How to predict the likelihood of brain amyloid beta accumulation

Abstract

The present invention relates to a method for predicting the likelihood of cerebral amyloid beta accumulation in order to predict the onset of diseases caused by cerebral amyloid beta accumulation, such as Alzheimer's disease, in Korean individuals, and more specifically to a method for providing information for predicting the likelihood of cerebral amyloid beta accumulation, and a composition and kit for predicting the likelihood of cerebral amyloid beta accumulation. The method for providing information for predicting the likelihood of cerebral amyloid beta accumulation can effectively select individuals who are likely to have cerebral amyloid beta accumulation at an early stage, thereby suppressing the onset of diseases caused by cerebral amyloid beta accumulation and providing appropriate treatments for individuals who have developed the disease.

Description

[Technical field]

[0001] The present invention relates to a method for predicting the likelihood of cerebral amyloid beta accumulation in order to predict the occurrence of diseases caused by cerebral amyloid beta accumulation, including Alzheimer's disease, in Korean subjects, and more specifically, to a method for providing information for predicting the likelihood of cerebral amyloid beta accumulation, and a composition and kit for predicting the likelihood of cerebral amyloid beta accumulation. [Background technology]

[0002] Alzheimer's disease (AD) is a representative degenerative brain disease that accounts for approximately 70% of dementia cases and is the most common cause of dementia in the elderly. With a heritability of 58-79% and strong genetic predisposition, genetic factors play an important role in the onset of AD. Apolipoprotein E (APOE) ε4 is a well-known risk mutation for AD, and many genetic risk mutations for AD have been discovered recently through genome-wide association studies (GWAS). However, much of AD heritability remains unexplained.

[0003] Meanwhile, the initial change in the brain of AD patients is the accumulation of amyloid beta (Aβ) in the brain, which is followed by tau accumulation, neurodegeneration, and cognitive impairment. In other words, cerebral amyloid beta begins to accumulate 10 to 15 years before the clinical symptoms of AD appear. Therefore, detecting or discovering individuals with cerebral amyloid beta accumulation is of utmost importance for the early diagnosis and early treatment of AD. Conventionally, cerebral amyloid beta accumulation has been confirmed using positron emission tomography (PET) or cerebrospinal fluid testing, but PET has the disadvantages of being performed mainly in large hospitals due to the high imaging costs and limited accessibility, and has risk factors such as radiation exposure, and cerebrospinal fluid analysis requires an invasive lumbar puncture procedure, with differences in reliability depending on the institution. Therefore, there is a need for a biomarker that can predict cerebral amyloid beta accumulation in a low-cost, non-invasive manner.

[0004] Since there are large differences between races in genetic makeup, the genetic factors associated with Alzheimer's disease and cerebral amyloid beta accumulation vary by race. Most conventional Alzheimer's disease genetic studies have been conducted on Europeans, and genetic mutation studies on non-Europeans are currently lacking. In addition, research on brain amyloid beta-related genes, which are early diagnostic markers for Alzheimer's disease, is very rare overseas (Yan Q,et al.Mol Psychiatry.2018:1-13;Apostoloval LG et al.JAMA Neurol.2018:328-341;Raghavan NS,et al.JAMA Neurol.2020:1288-1298), and there are none in Korea.

[0005] Therefore, to predict cerebral amyloid beta accumulation, an early diagnostic marker for Alzheimer's disease in Koreans, using a low-cost, minimally invasive method (basic clinical and genetic information), a prediction model based on Korean data is needed. Also, a validation procedure must be carried out to see whether the same model works in an independent cohort. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent No. 10-2130929 [Patent Document 2] Korean Patent No. 10-1989695 [Patent Document 3] Korean Patent Publication No. 10-2020-0073156 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention aims to provide a method for providing information for predicting the likelihood of cerebral amyloid beta accumulation based on a combination of individual characteristics and genetic information.

[0008] Another object of the present invention is to provide a composition for predicting the possibility of cerebral amyloid beta accumulation that can confirm genetic information.

[0009] Furthermore, the present invention aims to provide a kit for predicting the possibility of cerebral amyloid beta accumulation, comprising the above-mentioned composition. [Means for solving the problem]

[0010] The inventors conducted a GWAS analysis to identify SNPs (single nucleotide polymorphisms) associated with cerebral amyloid beta accumulation using a large sample of Korean individuals, and as a result, identified six new SNPs related to amyloid beta accumulation. They then replicated and verified the association in an independent cohort of Korean individuals. They then integrated these new SNP genotypes with individual characteristics and APOE genotypes to develop a new amyloid beta prediction model, thereby completing the present invention, which provides a method and related composition for predicting the possibility of cerebral amyloid beta accumulation in Korean individuals.

[0011] One aspect of the present invention provides a method for providing information for predicting the possibility of cerebral amyloid beta accumulation by combining demographic characteristics, APOE ε4 genotype and SNP genotype.More specifically, the method for providing information for predicting the possibility of cerebral amyloid beta accumulation includes: a) collecting individual characteristics consisting of gender, age and education level from an individual; b) measuring APOE ε4 genotype and one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464 and rs11983537 in the biological sample of the individual; and c) combining the collected individual characteristics and the measured APOE ε4 genotype and SNP genotype to predict the possibility of cerebral amyloid beta accumulation.

[0012] In the present invention, "possibility of cerebral amyloid beta accumulation" means the risk of cerebral amyloid beta accumulation in an individual, and the higher the risk, the higher the possibility of developing a disease caused by amyloid beta accumulation. Here, the disease caused by amyloid beta accumulation may be, but is not limited to, Alzheimer's disease, dementia with Lewy bodies, cerebral amyloid angiopathy, etc. Such prediction of the possibility of cerebral amyloid beta accumulation is distinguished from a technique for diagnosing Alzheimer's disease or Alzheimer's dementia in an individual. In the present invention, a case where there is a possibility of amyloid beta accumulation in the brain or where it has already accumulated can be determined as positive.

[0013] The above steps a) to c) will be described in detail below.

[0014] The step a) is a process of collecting individual characteristics from an individual who needs to be tested for the presence or absence of cerebral amyloid beta accumulation.

[0015] The individual is a subject for which the possibility of amyloid beta accumulation in the cerebral cortex is predicted, and may be a patient with a family history of diseases caused by the accumulation of cerebral amyloid beta (e.g., Alzheimer's disease, dementia with Lewy bodies, cerebral amyloid angiopathy), a patient suspected of having a brain disease due to the manifestation of some clinical symptoms such as memory loss, impaired language ability, and impaired judgment, or a person with no particular clinical symptoms.

[0016] The individual characteristics may be basic characteristics of an individual, such as demographics, consisting of gender, age and education level.

[0017] The sex refers to biological sex, and it is known that women are more likely to develop Alzheimer's dementia than men. The age refers to the actual age calculated at the time of examination from the actual birth date, and the older one is, the higher the possibility of amyloid beta accumulation, and it is known that Alzheimer's disease mainly occurs in elderly people aged 65 years or older. The education level refers to the overall education level including the formal education process, and in the case of dropout, the education level is calculated excluding the year of dropout, and it has been reported recently that the risk of dementia increases in the case of low education. Therefore, an individual's sex, age, and education level can be significant variables in predicting the possibility of cerebral amyloid beta accumulation.

[0018] The step b) is the process of measuring the level of a biomarker present in a biological sample.

[0019] The biological sample means a sample that can be collected from an individual and used to predict the possibility of cerebral amyloid beta accumulation, and may be, for example, blood, cerebrospinal fluid, tears, saliva, urine, etc. In the present invention, it is preferable to use blood, saliva, oral mucosa, or hair, which are relatively less painful and less expensive to collect than cerebrospinal fluid.

[0020] According to one embodiment of the present invention, the biological sample may be one selected from the group consisting of whole blood, plasma, saliva, oral mucosa and hair.

[0021] The biomarker generally includes all organic biomolecules such as polypeptides, proteins, nucleic acids, genes, lipids, glycolipids, glycoproteins, sugars, etc., which can be used to know biological changes as detectable substances from biological samples. The present invention is characterized in that the APOE ε4 genotype and SNP genotype are used as biomarkers.

[0022] The APOE gene is classified into three alleles called ε2 (cys112, cys158), ε3 (cys112, arg158) and ε4 (arg112, arg158), and every person inherits one allele from each parent, and each individual's genotype exists as ε2 / ε2, ε2 / ε3, ε2 / ε4, ε3 / ε3, ε3 / ε4 or ε4 / ε4. Of these, the ε4 allele is found in about 20% of the population and is known to increase the risk of developing Alzheimer's dementia, so APOE ε4 is a risk factor that is highly associated with the onset of AD. Therefore, possessing the APOE ε4 genotype is a significant variable in predicting the possibility of cerebral amyloid beta accumulation, and the risk of cerebral amyloid beta accumulation may increase depending on the number of alleles possessed.

[0023] According to one embodiment of the present invention, when the possibility of cerebral amyloid beta accumulation is predicted by combining the APOE ε4 genotype with individual characteristics in a Korean subject, the AUC (Area Under Curve) value can be improved.

[0024] Here, AUC refers to the total area under the ROC curve calculated by ROC analysis, and is known to indicate 0.5 in the case of a random model and converge to 1 as the predictive performance of the model improves.

[0025] The SNP refers to the presence of two or more alleles at one gene locus, and refers to a polymorphic site that differs by only a single base. In the present invention, the SNP may be one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464, and rs11983537, which have been confirmed to be associated with cerebral amyloid beta accumulation through GWAS analysis of Korean population. These rs73375428, rs6978259, rs2903923, rs3828947, rs6958464 and rs11983537 are located in the intron of the CCDC146 gene (coiled-coil domain containing 146 gene) in human chromosome 7 and show high linkage disequilibrium (LD) with each other. 2 >0.75), and are located at loci 76907550, 76909167, 76907750, 76908199, 76909035, and 76908690 on chromosome 7. These six SNPs have never been reported to be associated with cerebral amyloid beta accumulation, and their relationship with cerebral amyloid beta accumulation in Korean subjects has never been reported.

[0026] Furthermore, according to one embodiment of the present invention, when SNP genotype is combined with individual characteristics and APOE ε4 genotype to predict the likelihood of cerebral amyloid beta accumulation, the AUC can be improved.

[0027] The step c) is a process of preparing a brain amyloid beta accumulation evaluation model by combining individual characteristics, APOE ε4 genotype and SNP genotype.

[0028] According to one embodiment of the present invention, the step c) may involve setting the individual characteristics, the APOE ε4 genotype, and the SNP genotype as independent variables, respectively, and setting specific coefficients corresponding thereto as dependent variables, and creating a brain amyloid beta accumulation evaluation model by statistical analysis.

[0029] According to one embodiment of the present invention, the statistical analysis may be performed using an analysis method selected from the group consisting of a linear or nonlinear regression analysis method, a linear or nonlinear classification analysis method, ANOVA, a neural network analysis method, a deep neural network analysis method, a genetic analysis method, a support vector machine analysis method, a hierarchical analysis or clustering analysis method, a hierarchical algorithm using a decision tree or a Kernel principal components analysis method, a Markov Blanket analysis method, a recursive feature elimination or an entropy-based recursive feature elimination analysis method, a forward floating search or a backward floating search analysis method, and combinations thereof.

[0030] According to one embodiment of the present invention, the relationship between individual characteristics, APOE ε4 genotype, and SNP genotype, which were set as independent variables, and each specific coefficient, which was set as a dependent variable, was analyzed by multivariate logistic regression analysis to create a model for evaluating cerebral amyloid beta accumulation.

[0031] According to one embodiment of the present invention, the cerebral amyloid beta accumulation evaluation model may be represented by the following formula 1.

[0032]

number

[0033] (In the above formula 1, α is -0.612 for SNP rs6978259, -0.652 for SNP rs6958464, -0.658 for SNP rs11983537, -0.571 for SNP rs3828947, -0.612 for SNP rs2903923, and -0.633 for SNP rs73375428.

[0034] According to one embodiment of the present invention, when individual characteristics, APOE ε4 genotype and SNP genotype are combined to predict the possibility of cerebral amyloid beta accumulation, the AUC can be improved. In this case, the AUC in the cerebral amyloid beta accumulation evaluation model is 0.74 or more regardless of the type of SNP (0.748 for rs6978259; 0.749 for rs6958464; 0.749 for rs11983537; 0.751 for rs3828947; 0.748 for rs2903923; 0.749 for rs73375428), which is more accurate than the individual characteristics alone (0.506) or the combination of individual characteristics and APOE ε4 genotype (0.723).

[0035] It can be said that the higher the value derived from such a brain amyloid beta accumulation evaluation model, the higher the predicted possibility of brain amyloid beta accumulation in the individual being tested.

[0036] Another aspect of the present invention provides a composition for predicting the likelihood of cerebral amyloid beta accumulation, comprising a preparation capable of detecting the APOE ε4 genotype; and one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464, and rs11983537.

[0037] In the present invention, "composition for predicting the possibility of cerebral amyloid beta accumulation" means a composition for a series of actions for predicting cerebral amyloid beta accumulation by detecting base sequences including the APOE ε4 genotype and SNP genotype, thereby enabling early diagnosis of the risk of developing a disease caused by cerebral amyloid beta accumulation or the presence or absence of the disease.

[0038] Here, the APOE ε4 genotype and the six SNP genotypes are the same as those described above.

[0039] The formulation may be capable of detecting biomarkers for predicting the likelihood of cerebral amyloid beta accumulation, namely APOE ε4 genotype and SNP genotype.

[0040] According to one embodiment of the present invention, the preparation may be one or more selected from the group consisting of a primer set, a probe, and an antisense oligonucleotide that specifically binds to a base sequence including an APOE ε4 genotype or SNP genotype. In the present invention, the primer set or probe is preferably designed to specifically amplify a specific region of a gene based on the nucleic acid sequence information of the gene.

[0041] The term "specifically bind" means that the binding strength to a target substance is superior to other substances to such an extent that the presence or absence of the target substance can be detected by the binding.

[0042] The primer set refers to a combination of primers that are short RNA or DNA sequences that recognize the entire or partial base sequence including the APOE ε4 genotype or SNP genotype, and may be, for example, a primer set of all combinations of forward and reverse primers that can bind to a base sequence including the APOE ε4 genotype or SNP genotype. The nucleic acid sequence of the primer is a sequence that does not match with the non-target sequence present in the sample, so that it can amplify only the target gene sequence containing the complementary primer binding site and can provide high specificity when the primer does not induce non-specific amplification.

[0043] The probe means a substance capable of specifically binding to a target substance to be detected in a sample, and capable of specifically confirming the presence of the target substance in a sample through said binding. The constituent substance of the probe is a substance capable of specifically binding to the target substance, and examples thereof may include, but are not limited to, PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA, DNA, etc.

[0044] Another aspect of the present invention provides a kit for predicting the possibility of cerebral amyloid beta accumulation, comprising the composition.

[0045] In the present invention, the "kit for predicting the possibility of cerebral amyloid beta accumulation" refers to a substance capable of predicting the possibility of cerebral amyloid beta accumulation from a biological sample collected from a test subject, thereby enabling early, rapid and simple diagnosis of the risk of disease development or the presence or absence of disease due to cerebral amyloid beta accumulation. Examples of the kit may be, but are not limited to, a PCR (polymerase chain reaction) kit, a DNA chip kit, etc. In addition, the kit may additionally include one or more other component compositions, solutions or devices suitable for the analysis method.

[0046] According to one embodiment of the present invention, the kit may include essential elements required for PCR, such as a primer set that specifically binds to a base sequence including the APOE ε4 genotype or one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464, and rs11983537, a test tube or other suitable container, a reaction buffer (pH and magnesium concentration may vary depending on conditions), deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNAse, RNAse inhibitor, DEPC water, and sterilized water.

[0047] According to one embodiment of the present invention, the kit may include essential elements required for performing DNA chip. In this case, the kit may include a substrate to which cDNA or oligonucleotides corresponding to the entire base sequence or a fragment thereof including the APOE ε4 genotype or one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464 and rs11983537 are attached, and reagents, preparations, enzymes, etc. for preparing fluorescently labeled probes. The substrate may also include cDNA or oligonucleotides corresponding to a control gene or a fragment thereof. Effect of the Invention

[0048] The present invention's method for providing information for predicting the possibility of cerebral amyloid beta accumulation can effectively select individuals who are likely to have cerebral amyloid beta accumulation at an early stage, thereby suppressing the onset of diseases caused by cerebral amyloid beta accumulation and providing appropriate treatments to individuals who have developed the disease. [Brief description of the drawings]

[0049] [Figure 1] 1 is a graph showing a quantile-quantile plot versus P value according to one embodiment of the present invention. [Diagram 2] 1 is a graph showing SNPs that are significant for amyloid beta accumulation located on chromosomes 19 and 7 in the full-length genome according to one embodiment of the present invention. [Diagram 3] 1 is a graph showing the chromosomal location of rs6978259 according to one embodiment of the present invention. [Figure 4]1 shows amyloid PET (positron emission tomography) images of a subject for voxel-based morphological analysis according to one embodiment of the present invention, in which (A) is an image confirming reduced Aβ deposition in a subject having the rs6978259 allele, and (B) is an image confirming increased Aβ deposition in a subject having the APOE ε4 gene. [Diagram 5] 13 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs6978259 according to one embodiment of the present invention. [Figure 6] 13 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs6958464 according to one embodiment of the present invention. [Figure 7] 13 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs11983537 according to one embodiment of the present invention. [Figure 8] 13 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs3828947 according to one embodiment of the present invention. [Figure 9] 1 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs2903923 according to one embodiment of the present invention. [Figure 10] 13 shows the results of AUC values ​​of the evaluation model development and validation data sets for rs73375428 according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0050] The present invention will be described in more detail below. However, such description is merely presented as an example for understanding the present invention, and the scope of the present invention is not limited by such exemplary description.

[0051] Example 1. Development of an evaluation model for predicting the likelihood of cerebral amyloid beta accumulation

[0052] 1-1. Subject Recruitment For the evaluation model development dataset, a total of 1,214 Koreans were recruited from 14 hospitals in South Korea between January 2013 and July 2019. Of these, 923 were recruited from Samsung Seoul Medical Center, 201 from the multi-institutional study of the Korea Brain Aging Study for Early Diagnosis and Prediction of AD (KBASE-V), and 90 from the multi-institutional study of the Dementia Cohort-Based Clinical Research Platform.

[0053] For the validation dataset, data from 306 Korean subjects were recruited from the Biobank of Chronic Cerebrovascular Disease Consortium (biobank), which was recruited from 2016 to 2018. This was part of the Biobank Innovation for Chronic Cerebrovascular Diseases (BICWALZS) and Neurological Disorders Fusion Research Center along with ongoing Alzheimer's disease research.

[0054] The subjects recruited were i) diagnosed with mild cognitive impairment (aMCI), AD dementia (ADD), or cognitively unimpaired (CU) based on a detailed neuropsychiatric examination, and ii) selected from those who underwent amyloid PET imaging. Those with causative gene mutations for AD, such as PSEN1, PSEN2, and APP, were excluded. After performing quality control (QC) as described above, a total of 1,190 subjects (383 CU, 330 aMCI, 477 ADD) were used for the data for evaluation model development, and a total of 284 subjects (46 CU, 167 aMCI, 71 ADD) were used for the data for validation. All subjects provided written informed consent, and the study was approved by the Institutional Review Board of each center.

[0055] As a result, the basic demographic characteristics for the two data sets (model development and validation data) are shown in Table 1 below. Model development subjects were younger and had a higher proportion of females than validation data. As expected, in both data sets, PET-confirmed amyloid beta (Aβ) positivity subjects had a higher proportion of aMCI and AD than Aβ-negative subjects. Additionally, Aβ-positive subjects in the validation data were older than Aβ-negative subjects.

[0056] [Table 1]

[0057] 1-2. Genotyping and imputation Subjects were genotyped to analyze SNP markers using Illumina Asian Screening Array Beadchips (Illumina, USA) for the model development data and Affymetrix customized Korean chips (Affymetrix, USA) for the validation data. QC was performed using PLINK software (version 1.9). Subjects were excluded based on the following criteria: (i) call rate < 95%, (ii) discrepancy between reported sex and genetically inferred sex, (iii) statistical outliers in each population parameter, (iv) heterozygosity rate (average 5 standard deviations exceeded), and (v) related pairs (identified by identity by descent (IBD) ≥ 0.125) in the model development and validation datasets.

[0058] SNPs were excluded based on the following criteria: (i) CR (call rate) < 98%, (ii) MAF (minor allele frequency) < 1%, and (iii) P value < 1.0 × 10 by Hardy-Weinberg equilibrium test. -6 in the case of.

[0059] After QC, full-length gene imputation was performed using Minimac4 software with all reference haplotypes available in HRC-r1.1 on the Imputation Server at the University of Michigan, USA. For post-imputation QC, the following criteria were applied to exclude SNPs: (i) if the imputation quality was poor (R 2 ≤ 0.8), and (ii) when MAF ≤ 1%. As a result, a total of 4,906,407 SNPs were analyzed.

[0060] 1-3. Aβ PET acquisition and voxel-based morphological analysis Aβ PET images were acquired using a Discovery STE PET / CT scanner (GE Medical Systems, USA). PET images were acquired using FBB ( 18 F-florbetaben) or FMM( 18 F-flotemetamol) was administered intravenously and acquired for 20 min starting at 90 min. Aβ positivity or negativity was determined using visual assessment for FBB and FMM PET. A subset of subjects in the model development cohort (n=824) had Aβ PET data available for voxel-based morphometry (VBM) analysis.

[0061] For VBM analysis, the following preprocessing was performed using Statistical Parametric Mapping software version 12 (SPM, http: / / www.fil.ion / uc.ac.uk / spm) running in MATLAB (MathWorks 2014b): (1) co-registration of PET to T1-weighted structural MRI, (2) structural MRI segmentation and transformation matrix calculation, (3) normalization of PET to Montreal Neurological Institute (MNI) space, and (4) spatial smoothing with a Gaussian kernel of 8 mm full width at half-maximum.

[0062] Two reference regions (cerebellar cortex for FBB and pons for FMM) were used to calculate the standardized uptake value ratio (SUVR) for each PET image. Masks of the reference regions were obtained from the GAAIN website (http: / / www.GAAIN.org).

[0063] 1-4.Statistical analysis [1] GWAS analysis To detect associations between SNPs and Aβ positivity, adjusting for age, sex, and population subgroup effects, logistic regression analysis was performed using an additive model coded 0, 1, and 2 depending on the number of minor alleles. To correct for population subgroup effects, a principal components analysis was performed on the genetic data, using the first, second, and third principal components as adjustment variables. The reported P values ​​are two-tailed and are 5.0 × 10 based on previous studies. -8 A P value of less than 1.0 × 10 was statistically significant. -5A P value of < was defined as the statistically significant level. Genomic inflation was assessed by dividing the median of the chi-squared statistics derived from the GWAS analysis by the median of the chi-squared distribution with one degree of freedom (0.456). For validation analyses, one-tailed P values ​​were reported, taking into account that effects were expected to occur in the same direction as the results from the evaluation model development dataset. To confirm whether SNPs were associated with Aβ positivity independent of APOE genotype, conditional analyses were performed with additional adjustment for APOE genotype.

[0064] We also performed a P-value based meta-analysis and calculated summary effect sizes by applying weights reflecting the standard error of study-specific effect sizes.

[0065] As a result, referring to FIG. 1, the quantile-quantile plot of P values ​​showed no gene inflation (λ=1.008). Referring to FIG. 2, in the evaluation model development data, 61 significant full-length gene SNPs (P value <5×10) located on chromosome 19 were detected. -8 ) was confirmed (see Table 2 below). However, all significant SNPs were located within a 500 kb region surrounding the APOE gene, and the significance for the full-length gene was lost when correcting for the APOE ε4 allele. Outside the APOE region, 38 SNPs located on chromosomes 1, 7, 8, 12, and 22 showed suggestive significance (P value < 1.0 × 10 -5) (see Table 3 below). In the validation analysis, of the 38 suggestive SNPs, six SNPs (rs73375428, rs6978259, rs2903923, rs3828947, rs6958464, and rs11983537) located on chromosome 7 showed significant associations in the same direction as the results obtained from the evaluation model development data. Of the six SNPs, two SNPs (rs6958464 and rs11983537) were directly genotyped, and the remaining SNPs (rs73375428, rs6978259, rs2903923, and rs3828947) were analyzed by imputation. Notably, of the six SNPs, two (rs73375428 and rs2903923) showed significant associations with the full-length genotype in meta-analyses of the model development and validation datasets (see Tables 4 and 5 below). When the effect of the APOE ε4 allele was corrected, all six SNPs showed suggestive P values ​​with the full-length genotype, and the associations were verified in the validation data. The six identified SNPs were in high linkage disequilibrium (LD:r) with each other, as shown in Table 6 below. 2 >0.75), and therefore, of the six SNPs, rs6978259 was used as a representative for subsequent analysis.

[0066] [Table 2-1]

[0067] [Table 2-2]

[0068] [Table 3-1]

[0069] [Table 3-2]

[0070] [Table 4]

[0071] [Table 5]

[0072] [Table 6]

[0073] [2] Impact of newly identified SNPs After identifying the associated SNPs, the risk of the six SNPs to Aβ accumulation was calculated in total subjects and at each cognitive level (CU, aMCI, ADD).

[0074] We then performed VBM analyses to assess how SNPs were associated with regional Aβ accumulation after adjusting for the effects of age, sex, genetic PCs, APOE genotype, and Aβ PET tracer type.

[0075] To test the clinical utility of the newly identified SNPs, we developed a multivariable logistic model to predict Aβ positivity in each individual. To evaluate the performance of the logistic model, we measured the area under the curve (AUC) using receiver operating characteristic curve analysis (ROC).

[0076] For internal validation, 10-fold cross-validation was performed with 100 iterations using the evaluation model development data. The average AUC with 95% confidence interval (CI) of the model was reported. For external validation, the parameters estimated from the evaluation model development data were used to calculate the Aβ prediction performance between the validation data. R software (http: / / www.r-project.org) and MATLAB were used for the statistical analysis and visualization of the results.

[0077] As a result, the characteristics of the subjects relative to the number of minor alleles (C) of rs6978259 are shown in Table 7 below.

[0078] [Table 7]

[0079] As shown in Table 8 below, in the logistic model, the APOE ε4 allele was associated with a 5-fold increased risk of Aβ positivity (OR (odds ratio) = 5.330, 95% CI = 4.188-6.788), and rs6978259 was associated with a 2-fold decreased risk of Aβ positivity (OR = 0.521. 95% CI = 0.407-0.670). In subgroup analyses, the association between Aβ positivity and rs6978259 was significant in the CU and aMCI groups, but not in the ADD group, and the association of APOE ε4 was significant across all cognitive conditions.

[0080] [Table 8]

[0081] Referring to Figure 4, VBM analysis revealed that APOE ε4 was associated with increased Aβ accumulation in broad cortical regions of the frontal, parietal and temporal lobes. rs6978259 was associated with decreased Aβ accumulation in the precuneus, temporal and frontal regions, independent of age, sex, education level, APOE ε4 and Aβ PET tracer type.

[0082] To test the clinical utility of the APOE ε4 allele, various predictive models were developed, each for six SNPs (rs6978259, rs6958464, rs11983537, rs3828947, rs2903923 and rs73375428) (see Equation 1). Referring to FIG. 5, in a 10-fold cross-validation with 100 repetitions, model 1 including only individual characteristics (age, sex, and education level) had an AUC of 0.506 (95% CI = 0.500-0.512), model 2 incorporating the APOE ε4 allele into model 1 had an AUC of 0.723 (95% CI = 0.717-0.729), which showed improved prediction performance, and model 3 (Equation 1) incorporating rs6978259 into model 2 had an AUC of 0.748 (95% CI = 0.742-0.755), which further improved prediction performance. As a result of testing each model trained with the evaluation model development data on the validation data, model 3, which integrated individual characteristics, APOE ε4 genotype, and rs6978259, showed a higher AUC value than models 1 and 2 (AUC = 0.509 for model 1, AUC = 0.693 for model 2, AUC = 0.713 for model 3). Referring to Figures 6 to 10, similar results were observed for rs6958464, rs11983537, rs3828947, rs2903923, and rs73375428.

[0083] [3] Functional analysis Finally, we characterized the functions of the six identified SNPs using bioinformatic tools and previously reported results. First, we used the 1000 Genomes Project dataset to determine whether the MAFs of the SNPs identified in this study were similar to those of East Asian populations. To evaluate the genotype-specific expression of the identified SNPs in human brain tissue, we performed cis-eQTL (cis-expression quantitative trait loci) analysis using the Genotype-Tissue Expression portal (https: / / gtexportal.org).

[0084] As a result, as shown in Figure 3, it was revealed that rs6978259 is located in an intron of the CCDC146 gene (coiled-coil domain containing 146 gene).

[0085] These results suggest that there are differences in the types of SNPs highly associated with cerebral amyloid beta accumulation depending on the cohort, and that when analyzing Korean individuals in combination with individual characteristics such as gender, age, and education level, the APOE ε4 genotype as blood biomarkers and one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464, and rs11983537, it is possible to effectively select individuals who are likely to have early cerebral amyloid beta accumulation.

[0086] The present invention has been described above with a focus on the preferred embodiments. Those skilled in the art will understand that the present invention can be realized in modified forms without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an explanatory perspective, not a restrictive one. The scope of the present invention is defined in the claims, not the above description, and all differences within the scope of the equivalents should be interpreted as being included in the present invention.

Claims

1. a) collecting individual characteristics from individuals consisting of gender, age and education level; b) determining the APOE ε4 genotype and one or more SNP genotypes selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464 and rs11983537 in the individual's biological sample; c) combining the collected individual characteristics and the measured APOE ε4 genotype and SNP genotype to predict the likelihood of cerebral amyloid beta accumulation; A method for providing information for predicting the likelihood of cerebral amyloid beta accumulation, comprising:

2. The method according to claim 1, wherein the biological sample in step b) is one selected from the group consisting of whole blood, plasma, saliva, oral mucosa and hair.

3. The method according to claim 1, wherein the step c) is to set the individual characteristics, the APOE ε4 genotype and the SNP genotype as independent variables, respectively, and set the specific coefficients corresponding thereto as dependent variables, thereby creating a brain amyloid beta accumulation evaluation model by statistical analysis.

4. The method of claim 3, wherein the statistical analysis is performed using an analysis method selected from the group consisting of a linear or nonlinear regression analysis method, a linear or nonlinear classification analysis method, ANOVA, a neural network analysis method, a deep neural network analysis method, a genetic analysis method, a support vector machine analysis method, a hierarchical analysis or clustering analysis method, a hierarchical algorithm using a decision tree or a Kernel principal components analysis method, a Markov Blanket analysis method, a recursive feature elimination or an entropy-based recursive feature elimination analysis method, a forward floating search or a backward floating search analysis method, and combinations thereof.

5. The method according to claim 3 , wherein the brain amyloid beta accumulation evaluation model is represented by the following mathematical formula 1. [0010] (In the above formula 1, α is −0.612 when the SNP is rs6978259, −0.652 when it is rs6958464, −0.658 when it is rs11983537, −0.571 when it is rs3828947, −0.612 when it is rs2903923, and −0.633 when it is rs73375428.)

6. A composition for predicting the possibility of cerebral amyloid beta accumulation, comprising a formulation selected from the group consisting of a primer set, a probe and an antisense oligonucleotide that specifically binds to a base sequence including an APOE ε4 genotype or a SNP genotype, wherein the SNP genotype is selected from the group consisting of rs73375428, rs6978259, rs2903923, rs3828947, rs6958464 and rs11983537.

7. A kit for predicting the likelihood of brain amyloid beta accumulation comprising the composition described in claim 6.

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