KR20200009927A - Methods for identifying new variant that cause hereditary spastic paraplegia and Diagnostic chip of hereditary spastic paraplegia - Google Patents

Abstract

The present invention relates to a method for identifying novel variants of hereditary spastic paraplegia (HSP) disease, an HSP disease cause gene identification system, a computer program stored in an informational medium for predicting HSP, a method of providing information for diagnosing HSP, a system for diagnosing HSP disease, a chip for diagnosing a cause of HSP disease, and an HSP diagnostic kit.

Description

Methods for identifying new variant that cause hereditary spastic paraplegia and Diagnostic chip of hereditary spastic paraplegia}

The present invention relates to a method for identifying a novel variant of Hereditary Spastic Paraplegia (HSP) disease, a system for identifying a gene causing HSP disease, a computer program stored in an information providing medium for predicting HSP, and information for diagnosing HSP. The present invention relates to a method for providing an HSP disease diagnosis system, a chip for diagnosing the cause of an HSP disease, and a kit for diagnosing an HSP.

Hereditary spastic paraplegia (HSP) is a hereditary nervous system disorder in which the muscles of the legs gradually weaken and become paralyzed, muscle tension increases and stiffness occurs. The cause of hereditary tonic paraplegia is not yet clear. However, symptoms of hereditary anterior paraplegia are presumed to be due to gradual degenerative changes of the spinal cord to the cortical spinal cord. Hereditary stiff paraplegia is known to occur in autosomal dominant (AD type) and chromosomal recessive (AR type) (Novarino G et al, Science 343: 506-511, 2014).

Recently, with the advent of next-generation sequencing (WGS) based on next-generation sequencing (NGS) technology, genetic studies of various diseases including various disease causing genes and mutations thereof are rapidly expanding.

To date, genetic diagnosis of Hereditary Spastic Paraplegia (HSP), one of rare incurable diseases, has been carried out using NGS and Sanger sequencing of several major causative genes. About 40% of domestic patients) and HSP3 (13% of domestic patients) only mutations are identified. Therefore, the necessity of developing a diagnostic system capable of analyzing all causal genes related to HSP disease is emerging. In particular, genetic studies such as NGS are considered to be a very suitable method for identifying the causal genes and mutations of heterogeneous diseases with not one or two causal genes.

Against this background, the researchers have worked hard to develop new ways to more effectively identify novel variants that are the cause of hereditary ankylosing paraplegia (HSP) disease. The present invention was completed by constructing a diagnostic system.

It is an object of the present invention to provide a method for identifying novel variants of HSP disease causes.

Another object of the present invention is to provide a computer program stored in an information providing medium for HSP prediction.

Still another object of the present invention is to provide a method for providing information for diagnosing HSP.

Still another object of the present invention is to provide a chip for diagnosing the cause of HSP disease.

Still another object of the present invention is to provide a kit for diagnosing HSP.

This will be described in detail below. Meanwhile, each of the descriptions and the embodiments disclosed in the present invention may be applied to each of the other descriptions and the embodiments. That is, all combinations of the various elements disclosed in the present invention fall within the scope of the present invention. In addition, the scope of the present invention is not to be limited by the specific description described below.

One aspect of the present invention for achieving the above object is to provide a method for identifying novel variants of HSP disease causes.

Specifically, (a) Hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information into which the hereditary stiff paraplegic single nucleotide sequence data and family information and SNV data are quantified using a predetermined formula. Becoming; (b) determining a window size n using the number of family members capable of analyzing the SNV data quantified in step (a); (c) a window including n base sequences having a window size determined in step (b), respectively, is set to the left and the right of a specific position sequence, respectively, and is analyzed using the quantized SNV data in the set window. Calculating a proportion of the target family; (d) calculating a significant probability (p-value) using a single ratio test at a single sequence (SNV) position in the window set in step (c); (e) calculating weights for physical position correction of both ends of the window set in step (c); (f) calculating a priority score using the significance probability calculated in step (d) and the weight calculated in step (e); (g) converting a single nucleotide sequence (SNV) into a gene symbol according to the calculated priority score; And (h) determining the priority candidates according to the calculated priority scores and identifying a cause candidate gene list according to the determined priority scores.

Genetic diagnosis of Hereditary Spastic Paraplegia (HSP), one of the rare incurable diseases, is performed using next-generation sequencing (NGS) and Sanger sequencing on several major causal genes. Therefore, it is necessary to develop a diagnostic system capable of analyzing all causal genes related to HSP disease. In the present invention, it can be said that it is significant in that it is possible to identify a new HSP causal gene by constructing a system for identifying a new variant of hereditary stiff paraplegic disease cause, and diagnose HSP disease using the same.

The term "Hereditary Spastic Paraplegia" (HSP) in the present invention is a hereditary nervous system disease in which the muscles of the legs gradually weaken and become paralyzed, muscle tension increases and stiffness appears. Hereditary stiff paraplegia is divided into simple hereditary stiff paraplegia (Uncomplicated HSP) and complex hereditary stiff paraplegia (Complicated HSP). Simple hereditary stiff paraplegia may develop progressive stiff paraplegia with initial findings, muscle stiffness, and difficulty controlling bladder, but complex hereditary stiff paraplegia may show additional neurological abnormalities. , Hearing impairment, mental retardation, voluntary dysfunction (motor ataxia) and the like may appear, but the symptoms to which the diagnostic method or identification method of the present invention is applied include without limitation.

Each step of the method for identifying a new variant of HSP disease causes is described in detail as follows.

First, in step (a), the sequential sequential single nucleotide variance single nucleotide sequence (SNV) data and family information is inputted. SNV data of step (a) is AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM , GARS, PSEN2, VCP, PMP22, KIF1A, HINT1, ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TLSBP, ATLB2 , GDAP1, LYST, AP4S1, AP4E1, AP4B1, ARL6IP1, NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, SPG1U1 , ERLIN2, PGAP1, ZFYVE26, WDR48, C12orf65, AP5Z1, C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 The specific nucleotide sequence to primers or probes that bind to that encodes at least one gene selected from the group, but can be obtain in the next generation sequencing ((Next Generation Sequencing), but is not limited thereto.

Specifically, the predetermined condition of step (a) may be a case where both the father and the mother base information of the individual can be used or not, more specifically, the father of the individual in step (a) If both mother and nucleotide sequence information can be used, the preset equation is

Can be. For example, in Equation 1, S is a variable for distinguishing a patient from a normal. S = 0 means normal, S = 1 means patient, and SNV _jv (S) means jth SNV of the vth family member.

In addition, in the step (a), if neither the nucleotide sequence information of the father nor the mother of the individual or only one can be used, the preset equation is

Can be. Here, MSNV _i (S = 1) is the most common pattern for the SNV of the patient at the i-th position, and may be v = 1, ..., V. For example, MSNV _j (S = 1) in Equation 3 represents a high frequency pattern among SNVs that only patients have within the family members to be analyzed. Using Equation 3, the pattern frequency LFj of the whole family can be calculated.

Step (b) may be a step in which the window size n is determined using the number of family members capable of analyzing the SNV data quantified in step (a), and step (c) is based on any specific position sequence A window including n base sequences of the window size determined in step (c) may be set to the left and the right, respectively, and the ratio of the family to be analyzed may be calculated using the quantized SNV data in the set window. As an example, n determining the window size may vary depending on the number of family members used in the analysis.

Step (d) may be a step of calculating a significant probability (p-value) using a single ratio test at a single sequence (SNV) position in the window set in step (c). For example, the null hypothesis of the single ratio test may be calculated by setting PRj. = 0.5 and setting the significant probability obtained from the single ratio test to f (PRj.).

Step (e) is a step for calculating a weight for the physical position correction of both ends of the window set in step (c), step (f) is a significant probability calculated in step (d) and (e) The priority score is calculated using the weight calculated in the step. Specifically, when the priority score calculated in step (f) is -log (0.05) = 2.996 or more, the method may further include checking whether the SNV pattern of the individual and the SNV pattern of the normal person correspond to each other. In addition, if the priority score calculated in the step (f) satisfies the condition -log (0.05) = 2.996 or more, further comprising the step of checking whether a single sequence (SNV) position is a coding region (coding region) It may be, but is not limited thereto.

Further, step (g) is a step in which a single nucleotide sequence (SNV) is converted into a gene symbol according to the calculated priority score, and step (h) has a priority according to the calculated priority score. And determining a cause candidate gene list according to the determined priority.

In one embodiment, through this method, 13 new variants have not been newly identified in Westerners. Specifically, AP4M1, ATL1, CYP7B1, DDHD1, KIAA0196, KIF1C, KIF5A, PLP1, REEP1, SPAST, SPG11, SPG20, and SPG7 were newly identified through the above method.

Another aspect for achieving the above object is to provide a computer program stored in an information providing medium for HSP prediction.

Specifically, (a) hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information is input, the data acquisition unit; (b) (i) digitizing the SNV data using a preset formula of the input SNV and family information; (ii) determining window size n using the number of family members that can analyze the quantified SNV data; (iii) a window comprising the determined window size n base sequences of (ii), respectively, to the left and the right around any specific position sequence, and the family to be analyzed using the quantified SNV data in the set window Calculate the ratio of; (iv) calculating a significance probability using a single ratio test at the SNV position in the window set in (iii) above; (v) weight calculation for physical position correction of both ends of the window set in (iii); And (vi) a data operator for performing a priority score operation using the significance probability calculated in (v) and the weighted value of (v); (c) a mapping unit for converting an SNV into a genetic symbol according to the priority score calculated by the calculating unit; And (d) identifying the mapped gene symbol, and identifying the HSP disease causing gene.

More specifically, the preset formula of step (b) is (i) when both the father and mother of the base sequence information can be used, the preset equation is

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(ii) If neither the father nor the mother's nucleotide sequence information is available or only one is available, the preset equation is

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Is,

Here, MSNV _i (S = 1) is the most general pattern for the SNV of the patient at the i-th position, and may be v = 1, ..., V, but is not limited thereto.

Another aspect for achieving the above object is to provide a system for identifying HSP disease cause genes.

Specifically, (a) Hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information into which the hereditary stiff paraplegic single nucleotide sequence data and family information and SNV data are quantified using a predetermined formula. Becoming; (b) determining a window size n using the number of family members capable of analyzing the SNV data quantified in step (a); (c) a window including n base sequences having a window size determined in step (b), respectively, is set to the left and the right of a specific position sequence, respectively, and is analyzed using the quantized SNV data in the set window. Calculating a proportion of the target family; (d) calculating a significant probability (p-value) using a single ratio test at a single sequence (SNV) position in the window set in step (c); (e) calculating weights for physical position correction of both ends of the window set in step (c); (f) calculating a priority score using the significance probability calculated in step (d) and the weight calculated in step (e); (g) mapping single nucleotide sequence (SNV) data and hereditary stiff paraplegic single nucleotide sequence (SNV) data according to the calculated priority score; And (h) outputting HSP risk using the SNV mapped in the above step, to provide a computer program stored in an information providing medium for HSP prediction.

When using the computer program of the present invention, the newly identified hereditary stiff paraplegic disease causal gene can be used to predict the possibility of developing HSP disease.

Another aspect for achieving the above object is to provide a method for identifying new variants of HSP disease causes.

Specifically, (a) AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM, GARS, PSMP22, VCP KIF1A, HINT1, ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TARDBP, KIF1B, BSCL1, ALS2, ATLS, AP4 AP4B1, ARL6IP1, NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, SPG11, FA2H, CCDC50, ERLINFY, GRL Nucleotide sequence encoding the gene for one or more genes selected from the group consisting of C12orf65, AP5Z1, C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 Genetic stiff paraplegic single nucleotide sequence through next generation sequencing using primers or probes that bind to SNV) obtaining data, (b) mapping the SNV data and data of a sample obtained from the subject, and (c) identifying the mutated position of the SNV data and the HSP disease causing gene, HSP. It is to provide a method for identifying a disease-causing new variant.

Another aspect for achieving the above object is to provide a method for providing information for HSP diagnosis.

Specifically, (a) AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM, GARS, PSMP22, VCP KIF1A, HINT1, ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TARDBP, KIF1B, BSCL1, ALS2, ATLS, AP4 AP4B1, ARL6IP1, NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, SPG11, FA2H, CCDC50, ERLINFY, GRL Nucleotide sequence encoding the gene for one or more genes selected from the group consisting of C12orf65, AP5Z1, C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 Genetic stiff paraplegic single nucleotide sequence through next generation sequencing using primers or probes that bind to SNV) obtaining data, (b) mapping the SNV data and data of a sample obtained from the subject, (c) identifying new variants of HSP disease causes by identifying the mutated positions of the SNV data and HSP disease causing genes. And (d) predicting a high risk of hereditary tonic paraplegia (HSP) disease when the number of new variants of HSP disease causes is high.

Another aspect for achieving the above object is to provide a system for diagnosing HSP disease.

Specifically, (a) hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information is input, the data acquisition unit; (b) (i) digitizing the SNV data using a preset formula of the input SNV and family information; (ii) determining window size n using the number of family members that can analyze the quantified SNV data; (iii) a window comprising the determined window size n base sequences of (ii), respectively, to the left and the right around any specific position sequence, and the family to be analyzed using the quantified SNV data in the set window Calculate the ratio of; (iv) calculating a significance probability using a single ratio test at the SNV position in the window set in (iii) above; (v) weight calculation for physical position correction of both ends of the window set in (iii); And (vi) a data operator for performing a priority score operation using the significance probability calculated in (v) and the weighted value of (v); (c) a mapping unit for mapping the selected SNV and SNV data of the HSP according to the priority score calculated by the calculating unit; And (d) it provides a system for diagnosing HSP disease, including the identification of outputting the HSP risk using the SNV mapped in the step.

Another aspect for achieving the above object is a nucleotide encoding at least one gene selected from the group consisting of AP4M1, ATL1, CYP7B1, DDHD1, KIAA0196, KIF1C, KIF5A, PLP1, REEP1, SPAST, SPG11, SPG20, SPG7 It is to provide a chip for diagnosing the cause of HSP disease, including a primer or probe that specifically binds to the sequence.

The thirteen cause genes included in the chip for diagnosing the cause of the HSP disease are newly identified HSP cause variants using the method for identifying the HSP disease cause new variant provided by the present invention, and are specific for the nucleotide sequence encoding the gene. When using a chip for diagnosing the cause of the HSP disease, including a primer or a probe to bind to, it is possible to diagnose the cause of the HSP disease more accurately.

In addition, the chip for diagnosing the cause of the HSP disease is AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, GJB1, L1CAM, GARS, PSEN2, VCP PMP22, KIF1A, HINT1, ATXN2, ENTPD1, B4GALNT1, TFG, USP8, RTN2, PNPLA6, SLC33A1, TBK1, TARDBP, KIF1B, BSCL2, ALS2, GDAP1, LYST, AP4S1, AP4E1, AP4P21 NI GJC2, CPT1C, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, FA2H, CCDC50, ERLIN1, ERLIN2, PGAP1, ZFYVE26, WDR48, C12orf65, AP5Z1, C19FR2 It may further include one or more genes selected from the group consisting of KLC2, NT5C2, ZFYVE27, HSP60, ARL6IP2, and genes related to HSP disease may be included without limitation.

Another aspect for achieving the above object is to provide a HSP diagnostic kit comprising a chip for diagnosing the cause of the HSP disease. The kit may be RT-PCR kit, DNA chip kit or protein chip kit, but is not limited thereto.

The present invention provides a method for identifying a causal variant of HSP disease, thereby establishing a causal genetic identification algorithm for an existing complex hereditary stiff paraplegic disease and establishing a diagnostic system. This has the advantage of reducing the cost and time while increasing the identification efficiency of the HSP causal gene, and also has the significance of identifying a novel causal gene mutation of HSP disease.

1 is a diagram illustrating a method for analyzing nucleotide variation in HSP disease samples.
2 is a schematic of a method for identifying new variants of HSP disease causes.

Hereinafter, the present invention will be described in more detail with reference to Examples. These examples are intended to illustrate the present invention more specifically, but the scope of the present invention is not limited to these examples.

Example  1. Detection of hereditary tonic paraplegia Target  Selection of genes for

1-1. Selection of subjects for the detection of hereditary stiff paraplegic variance

A total of 109 hereditary anterior paraplegic (HSP) disease patients or normal household members of the patients were enrolled as subjects of the present invention. Clinical evaluation was conducted by two independent neurologists, and all participants provided subject informed consent in accordance with procedures approved by the Institutional Bioethics Committee of Sungkyunkwan University and Samsung Medical Center. The study was performed using 83 patients with hereditary anterior paraplegia and 26 normal household members of the patient as healthy controls.

1-2. Detection of hereditary tonic paraplegia mutation Target  Selection of genes for

The optimal gene related to hereditary stiff paraplegia was selected.

As a result, the genes for detecting INDEL (Insertions and deletions) and Single Nucleotide Variation (SNV) are LATL1, BSCL2, CYP7B1, DDHD1, HSPD1, KIAA0196, KIF1A, KIF5A, MAST, NIPA1, PLP1, PNPLA6, REEP1, RTN2, SPARTIN, SPAST, SPG11, SPG7, ZFYVE26, ZFYVE27, AP4B1, AP4E1, AP4M1, AP5Z1, ARL6IP1, C19orf12, CAPN1, CPT1C, ENTPD1, FLRT1, GAD1, KIF1C, LYST, MAG, NTP, SP2 Was selected.

1-3. Detection of hereditary tonic paraplegia mutation Target Probe  making

Target genes were selected as in Example 1-2 and primer design was used with Sureselect (Agilent Technologies, Santa Clara, Calif.). In the case of the UTR region of the gene, a probe was prepared to cover the region encoded by the protein after the exclusion from the target. Exon probes were constructed based on the sequence of 1229 exon regions, including CDS regions of a total of 85 genes. A total of 1229 axon regions were 362,093 bp in size.

1-4. Detection of hereditary tonic paraplegia mutation target  Capture and NGS  Library Authoring

Genetic Stiff Paraplegic Variation Detection Gene Panel Genomic DNA was isolated from the blood of hereditary stiff paraplegic patients using a QiAmp DNA Mini kit (Qiagen, Valencia, CA, USA) for next-generation sequencing experiments. Subsequently, Nanodrop 8000 UV-Vis spectrometer (Thermo Scientific Inc., DE, USA), Qubit 2.0 Fluorometer (Life technologies Inc., Grand Island, NY, USA) and 2200 TapeStation Instrument (Aglient Technologies, Santa Clara, CA, USA ), The concentration, purity, and degradation of the isolated genomic DNA were determined using the equipment. Clinical samples that met the QC criteria were used for the next step of the experiment.

Genomic DNA (~ 250ng) obtained from blood from clinical samples that passed QC was sheared using Covaris S220 (Covaris, MA, USA), followed by End-repair, A-tailing, Paired-End The sequencing library was fabricated through adapter ligation and amplification steps. Specifically, the hybridization time of the library was reacted at 65 ° C. for 24 hours using the composition including all of the probes designed to capture 1229 exon regions of the 85 genes selected in Examples 1-3 above. The genomic DNA library pieces captured by were purified. Purification took advantage of the binding properties of biotin and streptavidin attached to exons. Specifically, the combined library of streptavidin coated with magnetic beads and biotin attached to the captured library fragments were separated, and then the captured library fragments were separated from the mixture using magnetic force. Then, the purified genomic DNA library fragment was amplified with an index barcode tag.

1-5. Detection of hereditary tonic paraplegia mutation target Capture  Clinical Sample Genome Data Production

A sequencing library that captured 1,229 exon regions of 85 genes in a hereditary stiff paraplegic clinical sample was injected into an NGS sequencing machine (Miseq, illumina, USA) to obtain sequence information for each DNA fragment and to process genomic data. ) And the standard human genome to obtain sequence information for each gene in the sample. Sequencing reactions were performed using the TruSeq Rapid PE Cluster kit and TruSeq Rapid SBS kit (Illumina, USA) and were performed under paired-end conditions capable of reading bidirectional 100bp.

1-6. Hereditary stiff paraplegia Variant  Data extract

After sequencing reads data created on NGS sequencing equipment is subjected to the procedure of trimming the genomic data, UCSC hg19 reference genome (http://genome.ucsc.edu) using the Burrows-Wheeler Aligner (BWA) algorithm. ), Alignment was performed. PCR duplicated reads that occur when the NGS library is created in the process of mapping the QC-completed sequence of genomic data to the UCSC hg19 standard sequence, and the reason for removing the PCR duplicated reads are to amplify them for sequencing. This is necessary to eliminate the part where the error is more amplified. PCR duplications were removed using picard-tools-1.119 (http://picard.sourceforge.net/) and single nucleotide variations (SNV) and indels (INDEL) using the GenomeAnalysisTK-3.8 algorithm. ) Was identified. The identified nucleotide variations were annotated using the ANNOVAR program to annotate each variation based on the information of UCSC hg19.

1-7. Genetic mutation selection for hereditary stiff paraplegia

Characterization, variation type, amino acid change information, SNP, among the single Nucleotide Variation (SNV) and Indel-Deletion (INDEL) Considering the DB information (dbSNP), except for single nucleotide (SNV) mutations and indel-delete (INDEL) mutations in which the characteristics of the mutations can change the protein, the process of eliminating the cause gene selection was performed.

In addition, in the case of hereditary tonic paraplegia, a rare disease has been studied. Therefore, a single nucleotide variation and a mutation in which the total incidence among indels is more than 1% were defined as a common mutation and removed. To determine the incidence of mutations, candidate mutations that are responsible for hereditary stiff paraplegia after eliminating and identifying the total incidence of each single nucleotide mutation and indels using genomic data from dbSNP, 1000 Genome, and NHLBI GO Exome Project. To perform the process of selecting. The remaining mutations after the process of eliminating common mutations were defined as candidate mutations of hereditary tonic paraplegia, and if the mutations were selected from two or more different genes, the conserved genome sequences of different species of the variant sequence were preserved. Conservation rates were identified to prioritize high conserved variations of intermediate genome sequences.

In addition, a literature study was conducted to select genes to distinguish patients with amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS), which are confused with hereditary stiff paraplegia. Hotspot genes were selected.

As a result, SOD1, FUS, TBK1, TARDBP, VCP, PFN1, UBQLN2, ATXN2, FIG4 and ALS2 were selected as genes for detecting INDEL and SNV.

In addition, a hotspot gene was selected through a literature study to identify genes capable of distinguishing CMT (Charcot-Marie-Tooth) patients, a disorder confused with hereditary stiff paraplegia. .

As a result, PMP22, GJB1, MPZ, KIF1B, MFN2, NEFL, GARS, GDAP1, and HINT1 were selected as genes for detecting INDEL and SNV.

1-8. Preparation and Correction of Hereditary Stiff Paraplegic Gene Panels

HSP gene panel sequencing was first selected for 36 clinical samples of Korean patients with hereditary stiff paraplegia, and the causal genes of hereditary stiff paraplegia were selected from eight households. In order to calibrate the hereditary stiff paraplegic gene panel because of hereditary stiff paraplegic genes in the remaining 27 households, Korean hereditary stiff paraplegia was analyzed using WES genomic data analysis of Korean 106 hereditary stiff paraplegics. The cause genes identified in patients with paralysis and literature studies were further selected for the cause genes of hereditary stiff paraplegia patients.

In addition, as in Examples 1-7, genes capable of distinguishing amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS), and Charcot-Marie-Tooth (CMT) patients, which are confused with hereditary stiff paraplegia, were selected. Genes that can distinguish between patients with hereditary stiff paraplegia were selected as hotspot genes through literature studies.

As a result, SOD1, FUS, TBK1, TARDBP, VCP, PFN1, UBQLN2, ATXN2, FIG4, ALS2, PMP22, GJB1, MPZ, KIF1B, MFN2, NEFL, GARS, GDAP1, HINT1 as genes for detecting INDEL and SNV. Selected.

Genotype OMIM Gene symbol Gene locus Inheritance Age of onset One SPG1 303350 L1CAM Xq28 X-linked recessive Early onset 2 SPG2 312920 PLP1 Xq22.2 X-linked recessive Variable 3 SPG22 300523 SLC16A2 Xq13.2 X-linked recessive Early onset 4 SPG16 300266 ? Xq11.2 X-linked recessive Early onset 5 SPG34 300750 ? Xq24-q25 X-linked recessive Teenage / Adult 6 SPG3A 182600 ATL1 14q22.1 Autosomal dominant Early onset 7 SPG4 182601 SPAST 2p22.3 Autosomal dominant Variable 8 SPG6 600363 NIPA1 15q11.2 Autosomal dominant Teenage 9 SPG7 602783 SPG7 16q24.3 Autosomal dominant Variable 10 SPG8 603563 KIAA0196 8q24.13 Autosomal dominant Adult 11 SPG9A 601162 ALDH18A1 10q24.1 Autosomal dominant Teenage 12 SPG10 604187 KIF5A 12q13.3 Autosomal dominant Early onset 13 SPG12 604805 RTN2 19q13.32 Autosomal dominant Early 14 SPG13 605280 HSP60 2q33.1 Autosomal dominant Variable 15 SPG17 270685 BSCL2 11q12.3 Autosomal dominant Teenage 16 SPG19 607152 ? 9q Autosomal dominant Adult onset 17 SPG29 609727 ? 1p31.1-p21.1 Autosomal dominant Teenage 18 SPG31 610250 REEP1 2p11.2 Autosomal dominant Early onset 19 SPG33 610244 ZFYVE27 10q24.2 Autosomal dominant Adult 20 SPG36 613096 ? 12q23-q24 Autosomal dominant Teenage / Adult 21 SPG37 611945 ? 8p21.1-q13.3 Autosomal dominant Variable 22 SPG38 612335 ? 4p16-p15 Autosomal dominant Teenage / Adult 23 SPG41 613364 ? 11p14.1-p11.2 Autosomal dominant Adolescence 24 SPG42 612539 SLC33A1 3q25.31 Autosomal dominant Variable 25 SPG72 615625 REEP2 5q31 Autosomal recessive; dominant Infancy 26 SPG73 616282 CPT1C 19q13.33 Autosomal dominant Adult 27 SPG5A 270800 CYP7B1 8q12.3 Autosomal recessive Variable 28 SPG9B 616586 ALDH18A1 10q24.1 Autosomal recessive Early onset 29 SPG11 604360 SPG11 15q21.1 Autosomal recessive Variable 30 SPG14 605229 ? 3q27-q28 Autosomal recessive Adult 31 SPG15 270700 ZFYVE26 14q24.1 Autosomal recessive Early onset 32 SPG18 611225 ERLIN2 8p11.23 Autosomal recessive Early onset 33 SPG20 275900 SPG20 13q13.3 Autosomal recessive Early onset 34 SPG21 248900 SPG21 15q22.31 Autosomal recessive Early onset 35 SPG23 270750 ? 1q24-q32 Autosomal recessive Early onset 36 SPG24 607584 ? 13q14 Autosomal recessive Early onset 37 SPG25 608220 ? 6q23-q24.1 Autosomal recessive Adult 38 SPG26 609195 B4GALNT1 12q13.3 Autosomal recessive Early onset 39 SPG27 609041 ? 10q22.1-q24.1 Autosomal recessive Variable 40 SPG28 609340 DDHD1 14q22.1 Autosomal recessive Early onset 41 SPG30 610357 KIF1A 2q37.3 Autosomal recessive Teenage 42 SPG32 611252 ? 14q12-q21 Autosomal recessive Childhood 43 SPG35 612319 FA2H 16q23.1 Autosomal recessive Childhood 44 SPG39 612020 PNPLA6 19p13.2 Autosomal recessive Childhood 45 SPG43 615043 C19orf12 19q12 Autosomal recessive Childhood 46 SPG44 613206 GJC2 1q42.13 Autosomal recessive Childhood / teenage 47 SPG45 613162 NT5C2 10q24.32-q24.33 Autosomal recessive Infancy 48 SPG46 614409 GBA2 9p13.3 Autosomal recessive Variable 49 SPG47 614066 AP4B1 1p13.2 Autosomal recessive Childhood 50 SPG48 613647 AP5Z1 7p22.1 Autosomal recessive 6th decade 51 SPG49 615041 TECPR2 14q32.31 Autosomal recessive Infancy 52 SPG50 612936 AP4M1 7q22.1 Autosomal recessive Infancy 53 SPG51 613744 AP4E1 15q21.2 Autosomal recessive Infancy 54 SPG52 614067 AP4S1 14q12 Autosomal recessive Infancy 55 SPG53 614898 VPS37A 8p22 Autosomal recessive Childhood 56 SPG54 615033 DDHD2 8p11.23 Autosomal recessive Childhood 57 SPG55 615035 C12orf65 12q24.31 Autosomal recessive Childhhod 58 SPG56 615030 CYP2U1 4q25 Autosomal recessive Childhhod 59 SPG57 615658 TFG 3q12.2 Autosomal recessive Early onset 60 SPG58 611302 KIF1C 17p13.2 Autosomal recessive Within first two decades 61 SPG59 - USP8 15q21.2 ? Childhood 62 SPG60 - WDR48 3p22.2 ? Infancy 63 SPG61 615685 ARL6IP1 16p12.3 Autosomal recessive Infancy 64 SPG62 615681 ERLIN1 10q24.31 Autosomal recessive Childhood 65 SPG63 615686 AMPD2 1p13.3 Autosomal recessive Infancy 66 SPG64 615683 ENTPD1 10q24.1 Autosomal recessive Childhood 67 SPG66 - ARSI 5q32 ? Infancy 68 SPG67 615802 PGAP1 2q33.1 Autosomal recessive Infancy 69 SPG68 609541 KLC2 11q13.1 Autosomal recessive Childhood 70 SPG69 - RAB3GAP2 1q41 71 SPG70 - MARS 12q13 ? Infancy 72 SPG71 - ZFR 5p13.3 Childhood 73 SPG74 616451 IBA57 1q42.13 Autosomal recessive Childhood 74 SPG75 616680 MAG 19q13.12 Autosomal recessive Childhood 75 SPG76 616907 CAPN1 11q13 Autosomal recessive Adult 76 HSNSP 256840 CCT5 5p15.2 Autosomal recessive Childhood Related Genes disease Gene symbol 77 Hsp BICD2 78 LYST 79 ALS SOD1 80 C9ORF72 81 TDP43 82 FUS 83 PLS (Juvenile) ALS2 84 ALS OPTN 85 VCP 86 TARDBP 87 ANG 88 FIG4 89 EOFAD PS1 90 PS2 91 bAPP 92 CMT CMT1 93 CMT2 94 HSAN 95 HMN 96 CCDC50

Example  2. Analysis of Genetic Stiff Paraplegic Gene Panel

The primers were designed according to Sureselect (Agilent) optimized for the finally selected 82 hereditary stiff paraplegic genes (Table 1) in HiSeq2500 Genome Analyzer (Illumina). To calibrate the newly developed panel of genes, 31 clinical samples with predetermined causal variations were tested. As expected, 100% mutated hereditary stiff paraplegia causes genes could be detected from the sample. This suggests that the validity of the newly developed panel of HSP genes is sufficiently confirmed.

In addition, we analyzed 19 hereditary stiff paraplegic HSP gene panel data. , KIF1A, KIF1C, LYST, MARS, NIPA1, SLC2A1, SPG20, SPG7). Variants were found in the hereditary stiff paraplegic genes included in the hereditary stiff paraplegic gene panel, indicating that the hereditary stiff paraplegic gene panel was validated.

Family Chr Start Ref Alt Gene
Name AAChange . refGene HSP-019D chr12 57958724 C G KIF5A NM_004984 exon6 c.C469G p.H157D HSP-026 chr14 68241828 G C ZFYVE26 NM_015346 exon27 c.C5225G p.S1742C HSP-028 chr14 68248231 T C ZFYVE26 NM_015346 exon22 c.A4388G p.Q1463R HSP-031F chr15 44941134 G C SPG11 NM_001160227 exon7 c.C1532G p.A511G; SPG11 HSP-031P chr15 44941134 G C SPG11 NM_001160227 exon7 c.C1532G p.A511G; SPG11 HSP-032 chr8 126087346 C T KIAA0196 NM_014846 exon8 c.G872A p.S291N HSP-033 chr2 32362221 C T SPAST NM_199436 exon11 c.C1361T p.T454I; SPAST HSP-034 chr14 68241828 G C ZFYVE26 NM_015346 exon27 c.C5225G p.S1742C HSP-035 chr12 57963186 C T KIF5A NM_004984 exon10 c.C967T p.R323W HSP-036 chr12 57909709 C A MARS NM_004990 exon19 c.C2398A p.P800T HSP-037 chr15 44921001 T C SPG11 NM_001160227 exon10 c.A1933G p.S645G; SPG11 HSP-038D chr1 203690406 C T ATP2B4 NM_001001396 exon17 c.C2680T p.P894S; ATP2B4 HSP-038P chr15 23086365 GCCGCCGCC - NIPA1 NM_144599 exon1 c.39_47del p.13_16del HSP-039 chr2 241696841 TCC - KIF1A NM_001244008 exon27 c.2751_2753del p.917_918del HSP-040P chr8 65517243 G A CYP7B1 NM_004820 exon5 c.C1229T p.P410L HSP-041 chr18 12370872 T C AFG3L2 NM_006796 exon3 c.A268G p.K90E HSP-042 chr2 171675181 C A GAD1 NM_000817 exon2 c.C80A p.T27K; GAD1 HSP-043 chr15 44921001 T C SPG11 NM_001160227 exon10 c.A1933G p.S645G; SPG11 HSP-044 chr12 57963058 G A KIF5A NM_004984 exon10 c.G839A p.R280H HSP-045P chr16 89598912 C T SPG7 NM_003119 exon9 c.C1192T p.R398X; SPG7 89620943 T C SPG7 exon16 c.T2153C p.L718P HSP-046 chr1 43395555 C T SLC2A1 NM_006516 exon5 c.G668A p.R223Q HSPS-01953 chr7 4823070 A G AP5Z1 NM_014855 exon4 c.A490G p.S164G HSPS-030P chr1 110168830 G - AMPD2 NM_203404 exon2 c.207delG p.T69fs; AMPD2 HSPS-031P chr1 235897950 T G LYST NM_000081 exon32 c.A8368C p.K2790Q; LYST HSPS-032P chr2 32353513 TTT - SPAST NM_199436 exon8 c.1114_1116del p.372_372del; SPAST HSPS-033 chr12 57958739 C T KIF5A NM_004984 exon6 c.C484T p.R162W HSPS-034 chr17 4910278 T G KIF1C NM_006612 exon14 c.T1234G p.S412A HSPS-036P chr2 32353549 G A SPAST NM_014946 exon9 c.1245 + 1G> A; NM_199436 exon8 HSPS-038P chr15 44876467 CA - SPG11 NM_001160227 exon30 c.5410_5411del p.C1804fs; SPG11 chr15 44903037 C A exon18 c.3291 + 1G> T; NM_025137 exon18 HSPS-039 chr13 36905571 C A SPG20 NM_001142294 exon3 c.G973T p.D325Y; SPG20 HSPS-040 chr14 68274612 C G ZFYVE26 NM_015346 exon5 c.G389C p.G130A

Example  3. Development of analysis program for hereditary stiff paraplegic gene panel

The aim of this study was to develop an analytical program of a hereditary stiff paraplegic gene panel to provide a method of finding candidate genes for pedophilic and non-genetic hereditary hypoparesis. Specifically, nucleotide sequence information of patients, parents or siblings was digitized, and the patterns were analyzed to use a list of candidate genes for causes of household diseases.

Methods for providing a list of candidate genes for causative and non-genetic hereditary hypoparesis disorders include: (a) classifying according to the presence or absence of sequencing data of the parent of the patient; (b) quantifying single nucleotide sequence (SNV) data according to the conditions divided in step (a) using an indicator function; (c) determining window size n using the number of family members that can be analyzed; (d) performing a proportional test using (b) data of left and right n base sequences around a specific base sequence; (e) performing the assay of (d) for all base sequences; (f) obtaining a significant probability (p-value) at the base sequence of each position using the one-sided test result; (g) a weight calculation step for physical position correction of the window; (h) calculating a score using the significant probability of (f) and the weight of (g); (i) checking whether the pattern of the patient and the normal person coincides with each other in a single nucleotide sequence having a score of (h) above -log (0.05) = 2.996; (j) confirming whether a single sequence position satisfying the condition of (i) is a coding region; (k) converting a single nucleotide sequence of a position satisfying the condition of (j) into a gene symbol; And (l) identifying the cause candidate gene list after ranking according to the score.

In the step (a), the present invention analyzes the case where both the father's and mother's base sequence information can be used and the case where it is not.

In the present invention, when both the nucleotide sequence data of the father and mother of the patient can be used, a single nucleotide sequence is quantified by an instruction function as shown in Equation 1 below.

In Equation 1, S is a variable for distinguishing a patient from a normal. S = 0 means normal person, S = 1 means patient. In Equation 1, SNVjv (S) means the j-th SNV of the v-th family member.

If all of the patient's parent sequences are available, set the father's data to v = 1 and the mother's data to v = 2. v = 3,... , V is the v-2 child sequence data.

In Equation 1, REFj is a genotype of the j th position of the human genome reference sequence. The human genome reference sequence is hg19 provided by UCSC. An indication function is a function that indicates whether it belongs to a specific set as 0 or 1. The pattern frequency LFj of the entire family is calculated using Equation 1 above. The calculation method of the pattern frequency LFj is shown in Equation 2 below.

In Equation 2, C is the number of children.

If only one of the father or mother sequence information is available or both of the parent data is not available, the single sequence information is digitized using Equation 3 below.

In Equation 3, MSNV _j (S = 1) represents a high frequency pattern among SNVs that only patients have within the family members to be analyzed. Equation 3 is used to calculate the pattern frequency LFj of the entire family. The calculation method of the pattern frequency LFj is shown in Equation 4 below.

로 설정한다. 윈도우

는 아래의 수학식 5와 같다.The pattern is analyzed by including the surrounding nucleotide sequence around the specific position nucleotide sequence. Window n left and right SNVs around a specific i-th base sequence

Set to. window

Is the same as Equation 5 below.

The proportion of the family to be analyzed in the specific j th position sequence is represented by Equation 6 below.

Equation 7 is used to quantify the ratio data derived from Equation 6.

A proportional test is performed using the numerical value of Equation 7 at the SNV position in the window. The null hypothesis of the single rate test is set to PRj. = 0.5. Set the significance probability obtained from the single ratio test to f (PRj.). Move to the next i + 1th position (window moving) and perform the above single ratio test again. Moves in turn and assays for the entire SNP. The n for determining the window size depends on the number of family members used in the analysis, and Equation 8 below was used to determine the size.

Equation 8 is a statistical sample size determination method.

In Equation 8, z _0.05 is a test statistic at a significance level of 0.05 of the standard normal distribution. In Equation 8, E is a margin of error. The error limit is designated as 0.05. In Equation 8, C is the number of children. Calculate the score by assigning the weighted value of the probabilities of the SNV and the physical location of both ends of the window. The calculation method of the weight is expressed by Equation 9 below.

는 SNV가 발생된 위치를 나타낸다. From here

Indicates the position where the SNV is generated.

Equation (10) is a score obtained by multiplying the significance probability by a weight and taking a -log to invert a small value to a large value. Equation 10 is used to assign a score to a cause candidate gene of a family specific disease in a specific SNV. If the score is less than -log (0.05) = 2.996, which is the inverse of the significance level of 0.05, it is excluded from the candidate gene.

In the SNV that satisfies the above conditions, the sequence of the sequence of the patient and the normal person is selected using the sequence information of the patient, the parent and the sibling. The position of accurately distinguishing the nucleotide sequences of the patient and the normal person in the family to be analyzed is the position of LFj. = 0 in Equations 2 and 4 above.

Check whether the position of the filtered nucleotide sequence is a coding region, and target only the nucleotide sequence of the coding region. The selected SNV base sequence is converted into a gene symbol. Additional information is provided, including a description and phenotype of the genes corresponding to the selected SNV, and scores for the PolyPhen-2 and SIFT programs. PolyPhen-2 and SIFT are programs for predicting disease caused by mutated sequences. All the above processes are analyzed in R language, and the analysis results are arranged in order of high score to provide data in tabular form.

Example  4. New Gene Mutation Screening

The analysis of the association between the normal group and the hereditary stiff paraplegic patients with 60 known HSP clinical samples and 109 clinical samples showed that the Korean hereditary stiff lower body had 29 loci in 13 known genes. New variant information and indel-deleted results were obtained in paralyzed patients to obtain new variant information. The results are new variant information that has never been reported in Westerners (Table 3).

Information on the Novel Variations in Korean Hereditary Rigid Clinical Samples Gene Name Chromosome Position Alternative allele Protein change AP4M1 chr7 99703162 c.G929A p.R310Q ATL1 chr14 51060577 c.C536A p.S179Y CYP7B1 chr8 65509474 c.G1246C p.D416H chr8 65536958 c.259 + 2T> C DDHD1 chr14 53513580 c.A2525G p.Y842C chr14 53529855 c.1571_1572del p.F524fs KIAA0196 chr8 126087346 c.G872A p.S291N chr8 126067844 c.G2086A p.G696S KIF1C chr17 4910278 c.T1234G p.S412A KIF5A chr12 57958724 c.C469G p.H157D chr12 57963186 c.C967T p.R323W chr12 57958739 c.C484T p.R162W PLP1 chrX 103045525 c.G833C p.X278S REEP1 chr2 86481815 c.324 + 2T> C chr2 86479151 c.G265A p.D89N SPAST chr2 32353499 c.C1100G p.S367W chr2 32352053 c.1039_1044del chr2 32341282 c.1098 + 1G> chr2 32340807 c.812dupC p.T271fs chr2 32314658 c.570delC p.D190fs chr2 32362221 c.C1361T p.T454I chr2 32353513 c.1114_1116del chr2 32353549 c.1245 + 1G> A SPG11 chr15 44941134 c.C1532G p.A511G chr15 44949383 c.C779G p.T260S chr15 44941086 C1580T p.S527L SPG20 chr13 36905571 c.G973T p.D325Y SPG7 chr16 89598912 c.C1192T p.R398X chr16 89620943 c.T2153C p.L718P

From the above description, those skilled in the art will appreciate that the present invention can be implemented in other specific forms without changing the technical spirit or essential features. In this regard, the embodiments described above are to be understood in all respects as illustrative and not restrictive. The scope of the present invention should be construed that all changes or modifications derived from the meaning and scope of the following claims and equivalent concepts rather than the detailed description are included in the scope of the present invention.

Claims (16)

(a) quantifying SNV data using hereditary stiff paraplegic single nucleotide sequence data and family information in which hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information are input;
(b) determining a window size n using the number of family members capable of analyzing the SNV data quantified in step (a);
(c) a window including n base sequences having a window size determined in step (b), respectively, is set to the left and the right of a specific position sequence, respectively, and is analyzed using the quantized SNV data in the set window. Calculating a proportion of the target family;
(d) calculating a significant probability (p-value) using a single ratio test at a single sequence (SNV) position in the window set in step (c);
(e) calculating weights for physical position correction of both ends of the window set in step (c);
(f) calculating a priority score using the significance probability calculated in step (d) and the weight calculated in step (e);
(g) converting a single nucleotide sequence (SNV) into a gene symbol according to the calculated priority score; And
(h) determining the priority candidates according to the calculated priority score, and identifying a cause candidate gene list according to the determined priority.
The method of claim 1,
The predetermined condition of the step (a) is (i) if both the nucleotide sequence information of the father and mother of the individual can be used

ego,
(ii) If neither the father nor the mother's nucleotide sequence information is available or only one is available, the preset equation is

Is,
Where MSNV _i (S = 1) is the most common pattern for the patient's SNV at the i-th position and v = 1, ..., V, wherein the new variant of HSP disease causes.
The method of claim 1,
The SNV data of step (a) is AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM, GARS, PSEN2 , PMP22, KIF1A, HINT1, ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TARDBP, KIF1B2, BSCL2, ATLS, APCL1 , AP4E1, AP4B1, ARL6IP1, NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, SPG11, FA2H, CCDC50AP ERL1 Nucleotide sequence encoding one or more genes selected from the group consisting of: WDR48, C12orf65, AP5Z1, C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 A method for identifying new variants of HSP disease causes, which will be obtained through Next Generation Sequencing.
The method of claim 1,
(i) if the priority score calculated in step (f) is -log (0.05) = 2.996 or more, further comprising checking whether the SNV pattern of the individual and the SNV pattern of the normal person each match, the new cause of HSP disease How to identify variants.
The method of claim 4, wherein
The method of identifying a new variant of the cause of HSP disease, further comprising the step of identifying whether a single sequence (SNV) position that satisfies the conditions of step (i) is a coding region.
(a) a data acquisition unit into which the hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information are input;
(b) (i) digitizing the SNV data using a preset formula of the input SNV and family information; (ii) determining window size n using the number of family members that can analyze the quantified SNV data; (iii) a window comprising the determined window size n base sequences of (ii), respectively, to the left and the right around any specific position sequence, and the family to be analyzed using the quantified SNV data in the set window Calculate the ratio of; (iv) calculating a significance probability using a single ratio test at the SNV position in the window set in (iii) above; (v) weight calculation for physical position correction of both ends of the window set in (iii); And (vi) a data operator for performing a priority score operation using the significance probability calculated in (v) and the weighted value of (v);
(c) a mapping unit for converting an SNV into a genetic symbol according to the priority score calculated by the calculating unit; And
(d) a system for identifying HSP disease cause genes, comprising identifying the mapped gene symbols to identify HSP disease cause genes.
The method of claim 6,
The preset formula of step (b) is (i) when both the father and mother nucleotide sequence information of the individual is available, the preset equation is

ego,
(ii) If neither the father nor the mother's nucleotide sequence information is available or only one is available, the preset equation is

Is,
Here, MSNV _i (S = 1) is the most common pattern for the SNV of the patient at the i-th position, and v = 1, ..., V, HSP disease cause gene identification system.
(a) quantifying SNV data using hereditary stiff paraplegic single nucleotide sequence data and family information in which hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information are input;
(b) determining a window size n using the number of family members capable of analyzing the SNV data quantified in step (a);
(c) a window including n base sequences having a window size determined in step (b), respectively, is set to the left and the right of a specific position sequence, respectively, and is analyzed using the quantized SNV data in the set window. Calculating a proportion of the target family;
(d) calculating a significant probability (p-value) using a single ratio test at a single sequence (SNV) position in the window set in step (c);
(e) calculating weights for physical position correction of both ends of the window set in step (c);
(f) calculating a priority score using the significance probability calculated in step (d) and the weight calculated in step (e);
(g) mapping single nucleotide sequence (SNV) data and hereditary stiff paraplegic single nucleotide sequence (SNV) data according to the calculated priority score; And
and (h) outputting whether HSP is at risk using the SNV mapped in the step.
(a) AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM, GARS, PSEN2, VCP1, PMP22 , ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TARDBP, KIF1B, BSCL2, ALS2, ATL1, APB4 AP1, APB4 AP4 , NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, SPG11, FA2H, CCDC50, ERLIN1, ERLIN2, PGAP1, ZF48 Specifically binding to a nucleotide sequence encoding a gene, one or more genes selected from the group consisting of: C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 Hereditary tonic paraplegic single sequencing data via Next Generation Sequencing using primers or probes To obtain;
(b) mapping the SNV data and data of a sample obtained from the individual; And
(c) identifying the mutated position of the SNV data and the HSP disease cause gene, the method of identifying a new variant of HSP disease cause.
(a) AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, PLP1, GJB1, L1CAM, GARS, PSEN2, VCP1, PMP22 , ATXN2, ENTPD1, B4GALNT1, AP4M1, TFG, SPG7, KIF5A, KIF1C, USP8, RTN2, PNPLA6, SLC33A1, CYP7B1, TBK1, TARDBP, KIF1B, BSCL2, ALS2, ATL1, APB4 AP1, APB4 AP4 , NIPA1, SPG21, MFN2, GJC2, CPT1C, REEP1, RAB3GAP2, REEP2, FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, SPG11, FA2H, CCDC50, ERLIN1, ERLIN2, PGAP1, ZF48 Specifically binding to a nucleotide sequence encoding a gene, one or more genes selected from the group consisting of: C19orf12, DDHD1, TECPR2, DDHD2, IBA57, ZFR, KLC2, NT5C2, SPAST, ZFYVE27, KIAA0196, HSP60, SPG20, ARL6IP2 Hereditary tonic paraplegic single sequencing data via Next Generation Sequencing using primers or probes To obtain;
(b) mapping the SNV data and data of a sample obtained from the individual;
(c) identifying new variants of HSP disease causes by identifying the mutated positions of the SNV data and HSP disease cause genes; And
(d) predicting a high risk of hereditary tonic paraplegia (HSP) disease if the number of new variants of HSP disease causes is high.
(a) a data acquisition unit into which the hereditary stiff paraplegic single nucleotide sequence (SNV) data and family information are input;
(b) (i) digitizing the SNV data using a preset formula of the input SNV and family information;
(ii) determining window size n using the number of family members that can analyze the quantified SNV data; (iii) a window comprising the determined window size n base sequences of (ii), respectively, to the left and the right around any specific position sequence, and the family to be analyzed using the quantified SNV data in the set window Calculate the ratio of; (iv) calculating a significance probability using a single ratio test at the SNV position in the window set in (iii) above; (v) weight calculation for physical position correction of both ends of the window set in (iii); And (vi) a data operator for performing a priority score operation using the significance probability calculated in (v) and the weighted value of (v);
(c) a mapping unit for mapping the selected SNV and SNV data of the HSP according to the priority score calculated by the calculating unit; And
(d) a system for diagnosing HSP disease, comprising outputting whether or not there is a risk of HSP using the SNV mapped in the step.
The method of claim 11,
The preset formula of step (b) is (i) when both the father and mother nucleotide sequence information of the individual is available, the preset equation is

ego,
(ii) If neither the father nor the mother's nucleotide sequence information is available or only one is available, the preset equation is

Is,
Where MSNV _i (S = 1) is the most common pattern for the SNV of the patient at the i th position, and v = 1, ..., V, wherein the system for diagnosing HSP disease.
Includes primers or probes that specifically bind to a nucleotide sequence encoding one or more genes selected from the group consisting of AP4M1, ATL1, CYP7B1, DDHD1, KIAA0196, KIF1C, KIF5A, PLP1, REEP1, SPAST, SPG11, SPG20, SPG7 Chip for diagnosing the cause of HSP disease.
The method of claim 13,
The chips are AMPD2, PSEN1, APP, CAPN1, FUS, ALDH18A1, ALDH18A1, SOD1, MARS, MPZ, MAG, NEFL, PFN1, SLC16A2, UBQLN2, GJB1, L1CAM, GARS, PSEN2, VCP, PMP22, KIF1 ATXN , ENTPD1, B4GALNT1, TFG, USP8, RTN2, PNPLA6, SLC33A1, TBK1, TARDBP, KIF1B, BSCL2, ALS2, GDAP1, LYST, AP4S1, AP4E1, AP4B1, ARL6IP1, NIPA1, SPG21GJCN2 , FIG4, GBA2, BICD2, VPS37A, ARSI, CCT5, CYP2U1, FA2H, CCDC50, ERLIN1, ERLIN2, PGAP1, ZFYVE26, WDR48, C12orf65, AP5Z1, C19orf12, TECPR2, DDHD2, IBA57, ZF5 KZF27 And, further comprising one or more genes selected from the group consisting of ARL6IP2, the chip for diagnosing the cause of HSP disease.
HSP diagnostic kit, comprising the chip of claim 13.
The method of claim 15,
The kit is an RT-PCR kit, DNA chip kit or protein chip kit, HSP diagnostic kit.

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