式 12)より 、 A< 0であれば家族性アルッノヽイマ一病の病因遺伝子を保持して 、な 、と予測し、 A >0であれば家族性アルツハイマー病の病因遺伝子を保持して 、ると予測した。予 測結果を図 27に示す。 [0274] Of the expression signal values obtained by hybridization with the GeneChip HG-U133A Array, the marker gene set shown in Example 4 51 Each expression signal value of the probe set was used to predict the onset marker gene Expression signal value of the set and individual vector elements of the individual probe sets constituting the marker gene set shown in Fig. 26 of Example 5
Figure imgf000045_0001
From the average expression values and standard deviations of the tissue sample donors in Example 4 in the past, according to the onset prediction formula (Formulas 11 and 12), if A <0, the etiology of familial Arno-Ima disease It was predicted that the gene would be retained, and if A> 0, it was predicted to retain the etiological gene of familial Alzheimer's disease. Figure 27 shows the prediction results.
[0275] 図に示すように、 18人中 13人について予測が成功した。成功率 13/18の p値は 0.048 となり、 5%の有意水準に照らして有意に高い予測成功率であることを確認した。 実施例 7 [0275] As shown in the figure, 13 out of 18 predictions were successful. The p-value for the success rate 13/18 was 0.048, confirming a significantly higher predicted success rate against the 5% significance level. Example 7
[0276] Swedish栾 ¾、 Arctic栾異及びプレセ二リン 1遺伝子 H163Y^¾保持者皮膚組織線 維莽細qにおける遣伝早 現解析により;巽 されたアルツハイマー病 症予沏 Iマ 一力一遣伝早 用いたアルツハイマー病の 症予沏 I  [0276] Swedish ¾, Arctic dysfunction and presenilin 1 gene H163Y ^ ¾ Retention of histological lines in the carrier q Based on the present analysis; Alzheimer's disease prognosis Prognosis for Alzheimer's disease used
アルッノ、イマ一病の臨床症状の観察されておらず、家族性アルッノヽイマ一病病因 遺伝子を有しな!/、被験者の皮膚組織線維芽細胞の DNAチップ解析データから、ァ ルツハイマー病の発症予測を行う。  No clinical manifestations of Arno and Imah's disease have been observed and no pathogenic genes for familial Arno's disease have been found! Make a prediction.
[0277] 実施例 4に記載の方法で、被験者より提供された皮膚組織より線維芽細胞を得、さ らに RNAを抽出し、 GeneChip HG- U133A Arrayによる発現量の測定を行う。  [0277] By the method described in Example 4, fibroblasts are obtained from the skin tissue provided by the subject, RNA is further extracted, and the expression level is measured by GeneChip HG-U133A Array.
[0278] GeneChip HG-U133A Arrayとのハイブリダィゼーシヨンにより得られた発現シグナル 値のうちの実施例 4に示すマーカー遺伝子セット 51プローブセットのそれぞれの発現 シグナル値を用いて、発症予測マーカー遺伝子セットの発現シグナル値及び、実施 例 5の図 26に示したマーカー遺伝子セットを構成する個々のプローブセットの固有 ベクトルの要素、個々
Figure imgf000046_0001
ヽての実施例 4における組織試料提供者[0278] Of the expression signal values obtained by hybridization with the GeneChip HG-U133A Array, the marker gene set shown in Example 4 51 Each expression signal value of the probe set was used to predict the onset marker gene Expression signal value of the set and individual vector elements of the individual probe sets constituting the marker gene set shown in Fig. 26 of Example 5
Figure imgf000046_0001
Tissue sample provider in Example 4
2fiAの発現値の平均及び標準偏差の数値から、発症予測式 (数式 11、数式 12)より 、 Aく 0であれば家族性アルッノヽイマ一病の病因遺伝子を保持して ヽな 、と予測し、 A >0であれば家族性アルツハイマー病の病因遺伝子を保持していると予測する。 産業上の利用可能性 Based on the average expression value and standard deviation of 2fiA, it is predicted from the expression prediction formula (Formula 11 and Formula 12) that if it is 0, it will retain the etiological gene of familial Arno-Ima disease. If A> 0, it is predicted that the gene for the pathogenesis of familial Alzheimer's disease is retained. Industrial applicability
[0279] 本発明によって、発現部位とは異なる部位力 採取した試料を用いてその遺伝子 発現データを基に、生体の生理的変化を予測する方法及び、生体の生理的変化の 予測に有効に活用される生体の生理的変化予測マーカー遺伝子の選抜方法を提 供することができた。 [0279] According to the present invention, a method for predicting physiological changes in a living body based on the gene expression data using a sample collected from a site force different from the expression site, and effectively used for predicting physiological changes in the living body And a method for selecting a marker gene for predicting physiological changes in living organisms.
[0280] この方法により、解析用の遺伝子を発現部位力 採取することが困難な疾患等に ついて、疾患の発症や予後の良否、あるいは薬剤の効果や副作用の程度などを高 い精度で予測することができ、より適切な医療行為が可能となった。  [0280] With this method, it is possible to predict with high accuracy the onset and prognosis of diseases, the effectiveness of drugs, and the degree of side effects, etc., for diseases where it is difficult to collect the expression sites of genes for analysis. And more appropriate medical practice became possible.
図面の簡単な説明  Brief Description of Drawings
[0281] [図 1]この発明の一実施形態による判別基準作成処理の流れを示す図である。  FIG. 1 is a diagram showing a flow of discrimination criterion creation processing according to an embodiment of the present invention.
[図 2]この発明の一実施形態による生理的変化の予測処理の流れを示す図である。 圆 3]この発明の一実施形態による判別基準作成装置の機能ブロック図である。 FIG. 2 is a diagram showing a flow of a physiological change prediction process according to an embodiment of the present invention. [3] FIG. 3 is a functional block diagram of a discrimination criterion creating apparatus according to an embodiment of the present invention.
[図 4]図 3の装置を CPUを用いて実現した場合のハードウェア構成である。  [FIG. 4] This is a hardware configuration when the device of FIG. 3 is realized using a CPU.
[図 5]判断基準生成プログラムのフローチャートである。  FIG. 5 is a flowchart of a determination criterion generation program.
[図 6]判断基準生成プログラムのフローチャートである。  FIG. 6 is a flowchart of a judgment criterion generation program.
[図 7]記録された発現データのデータ構造を示すための図である。  FIG. 7 is a diagram for showing the data structure of recorded expression data.
圆 8]分割表を示す図である。 [8] It is a diagram showing a contingency table.
[図 9]遺伝子毎に記録された CV値のデータ構造を示す図である。  FIG. 9 is a diagram showing a data structure of CV values recorded for each gene.
圆 10]閾値とする CV値と正答率との関係を示す図である。 [10] It is a diagram showing the relationship between the CV value as the threshold and the correct answer rate.
[図 11]予測装置の機能ブロック図である。 FIG. 11 is a functional block diagram of a prediction device.
[図 12]図 11の装置を CPUを用いて実現した場合のハードウェア構成である。  [FIG. 12] This is a hardware configuration when the device of FIG. 11 is realized using a CPU.
[図 13]予測プログラムのフローチャートである。 FIG. 13 is a flowchart of a prediction program.
[図 14]図 14Aは、他の実施形態による DNAチップの断面図である。図 14Bは、処理 回路の詳細を示す図である。  FIG. 14A is a cross-sectional view of a DNA chip according to another embodiment. FIG. 14B is a diagram showing details of the processing circuit.
[図 15]予測システムの構成図である。  FIG. 15 is a configuration diagram of a prediction system.
[図 16]サーバ装置のハードウェア構成である。  FIG. 16 shows the hardware configuration of the server device.
圆 17]端末装置のハードウェア構成である。 [17] This is the hardware configuration of the terminal device.
[図 18]プローブを示す図である。  FIG. 18 shows a probe.
[図 19a]発現データを示す図である。  FIG. 19a is a diagram showing expression data.
[図 19b]発現データを示す図である。  FIG. 19b shows expression data.
[図 19c]発現データを示す図である。  FIG. 19c is a diagram showing expression data.
[図 19d]発現データを示す図である。  FIG. 19d is a diagram showing expression data.
[図 19e]発現データを示す図である。  FIG. 19e shows expression data.
[図 19f]発現データを示す図である。  FIG. 19f is a diagram showing expression data.
[図 20a]プローブセットと CV値との関係を示すデータである。  [FIG. 20a] Data showing the relationship between the probe set and the CV value.
[図 20b]プローブセットと CV値との関係を示すデータである。 [FIG. 20b] Data showing the relationship between the probe set and the CV value.
[図 21]判別式による境界面を示す図である。 FIG. 21 is a diagram showing a boundary surface based on a discriminant equation.
[図 22a]マーカー遺伝子群の発現値の平均 、標準偏差 σ、固有ベクトル Pl、 Ρ2、 Ρ 4などを示す図である。 [図 22b]マーカー遺伝子群の発現値の平均 、標準偏差 σ、固有ベクトル Pl、 Ρ2、 Ρ 4などを示す図である。 FIG. 22a is a diagram showing an average expression value, standard deviation σ, eigenvector Pl, Ρ2, Ρ4, etc. of a marker gene group. FIG. 22b is a diagram showing the mean expression value, standard deviation σ, eigenvector Pl, Ρ2, Ρ4, etc. of the marker gene group.
[図 22c]マーカー遺伝子群の発現値の平均 、標準偏差 σ、固有ベクトル Pl、 Ρ2、 Ρ 4などを示す図である。  FIG. 22c is a diagram showing the mean expression value, standard deviation σ, eigenvector Pl, Ρ2, Ρ4, etc. of the marker gene group.
[図 22d]マーカー遺伝子群の発現値の平均 、標準偏差 σ、固有ベクトル Pl、 Ρ2、 Ρ 4などを示す図である。  FIG. 22d is a diagram showing the mean expression value, standard deviation σ, eigenvector Pl, Ρ2, Ρ4, etc. of the marker gene group.
[図 23]プローブセットと CV値との関係を示すデータである。  [Fig. 23] Data showing the relationship between the probe set and the CV value.
[図 24]NCBIの GenBankのァクセシヨン番号と、ァフィメトリタス社 pるローブセットの番 号との対応を示す図である。  FIG. 24 is a diagram showing the correspondence between NCBI GenBank accession numbers and Affymetritas p lobe set numbers.
[図 25]主成分分析の結果を示した図である。  FIG. 25 shows the results of principal component analysis.
[図 26]マーカー遺伝子群の発現値の平均 、標準偏差 σ、固有ベクトル Pl、 Ρ2、 Ρ 3などを示す図である。  FIG. 26 is a diagram showing the mean expression value, standard deviation σ, eigenvector Pl, Ρ2, Ρ3, etc. of the marker gene group.
圆 27]予測判定結果の正確性を示す図である。 [27] It is a diagram showing the accuracy of the prediction judgment result.
符号の説明 Explanation of symbols
22· •発現量検出手段  22 • Expression level detection means
24· •基礎データ生成手段  24 • Basic data generation means
26 · •マーカー選択手段  26 • Marker selection means
28 · ，判別基準生成手段  28 ·, Discrimination criteria generation means
30· ，判別基準  30 ·, criteria
32· •発現量検出手段  32 · Expression level detection means
34· •予測手段  34 · Predictive measures
36 · •出力手段  36 · Output means

Claims

請求の範囲  The scope of the claims
[1] 生体の生理的変化の予測方法であって：  [1] A method for predicting physiological changes in the body:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 につ 、て、複数の遺伝子の遺伝子発現量を検出するステップと、  Targeting a plurality of individuals that produce the physiological change and a plurality of individuals that do not produce the physiological change, a site force that is different from the target site for the physiological change prediction. Detecting a gene expression level;
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子をマーカー遺伝 子群として選抜するステップと、  Selecting, among the genes, a gene in which a difference in expression level is statistically found between an individual causing the physiological change and an individual not causing the physiological change as a marker gene group;
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ いて前記発症の有無を判別するための判別基準を生成するステップと、  A multivariate analysis was performed on the expression level of the marker gene group between the individual that caused the physiological change and the individual that did not cause the physiological change, and the occurrence of the onset was determined based on the expression level of the marker gene group. Generating a discrimination criterion for determining the presence or absence;
予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 カゝら採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て遺伝子発現量を検出するステップと、  Detecting an expression level of a gene including at least a marker gene group in a biological tissue collected from a site different from the target site of the physiological change prediction for an individual to be predicted; and
予測対象個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に前 記判定基準を適用し、前記対象部位における生理的変化の有無を予測するステップ と  Predicting the presence or absence of a physiological change in the target region by applying the determination criteria to the gene expression level of a gene including a marker gene group of a prediction target individual; and
を備えた生体の生理的変化の予測方法。  A method for predicting physiological changes in a living body.
[2] 生体の生理的変化の予測方法であって：  [2] A method for predicting physiological changes in the body:
予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 カゝら採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て遺伝子発現量を検出するステップと、  Detecting a gene expression level for a gene including at least a marker gene group in a biological tissue collected from a site different from the target site of physiological change prediction for an individual to be predicted;
予測対象個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に判 定基準を適用し、前記対象部位における生理的変化の有無を予測するステップと を備え、  Applying a judgment criterion to the gene expression level for a gene including a marker gene group of a prediction target individual, and predicting the presence or absence of a physiological change in the target site, and
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、 A target body force that is different from the target site of the physiological change prediction for a plurality of individuals that cause the physiological change and a plurality of individuals that do not generate the physiological change. , Detect the gene expression level of multiple genes,
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  Among the genes, genes that are statistically found to have a difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ Vヽて作成された判別基準であること  Multivariate analysis is performed on the expression level of marker gene groups between individuals that produce the physiological change and individuals that do not produce the physiological change, and V is created based on the expression level of the marker gene group. Discriminated criteria
を特徴とする生体の生理的変化の予測方法。  A method for predicting physiological changes in a living body, characterized by:
[3] 生体の生理的変化の予測に用いる判別基準を生成する方法であって：  [3] A method for generating discriminant criteria used to predict physiological changes in living organisms:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 につ 、て、複数の遺伝子の遺伝子発現量を検出するステップと、  Targeting a plurality of individuals that produce the physiological change and a plurality of individuals that do not produce the physiological change, a site force that is different from the target site for the physiological change prediction. Detecting a gene expression level;
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子をマーカー遺伝 子群として選抜するステップと、  Selecting, among the genes, as a marker gene group a gene in which a difference in expression level is statistically found between an individual that causes the physiological change and an individual that does not cause the physiological change;
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ いて、前記対象部位における生理的変化の有無を判別するための判別基準を生成 するステップと、  A multivariate analysis was performed on the expression level of the marker gene group between the individual that caused the physiological change and the individual that did not cause the physiological change, and the target was determined based on the expression level of the marker gene group. Generating a discrimination criterion for discriminating the presence or absence of a physiological change in the site;
を備えた生体の生理的変化の予測に用いる判別基準を生成する方法。  A method for generating a discrimination criterion for use in predicting physiological changes in a living body.
[4] 生体の生理的変化の予測に用いるマーカー遺伝子群の選抜方法であって： 当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 につ 、て、複数の遺伝子の遺伝子発現量を検出するステップと、 [4] A method for selecting a marker gene group to be used for predicting physiological changes in a living body, comprising: a plurality of individuals that cause the physiological change and a plurality of individuals that do not cause the physiological change. Detecting a gene expression level of a plurality of genes for a body force collected from a site force different from the target site for prediction;
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子をマーカー遺伝 子群として選抜するステップと、 を備えたマーカ遺伝子群の選抜方法。 Selecting, among the genes, as a marker gene group a gene in which a difference in expression level is statistically found between an individual that causes the physiological change and an individual that does not cause the physiological change; A method for selecting a marker gene group comprising:
[5] 請求項 1〜3のいずれかの方法において、  [5] In any one of claims 1 to 3,
前記遺伝子発現量の検出は、遺伝子発現検出素子を用いて行うことを特徴とする もの。  The gene expression level is detected using a gene expression detection element.
[6] 生体の生理的変化の予測に用いる判別基準をコンピュータを用いて生成するため のプログラムであって、  [6] A program for generating, using a computer, a discrimination criterion used to predict physiological changes in a living body,
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 につ 、て、複数の遺伝子の遺伝子発現量を検出するステップと、  Targeting a plurality of individuals that produce the physiological change and a plurality of individuals that do not produce the physiological change, a site force that is different from the target site for the physiological change prediction. Detecting a gene expression level;
検出した個体ごとの遺伝子発現量を前記生理的変化の有無と関連付けて基礎デ ータとするステップと、  Correlating the detected gene expression level for each individual with the presence or absence of the physiological change as basic data;
前記基礎データに基づいて、前記遺伝子のうち、前記生理的変化を生じる個体と 前記生理的変化を生じな 、個体との間にお 、て、統計的に発現量の差異が見 、だ される遺伝子をマーカー遺伝子群として選抜するステップと、  Based on the basic data, among the genes, a difference in expression level is statistically found between an individual that produces the physiological change and an individual that does not produce the physiological change. Selecting a gene as a marker gene group;
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ いて前記対象部位における生理的変化の有無を判別するための判別基準を生成す るステップと、  A multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and the target site is based on the expression level of the marker gene group. Generating discriminant criteria for discriminating the presence or absence of physiological changes in
をコンピュータに実行させるための判定基準生成プログラム。  A criterion generation program for causing a computer to execute.
[7] 生体の生理的変化の予測に用いるマーカー遺伝子群の選抜をコンピュータを用い て行う方法であって： [7] A computer-based method for selecting marker genes for use in predicting physiological changes in the body:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 につ 、て、複数の遺伝子の遺伝子発現量を検出するステップと、  Targeting a plurality of individuals that produce the physiological change and a plurality of individuals that do not produce the physiological change, a site force that is different from the target site for the physiological change prediction. Detecting a gene expression level;
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子をマーカー遺伝 子群として選抜するステップと、  Selecting, among the genes, as a marker gene group a gene in which a difference in expression level is statistically found between an individual that causes the physiological change and an individual that does not cause the physiological change;
をコンピュータに実行させるためのマーカー遺伝子群選抜プログラム。 [8] 生体の生理的変化の予測をコンピュータによって実現するためのプログラムであつ て、 Marker gene cluster selection program for causing a computer to execute. [8] A program for realizing a physiological change prediction of a living body by a computer,
予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 カゝら採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て遺伝子発現量を検出するステップと、  Detecting a gene expression level for a gene including at least a marker gene group in a biological tissue collected from a site different from the target site of physiological change prediction for an individual to be predicted;
予測対象個体のマーカー遺伝子群を含む遺伝子についての遺伝子発現量に判 定基準を適用し、前記対象部位における生理的変化の有無を予測するステップと をコンピュータに実行させるための予測プログラムであって、  A prediction program for causing a computer to execute a step of applying a judgment criterion to a gene expression level for a gene including a marker gene group of a prediction target individual and predicting the presence or absence of a physiological change in the target region,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化の発症部位とは異なる部位力 採取した生体組織につ いて、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, the site force that is different from the site of the occurrence of the physiological change. Detect the quantity,
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  Among the genes, genes that are statistically found to have a difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ Vヽて作成された判別基準であること  Multivariate analysis is performed on the expression level of marker gene groups between individuals that produce the physiological change and individuals that do not produce the physiological change, and V is created based on the expression level of the marker gene group. Discriminated criteria
を特徴とする予測プログラム。  A prediction program characterized by
[9] 請求項 5〜8の!、ずれかのプログラムにお!/、て、 [9] Claims 5-8 !, any program! /
前記遺伝子発現量の検出は、遺伝子発現検出素子を用いて行うことを特徴とする もの。  The gene expression level is detected using a gene expression detection element.
[10] 生体の生理的変化の予測を行うために用いる遺伝子発現検出素子であって、 基板と、  [10] A gene expression detection element used for predicting physiological changes in a living body, comprising a substrate,
マーカー遺伝子群について、それぞれの遺伝子発現量を検出するため、基板に形 成されたプローブと、  For the marker gene group, in order to detect the expression level of each gene, a probe formed on the substrate,
を備え、 前記マーカー遺伝子群は、 With The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あること  Among the genes, genes that have been found to have a statistical difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
を特徴とする遺伝子発現検出素子。  A gene expression detection element characterized by the above.
生体の生理的変化の予測を行うための予測装置であって、  A prediction device for predicting physiological changes in a living body,
基板と、  A substrate,
マーカー遺伝子群について、それぞれの遺伝子発現量を検出するため、基板に形 成されたプローブと、  For the marker gene group, in order to detect the expression level of each gene, a probe formed on the substrate,
プローブによって捉えられた遺伝子発現量を電気信号に変換する変換部と、 各遺伝子発現量に対応する電気信号を受け、判定基準に基づいて生理的変化の 有無を予測する予測部と、  A conversion unit that converts the gene expression level captured by the probe into an electrical signal, a prediction unit that receives an electrical signal corresponding to each gene expression level and predicts the presence or absence of a physiological change based on a criterion,
を備え、  With
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり  Among the genes, genes that have been found to have a statistical difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記判定基準は、  The criterion is
前記生理的変化を発症する個体と前記生理的変化を発症しない個体との間でマ 一力一遺伝子群の発現量にっ 、て多変量解析を行 、、マーカー遺伝子群の発現量 に基づ!/、て作成された判別基準であること  Based on the expression level of the marker gene group, a multivariate analysis was performed based on the expression level of the gene group between the individual who developed the physiological change and the individual who did not develop the physiological change. The discriminant criteria created by! /
を特徴とする予測装置。 [12] 請求項 9または 10のプログラムまたは素子において、 The prediction apparatus characterized by this. [12] In the program or element of claim 9 or 10,
前記遺伝子発現検出素子は、 DNAチップまたは DNAアレイであることを特徴とす るもの。  The gene expression detection element is a DNA chip or a DNA array.
[13] 請求項 5〜 12の装置、素子またはプログラムにおいて、  [13] In the device, element or program of claims 5-12,
前記生体組織につ!、ての遺伝子の遺伝子発現量は、当該生体組織またはそれか ら調製した生体試料に基づいて検出することを特徴とするもの。  The gene expression level of each gene in the biological tissue is detected based on the biological tissue or a biological sample prepared therefrom.
[14] 請求項 5〜 13の装置、素子またはプログラムにおいて、 [14] In the device, element or program of claims 5-13,
前記生体組織が皮膚組織または粘膜組織であることを特徴とするもの。  The biological tissue is skin tissue or mucosal tissue.
[15] 請求項 13の装置、素子またはプログラムにおいて、 [15] The device, element or program of claim 13,
前記生体試料が繊維芽細胞であることを特徴とするもの。  The biological sample is a fibroblast.
[16] 請求項 13の装置、素子またはプログラムにおいて、 [16] The device, element or program of claim 13,
前記生体試料が繊維芽細胞由来 RNAであることを特徴とするもの。  The biological sample is a fibroblast-derived RNA.
[17] 請求項 5〜 16の装置、素子またはプログラムにおいて、 [17] In the device, element or program of claims 5-16,
前記発症部位が脳であることを特徴とするもの。  The onset site is the brain.
[18] 請求項 5〜 17の装置、素子またはプログラムにおいて、 [18] The device, element or program of claims 5 to 17,
前記生理的変化が疾患の発症であることを特徴とするもの。  The physiological change is the onset of a disease.
[19] 請求項 18の装置、素子またはプログラムにおいて、 [19] The device, element or program of claim 18,
前記疾患が中枢神経疾患であることを特徴とするもの。  The disease is a central nervous disease.
[20] 請求項 19の装置、素子またはプログラムにおいて、 [20] The device, element or program of claim 19,
前記中枢神経疾患が痴呆症、パーキンソン病、筋萎縮性側索硬化症、またはプリ オン病（クロイツフェルト一ヤコブ病）であることを特徴とするもの。  The central nervous disease is dementia, Parkinson's disease, amyotrophic lateral sclerosis, or prion disease (Kreuzfeld-Jakob disease).
[21] 請求項 20の装置、素子またはプログラムにおいて、 [21] The device, element or program of claim 20,
前記痴呆症がアルツハイマー病または前頭側頭型痴呆であることを特徴とするもの  The dementia is Alzheimer's disease or frontotemporal dementia
[22] 請求項 21の装置、素子またはプログラムにおいて、 [22] The device, element or program of claim 21
前記生理的変化を誘起する要素が Swedish変異、 Arctic変異およびプレセリニン 1 遺伝子 H136Y変異力も選ばれる 1種以上の要素であることを特徴とするもの。  The element that induces the physiological change is one or more elements selected from Swedish mutation, Arctic mutation, and preserinin 1 gene H136Y mutation.
[23] 請求項 5〜22の装置、素子またはプログラムにおいて、 前記多変量解析は、主成分分析および線形判別分析を含む解析方法であることを 特徴とするもの。 [23] In the device, element or program of claims 5-22, The multivariate analysis is an analysis method including principal component analysis and linear discriminant analysis.
[24] 請求項 5〜23の装置、素子またはプログラムにおいて、  [24] In the device, element or program of claims 5-23,
前記発現量の差異が見いだされる遺伝子の選抜は、情報量基準に基づいて行わ れることを特徴とするもの。  The selection of a gene in which a difference in expression level is found is performed based on an information criterion.
[25] 請求項 24の装置、素子またはプログラムにおいて、 [25] The device, element or program of claim 24,
前記情報量基準は、アレンのクロスノくリデーシヨン基準であることを特徴とするもの。  The information criterion is an Allen cross-reduction standard.
[26] 請求項 5〜25の装置、素子またはプログラムにおいて、  [26] In the device, element or program of claims 5-25,
前記遺伝子発現量の検出は、ハイブリダィゼーシヨンによって遺伝子発現検出素 子のプローブに結合した標識済みの遺伝子による光学的特性の変化を検出すること によって行うことを特徴とするもの。  The gene expression level is detected by detecting a change in optical properties due to a labeled gene bound to a probe of a gene expression detection element by hybridization.
[27] 請求項 5〜25の装置、素子またはプログラムにおいて、  [27] In the device, element or program of claims 5-25,
前記遺伝子発現量の検出は、ハイブリダィゼーシヨンによって遺伝子発現検出素 子のプローブに結合した遺伝子による電気的特性の変化を検出することによって行う ことを特徴とするもの。  The detection of the gene expression level is performed by detecting a change in electrical characteristics due to the gene bound to the probe of the gene expression detection element by hybridization.
[28] 生体の生理的変化の予測を行うために用いる遺伝子発現検出素子であって、 基板と、  [28] A gene expression detection element used for predicting physiological changes in a living body, comprising: a substrate;
マーカー遺伝子群について、それぞれの遺伝子発現量を検出するため、基板に形 成されたプローブと、  For the marker gene group, in order to detect the expression level of each gene, a probe formed on the substrate,
を備え、  With
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  Among the genes, genes that are statistically found to have a difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記各マーカー遺伝子のためのプローブは、 前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量について主成分分析を行い、当該主成分に係る合成変量の係数 に応じて、各遺伝子に対応するプローブの検出感度を設定したものであること を特徴とする遺伝子発現検出素子。 The probe for each marker gene is Principal component analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and corresponds to each gene according to the coefficient of the synthetic variable related to the principal component. A gene expression detection element, wherein the detection sensitivity of the probe to be set is set.
端末装置およびサーバ装置を備えた生体の生理的変化の予測システムであって： 前記端末装置は、  A biological physiological change prediction system including a terminal device and a server device, wherein: the terminal device includes:
予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 から採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て検出した遺伝子発現量を示す情報を送信する送信手段と、  Transmitting information indicating the gene expression level detected for at least a gene including a marker gene group for a biological tissue collected from a site different from the target site of the physiological change prediction, for an individual to be predicted Means,
サーバ装置力 の予測結果データを受信する受信手段と、  Receiving means for receiving prediction result data of the server device power;
受信した予測結果データを出力する出力手段とを備え、  Output means for outputting the received prediction result data,
前記サーバ装置は、  The server device
前記端末装置からの遺伝子発現量を示す情報を受信する受信手段と、 当該遺伝子発現量に判定基準を適用し、前記対象部位における生理的変化の有 無を予測する予測手段と、  Receiving means for receiving information indicating the gene expression level from the terminal device; prediction means for applying a criterion to the gene expression level and predicting the presence or absence of a physiological change in the target site;
前記予測手段による予測結果データを前記端末装置に送信する送信手段とを備 えており、  Transmission means for transmitting the prediction result data by the prediction means to the terminal device,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  Among the genes, genes that are statistically found to have a difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ ヽて多変量解析を行 ヽ、マーカー遺伝子群の発現量に基づ Vヽて作成された判別基準であること を特徴とする生体の生理的変化の予測システム。 Multivariate analysis is performed on the expression level of the marker gene group between the individual that causes the physiological change and the individual that does not cause the physiological change, and V is created based on the expression level of the marker gene group. Discriminated criteria A system for predicting physiological changes in living bodies.
[30] 端末装置力 の問い合わせに応じて生体の生理的変化の予測結果を送信するサ ーバ装置であって：  [30] A server device that transmits a prediction result of a physiological change of a living body in response to an inquiry about terminal device power:
予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 カゝら採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て検出した遺伝子発現量を示す情報を前記端末装置から受信する受信手段と、 当該遺伝子発現量に判定基準を適用し、前記対象部位における生理的変化の有 無を予測する予測手段と、  Information indicating the gene expression level detected for a gene including at least a marker gene group for a biological tissue collected from a site different from the target site for the physiological change prediction for an individual to be predicted. Receiving means for receiving from the terminal device; predicting means for applying a criterion to the gene expression level and predicting the presence or absence of a physiological change in the target site;
前記予測手段による予測結果データを前記端末装置に送信する送信手段とを備 えており、  Transmission means for transmitting the prediction result data by the prediction means to the terminal device,
前記マーカー遺伝子群は、  The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あり、  Among the genes, genes that are statistically found to have a difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
前記判定基準は、  The criterion is
前記生理的変化を生じる個体と前記生理的変化を生じない個体との間でマーカー 遺伝子群の発現量につ 、て多変量解析を行 、、マーカー遺伝子群の発現量に基づ Vヽて作成された判別基準であること  Multivariate analysis is performed on the expression level of marker gene groups between individuals that produce the physiological change and individuals that do not produce the physiological change, and V is created based on the expression level of the marker gene group. Discriminated criteria
を特徴とする生体の生理的変化の予測のためのサーバ装置。  A server apparatus for predicting physiological changes in a living body.
[31] 生体の生理的変化の予測を行うサーバ装置に接続される端末装置であって、 予測対象である個体にっ 、て、前記生理的変化予測の対象部位とは異なる部位 カゝら採取した生体組織について、少なくともマーカー遺伝子群を含む遺伝子につい て検出した遺伝子発現量を示す情報を送信する送信手段と、 [31] A terminal device connected to a server device that predicts physiological changes in a living body, and is collected from a portion that is different from the target portion of the physiological change prediction by an individual to be predicted Transmitting means for transmitting information indicating the gene expression level detected for the gene including at least the marker gene group for the living tissue;
サーバ装置力 の予測結果データを受信する受信手段と、  Receiving means for receiving prediction result data of the server device power;
受信した予測結果データを出力する出力手段とを備え、 前記マーカー遺伝子群は、 Output means for outputting the received prediction result data, The marker gene group is:
当該生理的変化を生じる複数の個体と当該生理的変化を生じない複数の個体を 対象として、当該生理的変化予測の対象部位とは異なる部位力 採取した生体組織 について、複数の遺伝子の遺伝子発現量を検出し、  Targeting multiple individuals that produce the physiological change and multiple individuals that do not produce the physiological change, a different site force from the target site for the physiological change prediction. Detect
前記遺伝子のうち、前記生理的変化を生じる個体と前記生理的変化を生じない個 体との間において、統計的に発現量の差異が見いだされる遺伝子を選抜したもので あること  Among the genes, genes that have been found to have a statistical difference in expression level between individuals that produce the physiological change and individuals that do not produce the physiological change are selected.
を特徴とする端末装置。  A terminal device characterized by the above.
請求項 21または 22の装置、素子またはプログラムにおいて、  The device, element or program of claim 21 or 22,
前記マーカー遺伝子には、米国生物工学情報センター（National Center for Biote chnology Informationゝ (NCBI) )の遺伝子情報データベース「Genbank」のァクセショ ン番号によって特定される、少なくとも以下の 51個の遺伝子が含まれることを特徴と するもの：  The marker gene should include at least the following 51 genes identified by the accession number of the gene information database “Genbank” of the National Center for Biotechnology Information (NCBI). Features:
BC006249, NM_000454、 NM_001780、 BG531983、 NM_000177、 NM_000801、 NM_0 03197、 NM— 006389、 NM— 004446、 NM— 007178、 NM— 002414、 NM— 004092、 NM— 00365 1、 NM— 003022、 NM— 004528、 NM— 005614、 NM— 004730、 BC004467, NM— 001483、 NM —003365、 NM— 007214、 AI927770, NM— 001685、 NM— 005493、 NM— 001753、 NM— 00296 1、 NM_001157、 NM_004545、 NM_003915、 AF208850、 AW510696、 AF312393、 BC00 2977、 AF313911、 AF000974, L18964、 U76833, M55580、 U43430, BC005911、 AU1 47399、 AL523310、 AI144075, AL117593、 AA650558、 AI123426, NM— 005051、 NM— 0 14380、 NM— 015920、 NM— 017821、 AK001105。  BC006249, NM_000454, NM_001780, BG531983, NM_000177, NM_000801, NM_0 03197, NM—006389, NM—004446, NM—007178, NM—002414, NM—004092, NM—00365 1, NM—003022, NM—004528, NM—004528 005614, NM—004730, BC004467, NM—001483, NM—003365, NM—007214, AI927770, NM—001685, NM—005493, NM—001753, NM—00296 1, NM_001157, NM_004545, NM_003915, AF208850, AW510696, AF3393 , BC00 2977, AF313911, AF000974, L18964, U76833, M55580, U43430, BC005911, AU1 47399, AL523310, AI144075, AL117593, AA650558, AI123426, NM—005051, NM—014380, NM—015920, NM—017821, AK001105.
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