KR20100114721A - Method of providing the information of single nucleotide polymorphism associated with blood pressure - Google Patents

Abstract

PURPOSE: A method for providing SNP related to blood pressure is provided to diagnose or predict obesity, diabetes, heart disease, hypertension, dyslipidemia and osteoporosis. CONSTITUTION: A single nucleotide polymorphism(SNP) marker comprises rs17249754 and is related to blood pressure. The SNP marker is used for diagnosing or predicting artery scleroma, hypertension, cardiac disease, obesity or diabetes. A kit for diagnosing or predicting artery scleroma, hypertension, cardiac disease, obesity or diabetes contains SNP rs17249754.

Description

Method of providing the information of single nucleotide polymorphism associated with blood pressure

The present invention relates to a method for providing monobasic polymorphism information associated with blood pressure.

With the completion of the Human Genome project in 2003, information on the human genome has been increasing rapidly, and the interest and importance in the field of disease genome research for identifying genetic factors of diseases are increasing.

Recently, lifestyle diseases such as diabetes and cardiovascular diseases are rapidly increasing due to changes in diet and living environment, but there are limitations in suppressing these diseases through the control of environmental factors. Is required.

The recent development of typing technology using single nucleotide polymorphism (SNP) chips has enabled the rapid analysis of large amounts of genotypes present in the human genome, and the study of large-scale genotype disease-related studies using them. It became.

As disease genome research using epidemiological cohorts, such as the Framingham Heart Study in the United States, is highly competitive internationally, there is a need to obtain genetic information using epidemiological cohorts through short-term intensive investment and research in Korea.

The Genome Center, National Institute for Disease Control and Prevention, conducted a genome epidemiological research project in 2001 to select 10,038 men and women aged 40 to 69 who live in Anseong (rural) and Ansan (small and medium cities). Every two years, we follow up. Genetic resources, including DNA and extensive and accurate epidemiological information collected from Anseong and Ansan epidemiological cohorts, will be used to achieve disease genome research using epidemiological cohorts to identify genetic and environmental risk factors related to disease.

The inventors have conducted pulses through a genome-wide association study; Blood pressure; Waist-hip ratio; New SNPs associated with bone strength have been discovered to complete the present invention.

It is an object of the present invention to provide a monobasic polymorphic marker that is associated with blood pressure.

In addition, another object of the present invention is to provide a kit for diagnosing or predicting a cardiovascular disease including the single nucleotide polymorphism.

It is still another object of the present invention to provide a method for providing monobasic polymorphism information associated with blood pressure.

In order to achieve the above object, the present inventors launched a large-scale genome analysis project (KARE, Korean Association Resource) for the residents of the epidemiological cohort. We performed full-length genome analysis using Affymetrix's high-density SNP chip selected for the entire Anho, Ansan community cohort (10,038) genome samples.

According to one aspect of the invention, the invention provides a monobasic polymorphic marker, characterized in that it is associated with blood pressure, consisting of a monobasic polymorphism rs17249754. The single nucleotide polymorphism marker can be used to diagnose or predict atherosclerosis, hypertension, heart disease, obesity or diabetes.

In addition, according to another aspect of the present invention, the present invention provides a kit for diagnosing or predicting atherosclerosis, hypertension, heart disease, obesity or diabetes, comprising a monobasic polymorphism rs17249754.

In addition, according to another aspect of the present invention, the present invention provides a method for identifying monobasic polymorphism rs17249754 in DNA obtained from an individual to provide monobasic polymorphism information associated with blood pressure. The monobasic polymorphism information associated with the blood pressure can be used to diagnose or predict atherosclerosis, hypertension, heart disease, obesity or diabetes.

Atherosclerosis; High blood pressure; heart disease; obesity; Tight correlations between diabetes and blood pressure have already been reported and are apparent in the art (see Eur Heart J. 2007 Jun; 28 (12): 1462-536).

Hereinafter, the present invention will be described in detail.

To identify genetic factors affecting physiologically important quantitative traits, we performed a full genome association study in 8,842 samples from a population-based cohort recruited in Korea. A wide association study was conducted. Here, quantitative trait refers to the action of each of a number of genes involved in a particular trait, and refers to a trait that represents a variation by the interaction of these genes and the interaction of the gene and the environment.

In the case of height and body mass index, most of the variants detected were consistent with those reported in European samples.

For other traits investigated, new loci of the six SNPs shown in Table 1 below are shown through replication of the predominant GWAS (Genome-Wide Association Study) signals in 7,861 independent Korean samples. ) Was identified.

Location and related genes of six new SNPs order rs  number Allele Chromosome location SNP location Related Genes Related Gene ID Remarks One rs17249754 A / G 12q21.33 88584717 ATP2B1 490 - 2 rs12731740 C / T 1q32.3 206091443 C1orf132 100128537 Intron 2 3 rs12110693 A / G 6q22.31 122199969 GJA1 2697 - 4 rs7776725 C / T 7q31.31 120820357 FAM3C 10447 Intron 1 5 rs1721400 C / T 7p14.1 38027310 SFRP4 6424 - 6 rs2074356 C / T 12q24.13 111129784 C12orf51 (FLJ30092) 283450 Intron 45

* (Genomic build: 36.3, based on dbSNP build 129)

In the above table, rs is an abbreviation of the reference sequence, and the following number is an accession number that distinguishes each single base polymorphism provided by a database called NCBI dbSNP.

When the pulse (pulse rate), have been mapped to the signal reaching the entire dielectric means chromosome ^{1q32 (rs12731740, p = 2.9x10 -9} ) and ^{6q22 (rs12110693, p = 1.6x10 -9} ), the latter is the coding sequence of GJA1 At ~ 400kb.

In the case of systolic blood pressure, most potent associations were related to variants near chromosome 12q21 and ATP2B1 genes (rs17249754, p = 1.3 × 10 ⁻⁷ ).

For the waist-hip ratio, variants on chromosome 12q24 (rs2074356, p = 7.8 × 10 ⁻¹² ) showed a reliable association.

In addition, two loci affecting bone density at multiple sites were identified. On chromosome 7q31, FAM3C Rs7776725 in the gene was associated with bone strength in the radius (p = 1.0 × ^{10 −11} ), tibia (p = 1.6 × ^{10 −6} ) and heel (p = 1.9 × ^{10 −10} ). On chromosome 7p14, SFRP4 Peurijeul DE (frizzled) showed a consistent association in the three areas that are close to the same as the rs1721400 gene mapping (respectively ^{p = 2.2x10 -3, p = 1.4x10} -7, p = 6x10 -4).

Innovative advances in high-efficiency genotyping technology have led to genome wide association (GWA) studies revealing the relationship between DNA sequence variation and increasing disease and biomedical traits. To date, most studies have focused on samples of European origin, and it is not clear whether the same loci contributes to Asian genetic relationships.

The Korean Association of Genome Analysis (KARE), launched in 2007, is the largest of the 10038 participants (age: 40-69) in the Anseong (n = 5018) and Ansan (n = 5020) community-based cohorts. GWA analysis was undertaken. These cohorts, conducted in 2001 as part of the Korean Genome Epidemiology Study (KoGES), provide extensive phenotypic data of more than 260 traits, but in the present invention six biomedical quantitative traits, "body mass" Index, waist-hip ratio, height, pulse rate, blood pressure, bone strength ".

10004 DNA were obtained from a total of 10038 participants, all of which were genotyped with Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix Inc., USA). Genotyping was performed using a Bayesian Robust Linear Modeling using Mahalanobis Distance (BRLMM) algorithm and standard QC procedures. Sequentially, 38,364 markers with HWE p-value <10 ⁻⁶ , 17,926 with genotype call rates below 95%, 92,050 with MAF <0.01 were discarded and 352,228 SNPs were left for subsequent analysis. The genotype match rate was greater than 99.7% in 20 double samples. Samples showing low call rate (<96%, n = 401), sample contamination (n = 11), sex inconsistency (n = 41), genotyping similarity (n = 608) and severe disease (n = 101) After removal, 8,842 GWA genotypes were included in the association analysis. In addition, those who consumed drugs that could affect related traits were further removed from the trait-specific assay.

The MAF plot of the AnNP and Ansan cohort SNPs shows that the MAFs of SNPs are very similar in both cohorts (FIG. 2-A).

2-d.f. For comparison of genotype frequencies in Ansan and Ansan cohorts. Quantitative-Quantile (Q-Q) analysis of logistic regression statistics confirmed the genetic homogeneity of these KARE study populations (Figure 2-B). In addition, the above facts were reaffirmed through multidimensional scaling (MDS) analysis and principal component analysis, and the characteristics of the Anseong-Ansan cohort were based on the JPT (Japanese) / CHB (Chinese) configuration of HapMap. Similar to the features.

In order to increase the range of common variants and capture additional correlation signals, SNP missing value prediction was performed through IMPUTE using a JPT / CHB component of HapMap. After predicting SNP missing values, a total of 2156535 SNPs were analyzed for quantitative trait correlation using the SNPTEST program (http://www.stats.ox.ac.uk/~marchini/software/gwas/snptest.html). .

Step 1 In the full genome association scan, adjust age and gender using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) and SAS program (version 9.1; SAS institute Inc., USA) Six traits, body mass index (BMI); Waist-hip ratio (WHR); key; pulse; Systolic blood pressure (SBP) and diastolic blood pressure (DBP); And a single marker test of association with bone strength measured in the radial and tibia. Despite limited evidence of genetic homogeneity and overall population stratification of the two cohorts described above, the recruitment area was adjusted because of differences in trait distribution between Ansan and Ansan (in particular, WHR and SBP). The Q-Q plot for the trend test shows that the distribution of observed p-values for most traits deviates from the expected P values only at the extreme tails (FIG. 3). The QQ plots for heights were exceptionally more pronounced, but this pattern was observed in other large-scale analyzes of the GWA data of this phenotype, showing a fairly large number of true signs of adult height revealed by this analysis ( 3).

The results of the genome-wide scan through the trend test are shown in FIG. 5. A total of 22 independent signals (pairwise linkage disequilibrium statistic r ² <0.5) for selected traits that passed any stage 1 threshold for validation (i.e., combining P values <1.0x10 ^-5 and p <0.01 in Ansan and Ansan). , MAF = 0.05) was confirmed (Table 2). Table 2 below shows the association (22 SNPs) between SNPs and quantitative traits in the GWA data and validation populations.

Visual inspection of cluster plots of these SNPs confirmed the absence of SNP-specific genotyping or allele-read artifacts (data not shown).

In a Phase 2 validation analysis, 19 out of these 22 SNPs were genotyped in an independent population of 7861 Korean subjects recruited to the Health 2 cohort from five different regions.

Genotyping was performed through TaqMan or Golden Gate analysis. The average genotyping success rate was 99.3% (TaqMan) and 99.9% (Golden Gate), and no analysis was lower than the 98.9% call rate. Duplicate genotyping of 1% and 2.5% of the samples showed 100% and 99.8% match rates in TaqMan and Golden Gate, respectively, indicating high genotyping reproducibility for both methods. Association analysis was performed using a trend test (additional model) after adjustment of age, gender and recruitment area.

For all traits except bone strength, Health2 validation samples directly provided equivalent phenotypic measurements, and meta-analysis of KARE GWA and Health2 validation data was performed using the R program (v2.7.1). Nine of the 15 SNPs obtained from verification of BMI, WHR, height, blood pressure or pulse rate showed consistent evidence for verification in Health2 (p <0.05). For all nine SNPs, the combined evidence of association was strong (p <2 × 10 ⁻⁷ ).

For the four SNPs showing evidence of association in the KARE GWA study of bone strength measurements in the radial or tibia, an equivalent meta-analysis was not possible because the measurement of bone strength in Health2 was limited to the calcaneus (heel bone). However, for two of the four related SNPs (rs7776725, rs1721400), the calcaneal bone strength measurements were consistently correlated (p <0.05) in both KARE GWA and strongly associated with bone strength (p <2 × 10 ⁻⁷ ).

In addition, for 11 SNPs showing evidence of verification, likelihood ratio tests were performed in a linear regression model to find evidence of interaction with gender. Only one SNP generated slight evidence of this interaction (rs17249754, gender: p = 3.9 × 10 ⁻³ ) but was not identified in the validation sample.

Retention water (BMI); Waist-hip ratio (WHR); key; Blood pressure; pulse; The correlation analysis results for bone strength are as follows.

end. Stay water ( BMI )

In BMI's KARE GWA analysis, two SNPs passed the stage 1 threshold. Of these, rs17178527 has not been verified. The facts validated in rs9939609 (p = 1.5x10 ^-7 , effect size of 0.335 ± 0.07 (kg / m ² ) in GWA analysis) were consistent with well-reported signals from the FTO (associated body fat and obesity) genes (Table 2). . In addition, the KARE GWA data is MC4R (rs17782313, p = 1.9x10 ^-4 ) and CTNNBL1 (rs6067731, p = 0.02) provided corroborating facts of already reported BMI signals near the gene (data not shown).

I. Waist-hip ratio ( WHR )

Based on the GWA analysis, two signals that passed the one-step threshold were selected for verification. Of these, rs17089410 located on chromosome 13q21.33 has not been validated. In contrast, rs2074356, located on chromosome 12q24.13 the 45th intron of the C12orf51 transfer member exhibited a strong evidence of the verification to create the effect size of the associated p-value 7.8x10 ^-12, and -0.007 ± 0.001 (Table 2, Fig. 6) . The predicted transcript function of C12orf51 is not yet known. The interval of maximal association includes other genes of potential interest, including PTPN11 . Proteins encoded by PTPN11 are members of the protein tyrosine phosphatase (PTP) family known as signaling molecules involved in various cellular processes, including cell growth, differentiation, cell division cycle and cancer transformation.

All. key

Several sequence variants that were found to contribute to key variations in Western European samples were observed in GWA analysis (Table 2). Among them, HMGA1 , PLAG1 , ZBTB38 and EFEMP1 SNPs near or within were validated in the two-stage data set (total combined p-value <10 ⁻⁷ ) (Table 2). Analysis of missing predicted SNPs provided further confirmation of the first signal observed in European lineage samples (including those located near HMGA2 , CABLES1 , C1orf19 and histone family 1 genes) (data not shown).

la. Blood pressure

Of the two SNPs selected for the validation of the blood pressure phenotype (cardiac contractile and cardiac diastolic), only rs17249754 was identified. Evidence of the association was -1.309 ± 0.266 in diastolic blood pressure (combined p = 1.3x10 ^-7 , GWA analysis) rather than cardiac diastolic blood pressure (combined p = 3.0x10 ^-6 , magnitude of effect of -0.882 ± 0.181 (mmHg) in GWA analysis). (mmHg) effect size, Table 2, Figure 6) was stronger. The most likely local candidate gene is ATP2B1 (ATPase, Ca ⁺⁺ transporting, plasma membrane 1), whose product is in the P-type primary ion transport ATPase family, characterized by the formation of aspartyl phosphate intermediates in the reaction cycle. Belong. These enzymes are known to play a key role in the maintenance of intracellular calcium starbursts by removing bivalent calcium ions for very large gradients in eukaryotic cells. Mice without a murine homolog of ATP2B4 have been reported to exhibit a vascular phenotype (loss of vasoconstriction) on the background of one copy of the murine homolog of ATP2B1 , indicating that ATP2B1 is responsible for phasic contraction. As it suggests that it can serve as a regulator locus, we have found evidence of gene-gene interactions between variants of ATP2B1 and ATP2B4 in the GWA data set . As a result, Rs10506974 of ATP21 and rs6594013 and rs12035521 of ATP2B4 were found to interact (p values: p = 5.8x10 ^-3 , p = 4.0x10 ^{-3, respectively} ).

hemp. pulse

Based on the GWA analysis, three SNPs passed the stage 1 threshold and were selected for verification. Of these, two SNPs (rs12731740 and rs12110693) reached directional coherence p <0.05 in two-step analysis (Table 2, FIG. 6). Of these signals, rs12731740 (p = 2.9x10 ^-9 , effect size of 0.993 ± 0.195 (BPM) in GWA analysis) is located on chromosome 1q32.2 close to the receptors of complement activation gene cluster (RCA). . Three genes CD46 , C1orf132 , and CD34 are located near this signal. The protein encoded by CD46 is a type 1 membrane glycoprotein that acts as a cofactor to protect complement activation in host cells. C1orf132 encodes a hypothetical protein of unknown function, and the protein encoded by CD34 is involved in early hematopoiesis and in attaching stem cells to the bone marrow extracellular matrix and / or stromal cells. It is known. Thus, the functional link to pulse variation is not yet clear at this stage.

The second verification signal of the pulse, rs12110693, is located on chromosome 6q22.31 (p = 1.6 × 10 ⁻⁹ , effect size of 0.573 ± 0.118 (BPM) in GWA analysis). The only candidate transcript close to the SNP is LOC644502, whose function is unknown. The GJA1 (gap junction protein, alpha 1, 43 kD) gene, located ~ 400kb away from rs12110693 , is more potentially relevant. In KARE GWA scan, a protein that is encoded in the GJA1 ~ 7kb upstream (upstream) of GJA1 are well-known to function in the simultaneous contraction of the heart, is a major component of cardiac gap junction (gap junction). Recently, muscle-specific microRNAs have been reported to regulate cardiac arrhythmia inducing potential by targeting GJA1 . In addition, it has been reported that post-infarct arrhythmia is prevented by the mixing of GJA1 expressing cells, and heartbeat obstruction is prevented in streptozotocin-induced diabetic rat heart by altered expression of GJA1 . This indicates the importance of GJA1 in maintaining heart rate.

bar. Bone strength

KARE GWA scans of bone strength in the radial or tibia detected five SNPs that passed the one-step threshold with rs7776725, which signaled both. In two of these, calcaneal bone strength measurements in the two-stage samples showed direction-consistent evidence of association (p <0.05) (Table 2, Figure 6).

rs7776725 is the radius (p = 1.0x10 ^-11 , effect size of 0.222 ± 0.033 in KARE), and tibia (p = 1.6x10 ^-6 , 0.155 ± 0.032 in KARE), and heel (p = 1.9x10 ^{− 10} , -0.228 ± 0.036 in Health 2) showed the strongest evidence of association with bone strength. The SNP is located on chromosome 7q31.31 in the first intron of FAM3C (family with sequence similarity 3, member C) (Table 3). Table 3 below shows SNPs showing strong evidence of association with bone strength-related traits in stage 1 (GWA) and stage 2 samples.

FAM3C , already known as the predicted osteoblast protein, encodes a novel cytokine required for epithelial mesenchymal transition, tumorigenesis and metastasis in epithelial cells.

The second locus of interest for bone mineral density is centered on rs1721400 on chromosome 7p14.1. The SNP is tibial (p = 1.4x10 ^-7 , -0.149 ± 0.028 effect size in KARE), radial (p = 2.2x10 ^-3 , -0.088 ± 0.029 in KARE) and calcaneus (p = 6.0x10 ^-4 , Health2 There is direction evidence of association at -0.11 ± 0.032). Of the three genes in close proximity, TXNDC3 , SFRP4 and EPDR1 , the obvious candidate is SFRP4 (secreted frizzled-related protein 4). Said member of the SFRP family includes a cystein-rich domain homologous to the candidate Wnt-binding site of the frizzled protein. SFRP, which acts as a soluble modulator of Wnt signaling, is well known to be involved in bone formation and resorption. QTL separation analysis of peak bone strength in aging-promoted mouse P6 (SAMP6) suggested that mouse ortholog Sfrp4 is involved in low bone strength of SAMP6. In addition, overexpression of osteoblast-targeted Sfrp4 in transgenic mice was associated with a decrease in bone strength.

The KARE project covers a wide range of quantitative traits and major biomedical lifestyle-related diseases, including type 2 diabetes (T2DM), dyslipidemia, hypertension, obesity, and osteoporosis. Designed to identify genetic factors that affect In the present invention, the results of large-scale GWA and replication analyses that successfully confirm many strong associations between genetic variants and quantitative traits including WHR, bone strength, blood pressure, pulse, height, and BMI are disclosed.

This study is the first high-density large-scale GWA scan performed in East Asian populations that target the quantitative traits. As expected, this study not only revealed a number of new associations that could reflect race-specific susceptibility loci), but also validated many of the previously reported associations in populations of European prosperity.

Overlaps with previously reported facts were most apparent in the analysis of anthropometric traits (particularly BMI and stature), perhaps because they have been the focus of some large GWA meta-analysis. For example, the association of BMI in the FTO was evident in Koreans as well as in Europeans. In other loci, such as HMGA2 , which affect adult height, effect size assessment (0.27 beta in Korean vs 0.48 in European) and allele frequency (T "key-growth" allele, 74% in Korean, 49% in European) There was an obvious racial difference.

The genetic information provided by the KARE project provides additional case-control association studies in Korea by providing GWA data from a large set of East Asian population-based controls, as well as providing new information on the genetics of the aforementioned traits. Can promote.

As noted above, the present invention provides genetic monobasic polymorphic markers associated with waist-hip ratio, pulse rate, blood pressure, or bone strength. The markers can be used to diagnose or predict lifestyle diseases such as obesity, diabetes, heart disease, hypertension, dyslipidemia and osteoporosis. Ultimately, the disease-related genetic information may be used to lower the incidence of disease and realize personalized and predictive medicine using individual genetic information.

Hereinafter, the components and technical features of the present invention will be described in more detail with reference to the following examples. However, the following examples are merely to illustrate the content of the present invention is not limited to the scope of the invention. The documents cited in the present invention are incorporated herein by reference.

Example

Example  One: Of Subjects  selection

Subjects were recruited from the Anseong (Rural) and Ansan (Small and Medium City) cohorts for GWA study. As part of the Korean Genome Epidemiology Study (KoGES), which began in 2001, the initial sample included 5,018 (Anseong) and 5,020 (Ansan) participants aged 40 to 69 years. The overall study design of the present invention is shown in FIG.

The sampling base of the two cohorts is located in Gyeonggi Province, adjacent to Seoul, the capital of South Korea. Both cohorts were designed for longitudinal prospective studies and adopted the same research strategy.

Participants were surveyed every two years since baseline with a third follow-up study (including family members) planned to be completed in 2008. More than 260 traits have been extensively investigated through epidemiological studies, physical tests, and laboratory tests applied to Ansan and Ansan cohort members.

Subjects in a replication study were selected from a second population-based cohort sample provided by the Health 2 study. This study integrates subjects from the cohorts of Wonju, Pyeongchang, Gangneung, Geumsan, and Naju regions of Korea, and adopts the same standard for quantitative trait measurement as those employed in Anseong and Ansan cohorts. The main exception to this is bone strength, which in the Health 2 cohort is related to heel bone dimensions (as opposed to radial and calcaneus in Anseong and Ansan). Of a total of 8,500 participants in the Health 2 cohort, 7861 subjects were selected for a validation analysis based on age (anxiety / ansan, 40-69 years old) and information on ancillary diseases and medications.

2 shows a comparison of genetic backgrounds between subjects in Ansan and Ansan cohorts. A is a MAF plot that tested the difference in allele frequencies of two cohorts. MAFs of the remaining 352,228 SNPs after SNP marker quality control were plotted between Ansan and Ansan cohort. B is a Quantile-Quantile (QQ) plot of the test statistics obtained from 2-df logistic regression analysis. This plot examined the differences in genotype frequencies between subjects in Ansan and Ansan cohorts. In order to reduce the effects of genotyping error and low cell count in this assay, each cohort had a missing rate> 0.05, MAF <0.05, and SNP with poor clustering after visual examination of the genotype cluster plot. SNPs with HWE p <10 ⁻³ were excluded first. The final data set of 311,917 SNPs for this analysis was then included by re-incorporation of SNPs with 0.01 ≦ MAF ≦ 0.05 and SNP overpass = 0.01. Logistic regression analysis was performed with control of age and gender (2-df). In the QQ plot, the dark areas are 95% concentration bands. The genomic control inflation factor measured was 1.061.

Example  2: Zino Typing ( Genotyping And quality control

The majority of genomic DNA genotyped on Affymetrix Genome-Wide Human SNP array 5.0 was isolated from peripheral blood extracted from Ansan and Ansan cohort participants. If the DNA sample for genotyping was inadequate for degeneration or the like, it was replaced with an Epstein-Barr virus-immortalized lymphoblastoid cell lines (LCL). Low concentration DNA samples were amplified prior to genotyping according to the manufacturer's protocol (Qiagen Inc., Valencia, CA, USA). Genotyping was performed identically for all three DNA sources. All 10,004 samples were genotyped using 500 ng of genomic DNA with Affymetrix Genome-Wide Human SNP array 5.0 (Affymetrix, Inc., Santa Clara, Calif.). A Bayesian Robust Linear Modeling using Mahalanobis Distance ( BRLMM) genotyping algorithm was used for genotype calling 500,568 SNPs. Samples and types with high error genotype call rate (> 4%, n = 401), high heterozygosity (> 30%, n = 11), gender inconsistencies (n = 41) Samples from individuals with cancer were excluded from subsequent analysis with related or identical individuals whose calculated mean pairwise identity-by-state (IBS) values were higher than those evaluated from first-degree relatives of Korean sibling samples. (> 0.80, n = 601). Samples whose gender was assumed to be genotype inconsistent with clinical records were retested for gender identification using the SNaPshot ^® Multiplex System (Applied Biosystems, USA). Methods for evaluating heterojunction and IBS are known. High genetic fault call rate (> 5%), low MAF (<0.01), and with the Divine Berg equilibrium (Hardy-Weinberg equilibrium) with a significant difference markers (p <1x10 ^-6) from the 8,842 people surveyed from the object A total of 352,228 markers were left out.

In the validation study, genotyping of a total of 7861 subjects was possible for 19 of the 22 strongest signals selected based on the GWA scan survey. Allele separation analysis using the TaqMan ^™ reaction gave 12 SNPs (rs17178527, rs2074356, rs17089410, rs6918981, rs17038182, rs10513137, rs13273123, rs17249754, rs12731740, rs12110693, rs7776725, and rs1721400). Seven SNPs (rs9939609, rs600130, rs3791675, rs715987, rs11576175, rs9525667, and rs550677) were genotyped by GoldenGate assays in a set of 384 complex SNPs (Illumina Inc., USA). For three SNPs (rs2079795, rs41464348, and rs6974574), no functional analysis could be designed on any platform. About 1% -2.5% of the sample was duplicated genotyped as a quality control. Only SNPs that matched over 99% duplicates and met 98% genotype success rate were included in the subsequent association analysis.

Example  3: Statistical Analysis

3-1. Identity-by-state (IBS) analysis

Pruned IBS between individuals was calculated using a subset of pruned markers (44724 SNPs) in close linkage equilibrium. IBS analysis was performed using the PLINK software package (http://pngu.mgh.harvard.edu/purcell/plink/) and the R statistical package (http://r-project.org). The calculated genome-wide mean IBS of the pair between each individual was used to assess individuals suspected of having a first degree relative or a group of relatives closer to the first degree relative.

3-2. Quantile - quantile  Plot

The distribution of the observed p-values of the corresponding SNPs was plotted against the theoretical distribution of the p-values expected to construct a quantile-quantile (QQ) plot of each quantitative trait. Concentration bands within dotted lines in all QQ plots represent 95% point-by-point confidence intervals and are derived by calculating the 2.5 ^th and 97.5 ^th percentiles of the p value under random sampling and null hypothesis. The genomic control inflation factor λ was calculated by dividing the median χ ² statistic by 0.456. The genomic control was not corrected in the KARE GWAS analysis because this expansion was adequate and the plots of QQ, MDS and PCA for MAF, genotype frequency differences suggested that the population structure could be ignored for KARE GWAS samples.

FIG. 3 shows a Q-Q plot for six quantitative traits (preventive water; waist-hip ratio; height; cardiac contractile and diastolic blood pressure; pulse; bone strength as assessed by T scores in the radial and tibia). The observed P value (y-axis) for each trait was compared with the expected P value under null distribution. The area between the dashed lines represents a 95% concentration band.

3-3. Multidimensional Scaling Multidimensional scaling (MDS) and principal component analysis (PCT)

To find evidence of the population infrastructure, MDS analysis and PCA were performed using 44,724 SNP markers from all KARE and HapMap 270 individuals (FIG. 4). In the case of MDS analysis, the dimensions are defined in the KARE and HapMap groups using PLINK (http://pngu.mgh.harvard.edu/purcell/plink/) and R (http://r-project.org). Calculations were based on identity-by-descent (IBD) pairwise distances among all individuals. The first dimension value was plotted against the second dimension value. Individuals of the KARE, Japanese (JPT), Chinese (CHB), Yorban (YRI), and Caucasus (CEU) groups were black, red, blue, green, and Plot with purple dots. KARE samples were clustered with HapMap's Chinese (CHB) and Japanese (JPT) configurations. For PCA, KARE and HapMap samples were plotted based on the first two eigenvectors obtained by smartpca (one of the program modules of the EIGENSTRAT soft package). Individuals of the KARE, Japanese (JPT), Chinese (CHB), Yorban (YRI), and Caucasian (CEU) groups were represented by closed cyan squares, open pink squares, blue asterisks, dark green crosses, and red crosses. In addition, KARE samples were clustered with HapMap's Chinese (CHB) and Japanese (JPT) configurations.

3-4. Zino Typing  Cluster plot

The genotype call for Affymetrix Genome-Wide Human SNP array 5.0 was determined in a batch of about 200-300 samples using the BRLMM algorithm. When creating a cluster plot of a given SNP, the entire signal information was processed to create an integrated summary file. The summary file was then translated into cluster plot format by an algorithm similar to SST1.0 (SNP signal tool, Affymetrix ^™ ) (data not shown).

3-5. Association analysis

Association analysis was performed using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) and SAS program (version 9.1; SAS Institute Institute, Cary, NC, USA). After adjusting for age, sex and recruitment area, the association of six selected quantitative traits was tested by linear regression analysis with additional models (1-d.f.). For most traits, raw measures were normally distributed within the KARE study population and no conversion was required. However, except for bone strength, each measurement of speed of sound (SOS) was converted to a T-score before analysis. For each trait assay, subjects who received drug treatment that could modify the potential trait value were excluded. The results of the KARE GWA and validation studies were integrated with an inverse-variance meta analysis method that estimates fixed effects with Cochran's Q test, which is used to evaluate heterogeneous comparative studies. All meta-analysis calculations were performed using the R program (version 2.7.1).

Figure 5 examines the association of GWA SNP data with six quantitative traits by the 1-df trend assay. Trend test p values were plotted on the y axis as -log ₁₀ (p) according to the chromosomal location on the x axis. In the KARE study population, SNPs with p <10 ⁻⁵ are marked red if they show evidence of association (p <10 ⁻² ) with both Anshn and Ansan cohorts, and green if the evidence is limited to Anshn or Ansan.

Figure 6 shows a novel loci on the chromosome of six SNPs showing strong association evidence with the quantitative traits of the present invention. In the top panel, -log ₁₀ (p-values) using the trend test shows the SNP distributed in the genomic region 0.5 Mb on the side of the most strongly associated signal. Black dots represent SNPs typed in the KARE study, gray dots represent SNPs whose genotypes have been substituted, red diamonds at each locus represent the strongest signal detected in the entire genome scan, and blue diamonds represent validation studies. Green diamonds represent the results obtained from the integrated analysis of KARE and validation. Genomic location is based on NCBI genome build 36 and dbSNP build 128. Signal boundaries, indicated by vertical dashed lines, were selected using recombinant hotspots flanking the strongest signal. If the recombinant hotspot did not appear within the 1 Mb window surrounding the strongest signal, 100 kb of genomic region flanking the strongest signal was selected. In the middle panel, the recombination rate (cM per Mb) evaluated from the Phase II HapMap is shown as black vertical lines. Short black horizontal lines along the 1 Mb genomic region indicate recombinant hotspots. The red line represents the accumulated genetic distance (cM) from the strongest signal. The top track in the bottom panel shows the location of genes known as red boxes and green lines representing exons and introns, respectively. The genes within the signal boundary are listed in the upper left portion of each plot. Genetic information was obtained from NCBI build 36. The bottom track of the bottom panel shows sequence conservation in 28 vertebrates. Blue boxes indicate areas that were highly conserved (sequence conservation score ≧ 0.6 based on phastCons information obtained from UCSC hg18). Through the six signals described in the present invention, genes were functionally identified within the signal boundary.

3-6. SNP Missing value  Prediction

SNP missing value predictions were performed using the IMPUTE program. Based on NCBI build 36 and dbSNP build 126, we used 90 individuals from JPT and CHB founders in HapMap as a reference panel containing 3.99 million SNPs (HapMap release 22). After removing SNPs with MAF <0.01 and SNP missing rate> 0.05, the remaining 1.8 million missing predicted SNPs were integrated with the directly typed KARE SNPs for association analysis with the selected quantitative trait. Association analysis of missing predicted SNPs was performed with the SNPTEST program.

Example  4: monobasic polymorphism for diagnosing or predicting cardiovascular disease Kit

In the present invention, a kit for diagnosing or predicting a cardiovascular disease including a single nucleotide polymorphism rs17249754 associated with blood pressure was prepared. Genomic DNA was isolated and fluorescently labeled from blood of a subject to diagnose or predict cardiovascular disease onset or likelihood using the method described in Example 2 above. Hybridization of the SNP markers prepared above and fluorescently labeled genomic DNA with 2 × SSC was performed at 42 ° C. for 4 hours. By confirming whether the subject has all or part of the SNP according to the present invention, the predictability of the onset of cardiovascular disease and the sensitivity to cardiovascular disease were measured.

While the present invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. You will know. In addition, many modifications may be made to adapt a particular situation and material to the teachings of the invention without departing from the essential scope thereof. Accordingly, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention be construed as including all embodiments falling within the scope of the appended claims.

Figure 1 shows the overall study design of the Korean genome epidemiological investigation.

Figure 2 compares the genetic background between the Ansan and Ansan cohort subjects.

FIG. 3 shows a Qant-Quantile (Q-Q) plot for six quantitative traits. (BMI, Body Mass Index; WHR, Waist to Hip Ratio; SBP, Systolic Blood Pressure / DBP, Diastolic Blood Pressure; BD-RT, Bone Strength in Radius, BD-TT, Bone Strength in Tibia).

FIG. 4 illustrates the results of multidimensional scaling (MDS) and principal component analysis (PCA).

5 shows the results of a genome-wide scan of six quantitative traits.

6 depicts the novel loci of six SNPs showing strong association evidence.

Claims (5)

Monobasic polymorphism A monobasic polymorphic marker consisting of rs17249754, which is associated with blood pressure. The method of claim 1, A single nucleotide polymorphism marker characterized by diagnosing or predicting atherosclerosis, hypertension, heart disease, obesity or diabetes. A kit for diagnosing or predicting atherosclerosis, high blood pressure, heart disease, obesity or diabetes, comprising monobasic polymorphism rs17249754. A method for identifying monobasic polymorphism rs17249754 in DNA obtained from an individual to provide monobasic polymorphism information associated with blood pressure. The method of claim 4, wherein A method for providing monobasic polymorphism information associated with blood pressure, characterized by diagnosing or predicting atherosclerosis, hypertension, heart disease, obesity or diabetes.

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