CN112151113A - Early screening method for late Alzheimer disease associated gene variation - Google Patents
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- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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Abstract
An early screening method for late Alzheimer disease associated gene variation, which comprises the following preparation steps: s1, determination of pathogenic genotype: establishing a crowd AD associated genotype database for 6 genes which are closely related to the LOAD and subjected to sequence analysis, establishing a LOAD pathogenic genotype and variation site database by combining the investigation of the apparent traits of the crowd, and analyzing variation sites of the LOAD associated genes at S2: on the basis of establishing a LOAD associated genotype database and a gene variation site database, strong associated genes and variation sites of LOAD patients are analyzed, and a LOAD pathogenic genotype variation site database is established. The invention utilizes gene sequencing technology to determine the occurrence frequency and base variation characteristics of genotypes of the LOAD strongly-associated genes such as APOE, CLU, CR1, PICALM, BIN1, ABCA7 and the like in people, determines the genotypes and associated variation sites of the LOAD diseases, and establishes a multi-gene combined systematic diagnosis standard.
Description
Technical Field
The invention relates to the field of screening methods, in particular to an early screening method for late-onset Alzheimer disease associated gene variation.
Background
With the increase of people's life span, the prevalence rate of Alzheimer's Disease (AD) gradually increases. The report of 2018 of world Alzheimer's disease shows that about 5000 million people all over the world have dementia symptoms, and scholars estimate that the number will exceed 1 hundred million 5200 ten thousand in 2050. As an important cause of dementia, AD is a neurodegenerative disorder with severe cognitive impairment; the symptoms such as severe cognitive impairment, recent memory impairment and character change caused by the traditional Chinese medicine composition greatly affect families of patients, and greatly increase social burden.
Statistics show that in 2010, about 3560 million dementia patients exist in the world, and the number is expected to rise to 1.15 billion by 2050, wherein the proportion of senile dementia in all dementia patients is more than 60%. Alzheimer's Disease (AD) is the most common cause of senile dementia, and epidemiological investigation in China shows that AD prevalence of elderly people over 65 years old is 3.4% in males, 7.7% in females and 5.9% in total; the 2010 statistical data shows that the population of the aged people in 65 years and above reaches 1.1883 billion, and the population of the aged people in China increases to 3.74 billion by 2040 years, which accounts for 24.48% of the total population in China. However, no effective medicine can slow down the occurrence and development of the senile dementia at present, but a plurality of means such as intelligence exercise, healthy diet, stimulation therapy and the like can reduce the risk of the senile dementia and delay the onset process.
However, there is currently no effective treatment and most clinical trials of new drugs are directed to failure; therefore, it is important to explore the pathological mechanism related to AD. More and more studies have found that genetic factors play an important role in the pathogenesis of AD.
Regardless of the type of AD, its pathogenesis is closely related to genetic factors. EOAD is inherited in autosomal dominant manner, and 3 pathogenic genes, namely Amyloid Precursor Protein (APP), presenilin 1 gene (PSEN1) and presenilin 2 gene (PSEN2) are discovered in the family research; although no clear pathogenic gene is found in the distribution-based LOAD, researchers have captured the 10 most strongly associated risk genes including apolipoprotein E (APOE), Clusterin (CLU), complement receptor1 (CR 1), phosphatidylinositol-binding clathrin assembly Protein (PICALM), bridged integrin 1 (BIN 1), and CD2 by techniques such as candidate gene screening and global genome association analysis (GWAS)
Protein-related gene (CD2AP), transmembrane 4 domain subfamily a member 4A/6A/4E gene (MS4A4A/MS4A6A/MS4A4E), erythropoietin-producing hepatocyte (EPH) receptor a1 gene (ephrechtora 1, EPHA1), Adenosine Triphosphate (ATP) binding cassette subfamily a member 7 gene (ABCA7), and leukocyte differentiation antigen group 33 gene (CD 33). For the above risk genes, most follow-up focused on APOE, CLU, CR1, PICALM and BIN 1.
Previous researches prove that different genotypes of a plurality of genes are positively correlated with the Alzheimer disease, the action of each genotype in the pathogenesis process is not completely the same, and in most cases, the pathogenesis can be the synergistic action among a plurality of genotypes, so that the establishment of a method for jointly evaluating the pathogenesis probability of the Alzheimer disease by a plurality of genes is urgent. The characteristics that the AD associated gene loci of different ethnicities, different ethnicities and different regional populations are different also exist, and the characteristics are proved by research, and the genotype and the distribution frequency of alleles of the LOAD associated gene of one regional population can represent the regional population. The allele characteristics of the LOAD associated genes of the population are determined by a high-throughput sequencing technology, so that an early diagnosis kit is developed, and help is provided for prevention, delay of occurrence and treatment of the LOAD.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an early screening method for the late-onset Alzheimer disease-associated gene variation.
In order to achieve the purpose, the invention adopts the following technical scheme:
an early screening method for late Alzheimer disease associated gene variation, which comprises the following preparation steps:
s1, determination of pathogenic genotype: establishing a crowd AD associated genotype database for 6 genes which are closely related to LOAD and subjected to sequence analysis, and establishing a LOAD pathogenic genotype and variation site database by combining the investigation on the apparent characters of the crowd;
s2, analysis of variant sites of LOAD-associated genes: on the basis of establishing a LOAD associated genotype database and a gene variation site database, analyzing a strong associated gene and a variation site of a LOAD patient, and establishing a LOAD pathogenic genotype variation site database;
s3, establishing a systematic diagnosis standard: the method comprises the steps of performing statistical analysis according to the contribution rate of 6 genes researched in a crowd to the LOAD, then determining the contribution rate of each gene to the LOAD, establishing a multi-gene weight-summing system diagnosis standard, and determining an incidence probability critical value and a confidence interval;
s4, genome extraction and target gene sequencing: extracting venous blood of a research object, extracting a genome, and performing high-throughput sequencing on the LOAD strong association gene by using a NimbleGen fixed-point sequence capture technology;
s5, sequence analysis: after the sequence of each gene obtained by high-throughput sequencing in S1 is subjected to multi-sequence comparison, data such as polymorphic sites, genotype frequency, homozygote and heterozygote diversity and the like are calculated by using DnaSP software;
s6, establishing a crowd AD associated genotype database, namely collecting blood samples of LOAD patients and old people over 65 years old, recording corresponding parameters, extracting the genomes of the blood samples, analyzing the sequence characteristics of the LOAD strong associated genes by using a high-throughput sequencing technology, and further establishing the crowd LOAD associated genotype database.
Preferably, the combination is used for investigating the apparent character of the human population according to the LOAD diagnosis standard.
Preferably, the LOAD patient strongly-associated genes are the genotypes of APOE, CLU, CR1, PICALM, BIN1 and ABCA 7.
Preferably, the systematic diagnosis standard in S3 is established, and since the contribution rate of each related gene to LOAD is not the same, the multiple gene joint detection is used to improve the diagnosis accuracy.
Preferably, the LOAD strongly-associated genes in S4 include APOE, CLU, CR1, PICALM, BIN1, ABCA7 and the like.
Preferably, the corresponding parameters in S6 include age, disease condition, memory, thinking, orientation, comprehension, calculation, language and judgment ability, etc.
Preferably, the LOAD strongly-associated genes in S6 are APOE, CLU, CR1, PICALM, BIN1 and ABCA 7.
The invention has the beneficial effects that:
the invention closely tracks the hot research field of AD pathogenesis related allele and early disease gene diagnosis technology internationally, but does not repeat the research work of other laboratories at home and abroad,
1. the invention comprises the following steps: the gene sequencing technology is utilized to determine the occurrence frequency and the base variation characteristics of the genotypes of the LOAD strongly-associated genes such as APOE, CLU, CR1, PICALM, BIN1, ABCA7 and the like in the population.
2. The invention comprehensively evaluates the aspects of age, disease condition, memory, thinking, orientation, understanding, calculation, language, judgment capability and the like, and determines the genotype and the relevance variation site of the LOAD disease, thereby establishing an allele database of the genes of people APOE, CLU, CR1, PICALM, BIN1, ABCA7 and the like.
3. The invention sets weights for 6 strongly-associated genes and establishes a multi-gene combined systematic diagnosis standard.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
An early screening method for late Alzheimer disease associated gene variation, which comprises the following preparation steps:
s1, determination of pathogenic genotype: establishing a crowd AD associated genotype database for 6 genes which are closely related to LOAD and subjected to sequence analysis, and establishing a LOAD pathogenic genotype and variation site database by combining the investigation on the apparent characters of the crowd;
s2, analysis of variant sites of LOAD-associated genes: on the basis of establishing a LOAD associated genotype database and a gene variation site database, analyzing a strong associated gene and a variation site of a LOAD patient, and establishing a LOAD pathogenic genotype variation site database;
s3, establishing a systematic diagnosis standard: the method comprises the steps of performing statistical analysis according to the contribution rate of 6 genes researched in a crowd to the LOAD, then determining the contribution rate of each gene to the LOAD, establishing a multi-gene weight-summing system diagnosis standard, and determining an incidence probability critical value and a confidence interval;
s4, genome extraction and target gene sequencing: extracting venous blood of a research object, extracting a genome, and performing high-throughput sequencing on the LOAD strong association gene by using a NimbleGen fixed-point sequence capture technology;
s5, sequence analysis: after the sequence of each gene obtained by high-throughput sequencing in S1 is subjected to multi-sequence comparison, data such as polymorphic sites, genotype frequency, homozygote and heterozygote diversity and the like are calculated by using DnaSP software;
s6, establishing a crowd AD associated genotype database, collecting samples of LOAD patients and old people over 65 years old and recording corresponding parameters, extracting blood sample genomes, analyzing sequence characteristics of LOAD strongly associated genes by using a high-throughput sequencing technology, further establishing the crowd LOAD associated genotype database, combining the investigation of human body apparent traits according to LOAD diagnosis standards, wherein the LOAD patient strongly associated genes are genotypes of APOE, CLU, CR1, PICALM, BIN1 and ABCA7, establishing a systematic diagnosis standard in S3, improving the diagnosis accuracy by multi-gene joint detection due to different contribution rates of each associated gene to LOAD, wherein the LOAD strongly associated genes in S4 comprise APOE, CLU, CR1, PICALM, BIN1, ABCA7 and the like, the corresponding parameters in S6 comprise age, illness condition, memory, thinking, orientation, understanding, calculation, language, judgment capability and the like, the LOAD strongly associated genes in S6 are APOE, and the corresponding parameters in S6 are, CLU, CR1, PICALM, BIN1, ABCA 7.
The working principle is as follows: first, the pathogenic genotype is determined: establishing a crowd AD associated genotype database for 6 genes which are closely related to LOAD and subjected to sequence analysis, and establishing a LOAD pathogenic genotype and variation site database by combining the investigation on the apparent characters of the crowd; analysis of variant sites of LOAD related genes: on the basis of establishing a LOAD associated genotype database and a gene variation site database, analyzing a strong associated gene and a variation site of a LOAD patient, and establishing a LOAD pathogenic genotype variation site database; establishing a systematic diagnosis standard: the method comprises the steps of performing statistical analysis according to the contribution rate of 6 genes researched in a crowd to the LOAD, then determining the contribution rate of each gene to the LOAD, establishing a multi-gene weight-summing system diagnosis standard, and determining an incidence probability critical value and a confidence interval; genome extraction and target gene sequencing: extracting venous blood of a research object, extracting a genome, and performing high-throughput sequencing on the LOAD strong association gene by using a NimbleGen fixed-point sequence capture technology; sequence analysis: after the sequence of each gene obtained by high-throughput sequencing in S1 is subjected to multi-sequence comparison, data such as polymorphic sites, genotype frequency, homozygote and heterozygote diversity and the like are calculated by using DnaSP software; establishing a crowd AD associated genotype database, namely collecting blood samples of LOAD patients and old people over 65 years old and recording corresponding parameters, extracting the genomes of the blood samples, analyzing the sequence characteristics of LOAD strong associated genes by using a high-throughput sequencing technology, and further establishing the crowd LOAD associated genotype database.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A method for early screening of late-onset Alzheimer's disease associated gene variation is characterized by comprising the following preparation steps:
s1, determination of pathogenic genotype: establishing a crowd AD associated genotype database for 6 genes which are closely related to LOAD and subjected to sequence analysis, and establishing a LOAD pathogenic genotype and variation site database by combining the investigation on the apparent characters of the crowd;
s2, analysis of variant sites of LOAD-associated genes: on the basis of establishing a LOAD associated genotype database and a gene variation site database, analyzing a strong associated gene and a variation site of a LOAD patient, and establishing a LOAD pathogenic genotype variation site database;
s3, establishing a systematic diagnosis standard: the method comprises the steps of performing statistical analysis according to the contribution rate of 6 genes researched in a crowd to the LOAD, then determining the contribution rate of each gene to the LOAD, establishing a multi-gene weight-summing system diagnosis standard, and determining an incidence probability critical value and a confidence interval;
s4, genome extraction and target gene sequencing: extracting venous blood of a research object, extracting a genome, and performing high-throughput sequencing on the LOAD strong association gene by using a NimbleGen fixed-point sequence capture technology;
s5, sequence analysis: after the sequence of each gene obtained by high-throughput sequencing in S1 is subjected to multi-sequence comparison, data such as polymorphic sites, genotype frequency, homozygote and heterozygote diversity and the like are calculated by using DnaSP software;
s6, establishing a crowd AD associated genotype database, namely collecting blood samples of LOAD patients and old people over 65 years old, recording corresponding parameters, extracting the genomes of the blood samples, analyzing the sequence characteristics of the LOAD strong associated genes by using a high-throughput sequencing technology, and further establishing the crowd LOAD associated genotype database.
2. The method of claim 1, wherein the combination of the screening for the phenotypic trait associated with late-onset alzheimer's disease is based on the diagnostic criteria for LOAD.
3. The method of claim 1, wherein the LOAD patient strongly-associated genes are APOE, CLU, CR1, PICALM, BIN1, ABCA7 genotypes.
4. The method of claim 1, wherein the systematic diagnosis criteria are established in S3, and the contribution rate of each related gene to LOAD is different, so that the diagnosis accuracy is improved by multi-gene combined detection.
5. The method of claim 1, wherein the LOAD strongly-associated genes in S4 include APOE, CLU, CR1, PICALM, BIN1, ABCA 7.
6. The method of claim 1, wherein the parameters of S6 include age, disease status, memory, thinking, orientation, comprehension, calculation, language and judgment ability.
7. The method of claim 1, wherein the LOAD strongly-associated genes in S6 are APOE, CLU, CR1, PICALM, BIN1 and ABCA 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114438193A (en) * | 2022-02-24 | 2022-05-06 | 杭州惠煜医疗科技有限公司 | Gene marker for detecting Alzheimer disease, detection method and application |
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