Polygenic genetic risk score for stroke and device for assessing risk of stroke and use thereof

Abstract

Provided are a polygenic risk score (PRS) for stroke, an apparatus for assessing the risk of stroke, and use thereof, specifically, a use of a reagent for detecting individual information in the manufacture of a detection apparatus for assessing the risk of stroke, the individual information including 280 stroke-related single nucleotide polymorphism sites, and preferably including one or more of CAD, SBP, WC, T2D, TC, PP, and AF-related single nucleotide polymorphism sites. Furthermore, by integrating the polygenic risk score with conventional risk factors, re-stratification of stroke risk can be achieved, which has important implications for primary prevention of stroke.

Description

[Technical Field]

[0001] The present invention relates to a polygenic risks score (PRS) for stroke and an apparatus for assessing the risk of stroke, and uses thereof. [Background technology]

[0002] Stroke death is one of the major health threats worldwide. The lifetime risk of stroke among adults aged 25 years and older worldwide is estimated to be approximately 25%, with the highest risk of 39% in East Asian populations. In China, stroke has become the leading cause of death among the population, with 2.07 million deaths due to stroke in 2017. Therefore, early identification of high-risk groups and healthy lifestyle management and drug interventions for major risk factors (e.g., hypertension, diabetes, dyslipidemia, etc.) are of great significance for the primary prevention of stroke in China and around the world.

[0003] Stroke is a complex disease caused by both genetic and environmental factors. Genome-wide association studies (GWAS) have identified at least 35 genetic susceptibility genes associated with stroke and approximately 100 genes associated with stroke-related phenotypes, including blood pressure, type 2 diabetes (T2D), lipid levels, body mass index (BMI), and atrial fibrillation (AF). Identification of these genetic variants will help develop cardiovascular disease risk predictors and guide primary prevention. Recently, polygenic risk scores (PRSs) for stroke that integrate information from multiple genetic variants have been successfully developed and applied to clinical evaluation of stroke risk prediction.

[0004] However, existing genetic scores are mostly based on European populations (Stroke 2014;45:394-402, Stroke 2014;45:403-412, Stroke 2014;45:2856-2862, BMJ 2018;363:k4168, JAMA cardiology 2018;3:693-702, Nat Commun 2019;10:5819), and studies on non-European populations are rare. The epidemiological characteristics of stroke vary by country, and East Asian populations, particularly Chinese populations, have a much higher incidence of stroke and hemorrhagic stroke than Western populations. Therefore, it is important to develop a stroke PRS for East Asian populations, particularly Chinese populations, and to rigorously evaluate its genetic risk prediction value in prospective cohorts.

[0005] Stroke risk and intervention benefits may also differ due to significant differences in environmental risk factors (lifestyle, diet, and behavior) and gene-environment interactions in different populations.

[0006] Furthermore, whether re-stratification of stroke risk can be achieved by integrating polygenic genetic risk scores with traditional risk factors has important implications for primary stroke prevention. Summary of the Invention

[0007] One of the objects of the present invention is to provide stroke-associated single nucleotide polymorphism sites and an onset risk assessment system applicable to East Asian populations.

[0008] Through intensive research and practical detection analysis, the present inventors have identified a group of stroke risk-related genes associated with East Asian populations, including 280 stroke-associated single nucleotide polymorphism sites, and by detecting these stroke-associated single nucleotide polymorphism sites, the risk of stroke in East Asian populations can be successfully assessed. The present invention further identifies single nucleotide polymorphism sites associated with CAD, SBP, WC, T2D, TC, PP, and AF, and by further detecting these associated single nucleotide polymorphism sites, the risk of stroke in East Asian populations can be successfully assessed.

[0009] Specifically, in one aspect, the present invention provides use of a reagent for detecting individual information in the manufacture of a detection device for assessing the risk of stroke, wherein the individual information includes the following single nucleotide polymorphism site information: Stroke-related single nucleotide polymorphism sites: rs10051787, rs10093110, rs10139550, rs10160804, rs10237377, rs10260816, rs10267593, rs10278336, rs1037814, rs10507248, rs10512861, rs10745332, rs10757274, rs10773003, rs10824026, rs10857147, rs10953541, rs10968576, rs11099493, rs1116357, rs11206510, rs11222084, rs11257655, rs11509880, rs1152591, rs11557092, rs11601507, rs11604680, rs11624704, rs11677932, rs1173766, rsms117601636, rs117711462, rs11787792, rs11810571, rs11838776, rs11869286, rs12027135, rs12037987, rs12202017, rs12229654, rs12415501, rs12438008, rs1,2445022, rs12500824, rs1250229, rs12549902, rs12571751, rs12581963, rs12692735, rs12718465, rs12801636, rs12897, rs12927205, rs12932445, rs12936587, rs12946454, rs13143308, rs13209747, rs1321309, rs13216675, rs13233731, rs13342232, rs1334576, rs13359291, rs1,344653, rs1359790, rs1367117, rs13723, rs1412444, rs1436953, rs1470579, rs1495741, rs1508798, rs151193009, rs1552224, rs1591805, rs16844401, rs16849225, rs16858082, rs16896398, rs16967013, rs16999793, rs17030613, rs17080091, rs17087335, rs17122278, rs17135399, rs173,01514, rs173396, rs17358402rs17477177、rs17514846、rs17581137、rs17612742、rs17680741、rs17791513、rs180327、rs181359、rs1861411、rs1868673、rs1870634、rs1887320、rs1892094、rs1902859、rs191835914、rs1976041、rs1982963、rs2000813、rs2028299、rs2057291、rs2068888、rs2074158、rs2075291、rs2075423、rs2107595、rs2128739、rs2145598、rs216172、rs2213732、rs2229383、rs2237896、rs2240736、rs2245019、rs2261181、rs2295786、rs2334499、rs243019、rs246600、rs247616、rs2487928、rs2535633、rs2575876、rs261967、rs273909、rs2758607、rs2782980、rs2796441、rs2815752、rs2820315、rs2861568、rs2925979、rs2972146、rs29941、rs326214、rs340874、rs351855、rs35337492、rs35444、rs36096196、rs368123、rs376563、rs3775058、rs3785100、rs3791679、rs3861086、rs3887137、rs3903239、rs3936511、rs4275659、rs4400058、rs4409766、rs4458523、rs4468572、rs4593108、rs46522、rs4719841、rs4722766、rs4724806、rs4731420、rs4752700、rs4766228、rs4788102、rs4812829、rs4821382、rs4836831、rs4846049、rs4883263、rs4911495、rs4918072、rs4932370、rs556621、rs56062135、rs574367、rs579459、rs582384、rs5996074、rs6093446、rs61776719、rs633185、rs6490029、rs6545814、rs663129, rs6666258, rs667920, rs6700559, rs671, rs67156297, rs67180937, rs6725887, rs67839313, rs6795735, rs6813195, rs6817105, rs6825454, rs6825911, rs6829822, rs6831256, rs6838973, rs6878122, rs6882076, rs6905288, rs6909752, rs69600 43, rs699, rs6997340, rs702485, rs702634, rs7136259, rs7164883, rs7178572, rs7193343, rs7199941, rs7202877, rs7206541, rs7258189, rs7258445, rs7258950, rs72689147, rs73015714, rs7304841, rs7306455, rs73069940, rs736699, rs737337, rs74035 31, rs740406, rs7499892, rs7500448, rs7503807, rs7568458, rs7610618, rs7616006, rs7696431, rs7770628, rs780094, rs7810 507, rs7859727, rs7917772, rs79223353, rs7947761, rs7955901, rs7965082, rs7980458, rs8042271, rs8108269, rs838880, rs8 40616, rs871606, rs880315, rs884366, rs885150, rs888789, rs9266359, rs9268402, rs9299, rs9319428, rs9376090, rs9473924 , rs9505118, rs9568867, rs964184, rs9687065, rs975722, rs9810888, rs9815354, rs9828933, rs984222, rs9892152, rs9970807. ,

[0010] According to a specific embodiment of the present invention, it is preferable that the individual information further includes one or more single nucleotide polymorphism sites associated with CAD, SBP, WC, or T2D. CAD-related single nucleotide polymorphism sites: rs10096633, rs10203174, rs1027087, rs1029420, rs10401969, rs10455782, rs10513801, rs1077834, rs10820405, rs10830963, rs10842992, rs10886471, rs11030104, rs11057830, rs11066280, rs11067763, rs11077501, rs11125936, rs11136341, rs11142387, rs11205760, rs1129555, rs11556924, rs11634397, rs1169288, rs11830157, rs11838267, rs11847697, rs1211166, rs12204590, rs12214416, rs12242953, rs12453914, rs12463617, rs12524865, rs12535846, rs12597579, rs12679556, rs12740374, rs12970066, rs12999907, rs130071, rs13041126, rs13078807, rs1317507, rs13266634, rs13277801, rs13306194, rs1378942, rs1467605, rs1496653, rs1514175, rs1535500, rs1555543, rs1558902, rs1575972, rs1689800, rs16933812, rs16986953, rs16990971, rs17080102, rs17150703, rs17249754, rs17381664, rs174547, rs17465637, rs17517928, rs17609940, rs17678683, rs17695224, rs17843768, rs1799945, rs1800234, rs1801282, rs181rs2820443, rs3129853, rs3130501, rs3213545, rs35332062, rs3809128, rs3827066, rs3846663 , rs391300, rs3993105, rs4148008, rs4266144, rs4377290, rs439401, rs4420638, rs4471613, rs 459193, rs4613862, rs4713766, rs4735692, rs4757391, rs4845625, rs4917014, rs4923678, rs4 99974, rs5215, rs55783344, rs56289821, rs56336142, rs590121, rs6065311, rs6494488, rs6518 21, rs660599, rs6807945, rs6808574, rs6818397, rs7087591, rs7107784, rs7116641, rs722558 1, rs72654473, rs748431, rs7525649, rs7617773, rs78169666, rs7901016, rs7989336, rs803037 9, rs8090011, rs820430, rs867186, rs896854, rs897057, rs9309245, rs93138, rs9349379, rs935 7121, rs9367716, rs9390698, rs944172, rs9470794, rs9534262, rs9552911, rs9593, rs995000; SBP-related single nucleotide polymorphism sites: rs1275988, rs7701094, rs7405452, rs751984; WC-related single nucleotide polymorphism site: rs2303790; T2D-associated single nucleotide polymorphism sites: rs10010670, rs10064156, rs1052053, rs10923931, rs11651052, rs11660468, rs1260326, rs13143871, rs1448818, rs1532085, rs16927668, rs174546, rs1 7608766, rs17843797, rs1800588, rs1832007, rs2081687, rs2123536, rs2156552, rs 2230808, rs2258287, rs2297991, rs2783963, rs2954029, rs3807989, rs3810291, rs39 18226, rs4142995, rs42039, rs4302748, rs4776970, rs4883201, rs58542926, rs6015 4123, rs6038557, rs634501, rs6871667, rs6984210, rs7185272, rs7208487, rs72136 03, rs738409, rs7528419, rs7678555, rs769449, rs76954792, rs7897379, rs7903146 , rs79548680, rs80234489, rs806215, rs9501744, rs9512699, rs9591012, rs9818870.

[0011] According to a specific embodiment of the present invention, it is more preferable that the individual information further includes one or more single nucleotide polymorphism sites associated with TC, PP, or AF. TC-associated single nucleotide polymorphism sites: rs10889353, rs11957829, rs13115759, rs1421085, rs1424233, rs1805081, rs1883025, rs2625967, rs2972143, rs3120140, rs3184504, rs34008534, rs4129767, rs4939883, rs507666, rs515135, rs6544713, rs7134594, rs7306523, rs7560163, rs7633770, rs9663362; PP-related single nucleotide polymorphism sites: rs10821415, rs11196288, rs312949, rs1333042, rs1867624, rs2292318, rs2519093, rs35419456, rs7916879; AF-associated single nucleotide polymorphism sites: rs11191416, rs1200159, rs12042319, rs2200733.

[0012] According to a specific embodiment of the present invention, the individual information further includes clinical factors, and the clinical factors preferably include the presence or absence of a family history of stroke, hypertension, diabetes, dyslipidemia, and / or obesity.

[0013] According to a specific embodiment of the present invention, a genetic risk score is obtained based on information on each single nucleotide polymorphism site according to the following calculation method.

number

[0014] According to a specific embodiment of the present invention, the effect value of each SNP in the present invention is as shown in Table 3.

[0015] According to a specific embodiment of the present invention, the higher the genetic risk score, the higher the individual's risk of developing stroke, including hemorrhagic stroke and / or ischemic stroke.

[0016] According to a specific embodiment of the present invention, the subject individuals in the present invention are from East Asian populations, particularly Chinese.

[0017] In another aspect, the present invention provides a stroke risk assessment device including a detection unit and a data analysis unit, The detection unit detects subject individual information and acquires a detection result (wherein the subject individual information is the same as the subject individual information according to any one of claims 1 to 3), The data analysis unit further provides a stroke risk assessment device that analyzes and processes the detection results from the detection unit.

[0018] According to a specific aspect of the present invention, in the present invention, the data analysis unit, when performing an analysis process on the detection result by the detection unit, assigns a weighting coefficient to the detection result of the single nucleotide polymorphism site and calculates a genetic risk score for the test individual.

[0019] Preferably, the data analysis unit comprises: a preprocessing module that normalizes the detection results of the single nucleotide polymorphism site; and The system includes a calculation module that substitutes the normalized detection results of single nucleotide polymorphism sites into the following evaluation model to obtain a genetic risk score for the test individual. Genetic risk score = Σβi × Ni Here, βi means the effect value of the i-th SNP, and Ni means the number of effect alleles of the i-th SNP that an individual has.

[0020] According to a specific embodiment of the present invention, in the present invention, the computing module may further combine the genetic risk score with clinical factors to evaluate lifetime stroke risk information.

[0021] According to a specific embodiment of the present invention, in the present invention, the data analysis unit further comprises: The apparatus further includes a matrix input module that receives the normalized detection results output from the pre-processing module and inputs the normalized detection results as a matrix to the calculation module.

[0022] Preferably, the data analysis unit further comprises: The system further includes an output module that receives the genetic risk score and / or lifetime stroke risk information output from the calculation module and outputs them as a diagnostic classification result.

[0023] In yet another aspect, the present invention provides a computer storage medium storing computer program instructions that are executed to obtain an individual's stroke risk assessment result based on subject individual information, where the individual information is as described above.

[0024] In yet another aspect, the present invention further provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the computer device obtains a result of stroke risk assessment for an individual based on subject individual information, where the individual information is as described above.

[0025] In a specific embodiment of the present invention, based on a large-scale prospective cohort from China, we identified single nucleotide polymorphism sites associated with stroke risk in East Asian populations, developed a polygenic risk score incorporating multiple genetic variants, and evaluated its effect on stroke risk stratification, either alone or in combination with traditional risk factors (hypertension, diabetes, dyslipidemia, obesity, and family history of stroke), in a large-scale prospective cohort of 41,006 study subjects. We found that individuals with high genetic risk (those in the top 20% of genetic risk) had a roughly two-fold higher risk of stroke than those with low genetic risk (those in the bottom 20% of genetic risk) (HR: 1.99, 95% CI: 1.66-2.38), with lifetime stroke risks of 25.2% (95% CI: 22.5%-27.7%) and 13.6% (95% CI: 11.6%-15.5%) in the two populations, respectively. When stratifying individuals using a genetic risk score in combination with traditional risk factors, the trajectory of stroke development differs significantly between populations. Individuals with low genetic risk and no family history of stroke had a lifetime risk of 13.2%. Individuals with either low genetic risk or a family history of stroke had a roughly two-fold increased risk (23.9%, 95% CI: 21.1%-26.5% and 23.7%, 95% CI: 13.4%-32.8%). However, individuals with both high genetic risk and a family history of stroke had the highest lifetime risk (41.1%, 95% CI: 31.4%-49.5%). Furthermore, the stroke risk assessment of the present invention can be applied to both hemorrhagic and ischemic stroke. This study demonstrated that the combination of a polygenic genetic risk score and traditional risk factors enables detailed re-stratification of stroke risk. For example, by applying this polygenic genetic risk score, it can identify 20% of the general population who have the same lifetime risk of stroke as the group with a family history of stroke at an early stage.When high genetic risk and family history of stroke are combined, the individual's risk of stroke further increases, reaching 40% or more.In clinical application, the combination of genetic risk and family history can be an important guide for early screening of stroke.Furthermore, when polygenic genetic risk scores were simultaneously integrated with traditional risk factors such as hypertension, diabetes, dyslipidemia, and obesity, significant differences in stroke trajectories were also observed among different populations. The above results reinforce that integrating polygenic genetic risk scores with traditional risk factors has important application value in achieving detailed re-stratification of stroke risk, early screening of high-risk populations, and guiding personalized interventions. The present invention has important potential applications in the primary prevention of stroke. [Brief explanation of the drawings]

[0026] [Figure 1] 1 shows the association between candidate polygenic risk scores (per 1 standard deviation increase) and stroke in the training set. [Figure 2] 1 shows the association between optimal polygenic risk scores (per 1 standard deviation increase) and stroke in the training set. [Figure 3] Correlation of metaPRS with optimal subphenotypic polygenic risk scores in a prospective cohort. [Figure 4] This shows the association between metaPRS and optimal subphenotype polygenic risk scores and stroke incidence in a prospective cohort. [Figure 5] Lifetime risk of stroke according to different genetic risks. [Figure 6] Figure 1 shows lifetime risk of stroke stratified by different genetic and family history of stroke. [Figure 7] The association between metaPRS quintiles and stroke incidence is shown. [Figure 8] Figure 1 shows lifetime risk of stroke for groups according to different genetic and clinical risk factors. [Figure 9] Figure 1 shows lifetime risk of ischemic and hemorrhagic stroke by stratification with different genetic risks. [Figure 10]Figures 9 and 10 show the lifetime risk of ischemic and hemorrhagic stroke stratified by different genetic and major risk factors. Risk ratios (HRs) and cumulative incidence curves of ischemic and hemorrhagic stroke up to age 80 were calculated using a cohort-stratified Cox proportional hazards regression model with age as the time scale and adjusted for sex. DETAILED DESCRIPTION OF THE INVENTION

[0027] In order to more clearly understand the technical features, objectives and beneficial effects of the present invention, the technical solution of the present invention will be described in detail below with reference to specific examples, but these examples are only for the purpose of illustrating the present invention and are not intended to limit the scope of the present invention. In the examples, all starting reagent materials are commercially available, and experimental methods without specific conditions are in accordance with conventional methods and conditions well known in the art or conditions recommended by instrument manufacturers. [Example]

[0028] Research design flow and research group In this study, we constructed the metaPRS using a case-control training set and validated and evaluated its clinical value for stroke risk prediction in a large prospective cohort called the "Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR)."

[0029] The training set included 2,872 stroke cases (2,548 ischemic and 324 hemorrhagic strokes) and 2,494 controls (Table 1). Strokes were derived from hospitals and diagnosed by neurologists based on computed tomography (CT) and / or magnetic resonance imaging (MRI) medical records. Controls were randomly selected from individuals who participated in a community cardiovascular risk factor survey and were confirmed to be stroke-free by medical history, clinical examination, and standard questionnaires.

[0030] The validation populations were derived from the China Multi-Center Collaborative Study of Cardiovascular Epidemiology 1998 (China MUCA 1998), the International Collaborative Study of Cardiovascular Disease in Asia (InterASIA), and the Community Intervention of Metabolic Syndrome in China & Chinese Family Health Study (CIMIC), as well as three China-PAR project cohorts. These three cohorts were updated between 2012 and 2015 using a unified questionnaire and protocol. From the 43,881 participants with blood samples and follow-up information, we further excluded 561 participants with a high genotype deletion rate (>5.0%) or a low mean sequencing depth (<30×), 1,352 participants with a baseline age <30 years or >75 years, and 962 participants with baseline cardiovascular disease (stroke and myocardial infarction), ultimately resulting in a total of 41,006 participants being included in the analysis.

[0031] All of these studies were approved by the Ethics Review Committee of Fuwai Hospital, Chinese Academy of Medical Sciences. Before data collection, each participant signed a written informed consent form. [Table 1]

[0032] Collection of baseline key traditional risk factors At baseline, each participant completed a standard questionnaire, physical examination, and laboratory tests. Professionally trained and tested investigators collected a series of lifestyle risk factors and cardiometabolic indices according to a standardized study protocol. Traditional baseline stroke risk factors included primarily hypertension, dyslipidemia, diabetes, and obesity (BMI ≥ 28 kg / m). 2 ), and a family history of stroke. Hypertension was defined as a systolic blood pressure (SBP) of ≥ 140 mmHg and / or a diastolic blood pressure (DBP) of ≥ 90 mmHg, and / or use of antihypertensive medication within the past 2 weeks. Dyslipidemia was defined as a total cholesterol (TC) of ≥ 240 mg / dl, and / or a high-density lipoprotein cholesterol (HDL-C) of < 40 mg / dl, and / or a triglyceride (TG) of ≥ 200 mg / dl, and / or a low-density lipoprotein cholesterol (LDL-C) of ≥ 160 mg / dl, and / or use of lipid-lowering medication. Diabetes was defined as a fasting blood glucose level of ≥ 126 mg / dl and / or use of insulin or oral hypoglycemic agents. A family history of stroke is defined as any first-degree relative (father, mother, or sibling) having a history of stroke.

[0033] Stroke Tracking The three cohorts were followed using the same study protocol. Stroke and mortality information for the study subjects was obtained through house-to-house surveys and house-to-house interviews. Medical histories and death certificates were also obtained for verification. All medical and death records were independently reviewed by two experts from the Endpoint Assessment Committee at Fuwai Hospital, Chinese Academy of Medical Sciences. In cases of disagreement between the two experts, a final diagnosis was reached through discussion with the other expert on the committee. Causes of death were coded according to the International Classification of Diseases (ICD-10), 10th Revision. Stroke was defined as the first fatal or nonfatal stroke diagnosed during follow-up (I60-I69). Stroke subtypes were divided into ischemic stroke (I63), hemorrhagic stroke (I60-I62), and undetermined subtype stroke (I64-I69).

[0034] Selection of single nucleotide polymorphism sites and genotyping Based on previous genome-wide association studies, the present invention selected 588 single nucleotide polymorphism (SNP) sites that showed significant genome-wide association with stroke or stroke-related phenotypes (Table 2). [Table 2]

[0035] All training and validation participants were genotyped using multiplex polymerase chain reaction-targeted amplicon sequencing technology. Target regions were amplified and high-throughput sequenced on an Illumina Hiseq X Ten sequencer. After excluding SNPs with genotype detection rates below 95%, 578 autosomal SNPs remained for further analysis. The mean genotype detection rate was 99.9%, with a median sequencing depth of 979×. To assess genotyping reproducibility, 1648 duplicate samples were measured, and the genotyping concordance rate was >99.4%.

[0036] Construction of metaPRS The number of variant alleles (0, 1, or 2) for each individual was weighted according to the effect value of the corresponding allele in the phenotype, and the weights were summed to construct 14 stroke-related subphenotype-specific PRSs (stroke, coronary heart disease, type 2 diabetes, atrial fibrillation, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, body mass index, waist circumference, total cholesterol, low-density lipoprotein cholesterol, triglycerides, and high-density lipoprotein cholesterol). For each subphenotype, different linkage disequilibrium r2 (0.2, 0.4, 0.6, 0.8) and significance thresholds (P value = 0.5, 0.05, 5 × 10) were used. -4 , 5×10 -6 ) was used to construct 16 candidate PRSs based on the summary data. The association between these candidate PRSs and stroke in the training set was evaluated using a logistic regression model, and the score with the largest odds ratio (OR) (per one standard deviation increase in the PRS) was selected as the optimal PRS (Figure 1). The SNP sites and effect values ​​used by the optimal stroke subphenotype (Stroke) PRS are shown in Table 3.

[0037] Each optimal PRS was converted to a score with a mean of 0 and a standard deviation of 1. Elastic net logistic regression (R package "glmnet") with 10-fold cross-validation was used to model the association between the 14 optimal PRSs and stroke, and further construct the metaPRS. The model with the largest area under the receiver operating characteristic curve (AUC) was selected as the final model, and adjustment coefficients for each PRS were obtained from it and used as weights. The adjusted effect values ​​for each PRS from univariate estimation (based on one PRS at a time) and elastic net logistic regression estimation are shown in Figure 2. After statistical processing steps, a total of 534 SNPs were finally included in the calculation of the metaPRS. Information and weights for all SNPs that met the criteria are listed in Table 3. [Table 3] TIFF0007748472000005.tif223149 TIFF0007748472000006.tif223149 TIFF0007748472000007.tif223149 TIFF0007748472000008.tif223149 TIFF0007748472000009.tif223149 TIFF0007748472000010.tif223149 TIFF0007748472000011.tif223149 TIFF0007748472000012.tif223149 TIFF0007748472000013.tif223149 TIFF0007748472000014.tif223149 TIFF0007748472000015.tif223149 TIFF0007748472000016.tif223149 TIFF0007748472000017.tif223149 TIFF0007748472000018.tif223149 TIFF0007748472000019.tif97149

[0038] statistical analysis Continuous variables in the baseline characteristics of the study subjects were expressed as means (standard deviations), and categorical variables were expressed as frequencies (percentages).Based on metaPRS levels, study subjects were classified into low (lowest quintile of metaPRS), intermediate (second-fourth quintile of metaPRS), and high (highest quintile of metaPRS) genetic risk groups.

[0039] A sex-adjusted, stratified Cox proportional hazards regression model with age as the time scale was used to calculate the risk ratio (HR) and 95% confidence interval (CI) for the genetic risk score, major clinical risk factors, and stroke occurrence. Gender-adjusted cumulative incidence curves were plotted using "survfit.coxph" (R package "survival") to assess the lifetime risk of stroke up to age 80 years for the study subjects stratified by different genetic and major clinical risk factors. The absolute risk reduction (ARR) was calculated from the difference in lifetime risk values ​​between the nonideal and ideal CVH groups, and a weighted least-squares regression model was used to assess the trend toward an increased ARR with genetic risk. A two-sided P value < 0.007 (P value divided by the number of multiple tests, i.e., 0.05 / 7) was considered to indicate a statistically significant difference using the Bonferroni correction for multiple testing. All analyses were performed using R software version 3.6.0 (RFoundation for Statistical Computing, Vienna, Austria) or SAS statistical software version 9.4 (SAS Institute Inc, Cary, NC).

[0040] Genetic risk grouping of the study population Table 4 shows the baseline characteristics of the 41,006 study subjects in the cohort. The mean age of the entire population was 51.9 (10.6) years, and 43.1% were male. Participants at high genetic risk (top 20% of metaPRS) had high cardiometabolic risk factors (hypertension, diabetes, dyslipidemia). During 367,750 person-years of follow-up (mean follow-up 9.0 years), 1,227 participants developed a stroke before age 80 (including 769 ischemic strokes, 355 hemorrhagic strokes, 21 combined ischemic and hemorrhagic strokes, and 124 unspecified subtypes of stroke). [Table 4]

[0041] Construction of polygenic genetic risk scores and prediction of stroke The optimal stroke subphenotype (Stroke) PRS identified a group of stroke risk-related genes for East Asian populations, including the 280 stroke-associated single nucleotide polymorphism sites shown in Table 3. By detecting these stroke-associated single nucleotide polymorphism sites and obtaining a genetic risk score for stroke risk using Σβi × Ni, the risk of stroke in East Asian populations could be successfully assessed. However, the effect value of each SNP associated with each stroke may be uniformly used, either as the effect value of the SNP shown in the subphenotype PRS column of Table 3, or as the effect value of the SNP shown in the metaPRS column of Table 3. The higher the genetic risk score, the higher the individual's risk of stroke.

[0042] There are different degrees of correlation between the 14 subphenotypic PRS (Fig. 3 ).

[0043] The method for assessing stroke risk of the present invention detects the 280 stroke-associated SNPs listed in Table 3, and then selectively detects one or more of the 159 CAD-associated SNPs, 4 SBP-associated SNPs, 1 WC-associated SNP, 55 T2D-associated SNPs, 22 TC-associated SNPs, 9 PP-associated SNPs, and 4 AF-associated SNPs listed in Table 3. Genetic risk scores can be obtained using Σβi × Ni, allowing for better assessment of stroke risk in East Asian populations. When the method for assessing stroke risk of the present invention involves detecting one or more of the CAD-, SBP-, WC-, T2D-, TC-, PP-, and AF-associated SNPs, the effect values ​​of these SNPs may be uniformly used as the effect values ​​of the SNPs listed in the subphenotype PRS column of Table 3, but it is preferable to uniformly use the effect values ​​of the SNPs listed in the metaPRS column of Table 3. The higher the genetic risk score, the higher the individual's risk of stroke.

[0044] The metaPRS, which includes 534 SNPs shown in Table 3, showed a stronger association with stroke than any other subphenotype PRS. For every 1 standard deviation increase in metaPRS, the HRs (95% CI) for total stroke, ischemic stroke, and hemorrhagic stroke were 1.28 (1.21-1.36), 1.29 (1.20-1.39), and 1.30 (1.17-1.45), respectively (Figure 4). By further adjusting for clinical risk factors, including family history of stroke (Table 5), it was demonstrated that the metaPRS of the present invention can be used to assess the risk of stroke occurrence independently of conventional clinical risk factors. [Table 5]

[0045] Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a cohort-stratified Cox proportional hazards regression model with age as the time scale, adjusted for sex, and with or without adjustment for clinical risk factors.

[0046] In the present invention, metaPRS genetic risk stratification was performed based on the metaPRS genetic risk score of the entire population (Table 6). If the metaPRS genetic risk score is <-0.140, the individual's genetic risk of developing stroke (metaPRS 0-20%) is determined to be low, and if the metaPRS genetic risk score is >0.305, the individual's genetic risk of developing stroke is determined to be high (metaPRS 80-100%). [Table 6]

[0047] After dividing the population into quintiles of metaPRS, the risk of stroke in each group showed a clear gradient (P for trend < 0.001) (Figure 5). Compared with those with low genetic risk (those in the bottom 20% of metaPRS), those with high genetic risk (those in the top 20% of metaPRS) had approximately two times higher risk of stroke (HR: 1.99, 95% CI: 1.66-2.38, P = 1.11 × 10 ‐13 ) (Figure 6). The lifetime risk of stroke (risk of stroke by age 80 years) in individuals at high genetic risk was also nearly twice as high as that in individuals at low genetic risk (25.2%, 95% CI: 22.5%-27.7% and 13.6%, 95% CI: 11.6%-15.5%, respectively).

[0048] Lifetime risk of stroke stratified by genetic risk and major risk factors When stratified by different genetic risk factors and major clinical risk factors, there were significant differences in lifetime stroke risk (Figures 7 and 8). For example, individuals with low genetic risk and no family history of stroke had a lifetime stroke risk of 13.2% (95% CI: 11.1-15.1%), whereas individuals with high genetic risk and a family history of stroke had similar lifetime stroke risks (23.9%, 95% CI: 21.1-26.5% and 23.7%, 95% CI: 13.4-32.8%). In the presence of both risk factors, the lifetime stroke risk was elevated to 41.1% (95% CI: 31.4-49.5%). Similar lifetime stroke risk gradients were observed when stratified by genetic risk and the other four clinical risk factors (hypertension, diabetes, dyslipidemia, and obesity) (Figure 8, Table 7).

[0049] The genetic risk results described above, or risk results combined with major risk factors, showed similar effects and risks for both hemorrhagic and ischemic stroke (Figures 9 and 10). [Table 7]

[0050] Example 2 Practical application example 1: The genetic risk of Ms. Li (a Chinese Han female, aged 35, with a family history of stroke) was assessed using the detection device for assessing genetic risk of stroke of the present invention, and guidance advice was provided in combination with traditional risk factors. The main steps were as follows: fasting blood was collected, DNA was isolated from the subject's anticoagulated blood, and the genotype of 534 loci was detected using an Illumina Hiseq X Ten sequencer. The genotypes of the 534 sites detected by Mr. Lee are shown in Table 8. [Table 8] TIFF0007748472000025.tif207149 TIFF0007748472000026.tif202149 TIFF0007748472000027.tif37149

[0051] Analysis of detection results: The detection results for 534 SNPs were compared with Table 3 to determine the genetic contribution of the effect allele corresponding to each site, and a weighted sum was added to obtain a genetic risk score. Genetic risk score = Σβi × Ni (where βi is the effect value of the i-th SNP, and Ni is the number of effect alleles of the i-th SNP possessed by the individual).

[0052] Mr. Li's genetic risk assessment for stroke: Mr. Li's genetic risk score for stroke was 0.660, which, according to Table 6, places him in the high genetic risk group. Combined with his family history of stroke, according to Table 7, his lifetime risk of stroke was 41.1%, placing him in the high-risk group. Combining genetic and clinical factors, it was predicted that Mr. Li was at high risk for stroke, and the doctor recommended that he manage his healthy lifestyle, pay attention to controlling his blood pressure, blood sugar, lipids, and weight, and undergo regular health checks, with any abnormalities discovered and prompt consultation with a doctor.

[0053] Application transformation: If the subject in Application Example 1 also had hypertension, then according to Table 7, their lifetime risk of stroke was 33.2%, placing them in a high-risk group. It was suggested that a focus on blood pressure intervention, combined with healthy lifestyle management, be implemented to reduce the risk of stroke.

[0054] If the subject in Application Example 1 also had diabetes, then according to Table 7, their lifetime risk of stroke was 42.5%, placing them in a high-risk group. It was suggested that a focus on blood glucose intervention, combined with healthy lifestyle management, be implemented to reduce the risk of stroke.

[0055] If the subject in Application Example 1 also had dyslipidemia, then according to Table 7, their lifetime risk of stroke was 30.9%, placing them in a high-risk group. It was suggested that a focus on lipid management, combined with healthy lifestyle management, could be used to reduce the risk of stroke.

[0056] If the subject in Application Example 1 also has obesity, then according to Table 7, their lifetime risk of stroke is 35.5%, placing them in a high-risk group. It is suggested that interventions to reduce the risk of stroke include increasing physical activity, improving dietary nutritional balance, and reducing high-fat, high-calorie foods, focusing on weight management.

[0057] For the subject of Application Example 1 above, a genetic risk score for stroke risk can be obtained by Σβi×Ni based on the detection results of the 280 stroke-related SNPs in Table 8, or further in combination with the detection results of 159 CAD-related SNPs, 4 SBP-related SNPs, 1 WC-related SNP, and / or 55 T2D-related SNPs shown in Table 8, or further in combination with the detection results of 22 TC-related SNPs, 9 PP-related SNPs, and 4 AF-related SNPs shown in Table 8, to assess the individual's risk of stroke.

Claims

1. Use of a reagent for detecting individual information in the manufacture of a detection device for assessing stroke risk, The individual information includes the number of effective alleles at the following single nucleotide polymorphism sites possessed by the individual: Stroke-related single nucleotide polymorphism sites: rs10051787, rs10093110, rs10139550, rs10160804, rs10237377, rs10260816, rs10267593, rs10278336, rs1037814, rs10507248, rs10512861, rs10745332, rs10757274, rs10773003, rs10824026, rs10857147, rs10953541, rs10968576, rs11099493, rs1116357, rs11206510, rs11222084, rs11257655, rs11509880, rs1152591, rs11557092, rs11601507, rs11604680, rs11624704, rs11677932, rs1173766, rs117601636, rs117711462, rs11787792, rs11810571, rs11838776, rs11869286, rs12027135, rs12037987, rs12202017, rs12229654, rs12415501, rs12438008, rs12445022, rs12500824, rs1250229, rs12549902, rs12571751, rs12581963, rs12692735, rs12718465, rs12801636, rs12897, rs12927205, rs12932445, rs12936587, rs12946454, rs13143308, rs13209747, rs1321309, rs1321rs17477177、rs17514846、rs17581137、rs17612742、rs17680741、rs17791513、rs180327、rs181359、rs1861411、rs1868673、rs1870634、rs1887320、rs1892094、rs1902859、rs191835914、rs1976041、rs1982963、rs2000813、rs2028299、rs2057291、rs2068888、rs2074158、rs2075291、rs2075423、rs2107595、rs2128739、rs2145598、rs216172、rs2213732、rs2229383、rs2237896、rs2240736、rs2245019、rs2261181、rs2295786、rs2334499、rs243019、rs246600、rs247616、rs2487928、rs2535633、rs2575876、rs261967、rs273909、rs2758607、rs2782980、rs2796441、rs2815752、rs2820315、rs2861568、rs2925979、rs2972146、rs29941、rs326214、rs340874、rs351855、rs35337492、rs35444、rs36096196、rs368123、rs376563、rs3775058、rs3785100、rs3791679、rs3861086、rs3887137、rs3903239、rs3936511、rs4275659、rs4400058、rs4409766、rs4458523、rs4468572、rs4593108、rs46522、rs4719841、rs4722766、rs4724806、rs4731420、rs4752700、rs4766228、rs4788102、rs4812829、rs4821382、rs4836831、rs4846049、rs4883263、rs4911495、rs4918072、rs4932370、rs556621、rs56062135、rs574367、rs579459、rs582384、rs5996074、rs6093446、rs61776719、rs633185、rs6490029、rs6545814、<h2 style=";text-align:left;direction:ltr">rs663129、rs6666258、rs667920、rs6700559、rs671、rs6715629 7、rs67180937、rs6725887、rs67839313、rs6795735、rs6813195、 rs6817105、rs6825454、rs6825911、rs6829822、rs6831256、rs6 838973、rs6878122、rs6882076、rs6905288、rs6909752、rs69600 43、rs699、rs6997340、rs702485、rs702634、rs7136259、rs7164883、rs7178572、rs7193343、rs7199941、rs7202877、rs7206541、rs7258189、rs7258445、rs7258950、rs72689147、rs73015714、rs7304841、rs7306455、rs73069940、rs736699、rs737337、rs74035 31、rs740406、rs7499892、rs7500448、rs7503807、rs7568458、rs7610618、rs7616006、rs7696431、rs7770628、rs780094、rs7810507、rs7859727、rs7917772、rs79223353、rs7947761、rs7955901、rs7965082、rs7980458、rs8042271、rs8108269、rs838880、rs84 0616、rs871606、rs880315、rs884366、rs885150、rs888789、rs9266359、rs9268402、rs9299、rs9319428、rs9376090、rs9473924、rs9505118、rs9568867、rs964184、rs9687065、rs975722、rs9810888、rs9815354、rs9828933、rs984222、rs9892152、rs9970807;、 CAD-related single nucleotide polymorphism sites: rs10096633, rs10203174, rs1027087, rs1029420, rs10401969, rs10455782, rs10513801, rs1077834, rs10820405, rs10830963, rs10842992, rs10886471, rs11030104, rs11057830, rs11066280, rs11067763, rs11077501, rs11125936, rs11136341, rs11142387, rs11205760, rs1129555, rs11556924, rs11634397, rs1169288, rs11830157, rs11838267, rs11847697, rs1211166, rs12204590, rs12214416, rs12242953, rs12453914, rs12463617, rs12524865, rs12535846, rs12597579, rs12679556, rs12740374, rs12970066, rs12999907, rs130071, rs13041126, rs13078807, rs1317507, rs13266634, rs13277801, rs13306194, rs1378942, rs1467605, rs1496653, rs1514175, rs1535500, rs1555543, rs1558902, rs1575972, rs1689800, rs16933812, rs16986953, rs16990971, rs17080102, rs17150703, rs17249754, rs17381664, rs174547, rs17465637, rs17517928, rs17609940, rs17678683, rs17695224, rs17843768, rs1799945, rs1800234, rs1801282, rs181360, rs2000999, rs200990725, rs2021783, rs2043085, rs2066714, rs2075260, rs2106261, rs2144300, rs2237892, rs2296172, rs230259�, rs2328223, rs2383208, rs2415317, rs2531995, rs2571445, rs2642442, rs2819348,<h2 style=";text-align:left;direction:ltr">rs2820443、rs3129853、rs3130501、rs3213545、rs35332062、rs3809128、rs3827066、rs3846663 rs391300、rs3993105、rs4148008、rs4266144、rs4377290、rs439401、rs4420638、rs4471613、rs 459193、rs4613862、rs4713766、rs4735692、rs4757391、rs4845625、rs4917014、rs4923678、rs4 99974、rs5215、rs55783344、rs56289821、rs56336142、rs590121、rs6065311、rs6494488、rs6518 21、rs660599、rs6807945、rs6808574、rs6818397、rs7087591、rs7107784、rs7116641、rs7225581、rs72654473、rs748431、rs7525649、rs7617773、rs78169666、rs7901016、rs7989336、rs803037 9、rs8090011、rs820430、rs867186、rs896854、rs897057、rs9309245、rs93138、rs9349379、rs9357121、rs9367716、rs9390698、rs944172、rs9470794、rs9534262、rs9552911、rs9593、rs995000;、 SBP-related single nucleotide polymorphism sites: rs1275988, rs7701094, rs7405452, rs751984; WC-associated single nucleotide polymorphism site: rs2303790; T2D-related single nucleotide polymorphism sites: rs10010670, rs10064156, rs1052053, rs10923931, rs11651052, rs11660468, rs1260326, rs13143871, rs1448818, rs1532085, rs16927668, rs174546, rs1 7608766, rs17843797, rs1800588, rs1832007, rs2081687, rs2123536, rs2156552, rs 2230808, rs2258287, rs2297991, rs2783963, rs2954029, rs3807989, rs3810291, rs39 18226, rs4142995, rs42039, rs4302748, rs4776970, rs4883201, rs58542926, rs6015 4123, rs6038557, rs634501, rs6871667, rs6984210, rs7185272, rs7208487, rs72136 03, rs738409, rs7528419, rs7678555, rs769449, rs76954792, rs7897379, rs7903146 , rs79548680, rs80234489, rs806215, rs9501744, rs9512699, rs9591012, rs9818870; TC-related single nucleotide polymorphism sites: rs10889353, rs11957829, rs13115759, rs1421085, rs1424233, rs1805081, rs1883025, rs2625967, rs2972143, rs3120140, rs3184504, rs34008534, rs4129767, rs4939883, rs507666, rs515135, rs6544713, rs7134594, rs7306523, rs7560163, rs7633770, rs9663362; PP-related single nucleotide polymorphism sites: rs10821415, rs11196288, rs312949, rs1333042, rs1867624, rs2292318, rs2519093, rs35419456, rs7916879; and AF-related single nucleotide polymorphism sites: rs11191416, rs1200159, rs12042319, rs2200733, A genetic risk score is obtained based on the information of each single nucleotide polymorphism (SNP) site according to the following calculation method: [Equation 1] (where βi means the effect value of the i-th SNP, and Ni means the number of effect alleles of the i-th SNP that an individual has) The effective alleles and effect values ​​for each SNP are as shown in the table below: 【number】 【number】 【number】 【number】 【number】 【number】 【number】 【number】 【number】 【number】 【number】 【number】 The higher the genetic risk score, the higher the individual's risk of developing a stroke.

2. The use described in claim 1, wherein the individual is from an East Asian population.

3. A stroke risk assessment device including a detection unit and a data analysis unit, the detection unit detects subject individual information and obtains a detection result, the subject individual information being the individual information defined in claim 1; the data analysis unit analyzes and processes the detection result by the detection unit; The data analysis unit performing the analysis process on the detection result by the detection unit includes assigning a weighting coefficient to the detection result of the single nucleotide polymorphism (SNP) site to calculate a genetic risk score for the test individual; The data analysis unit a preprocessing module that normalizes the detection results of the single nucleotide polymorphism site; and a calculation module for substituting the normalized detection result of the single nucleotide polymorphism site into the following evaluation model to obtain a genetic risk score for the test individual, [Equation 2] (where βi means the effect value of the i-th SNP, and Ni means the number of effect alleles of the i-th SNP that an individual has) The effective alleles and effect values ​​of each SNP are the same as those defined in claim 1; A stroke risk assessment device in which the higher the genetic risk score, the higher the risk of stroke in an individual.

4. A stroke risk assessment device as described in claim 3, wherein the stroke includes hemorrhagic stroke or ischemic stroke.

5. 5. The stroke risk assessment device of claim 3 or 4, wherein the calculation module further combines the genetic risk score with clinical factors to assess lifetime stroke risk information, and the clinical factors include the presence or absence of a family history of stroke, hypertension, diabetes, dyslipidemia, or obesity.

6. The data analysis unit further comprises: a matrix input module that receives the normalized detection results output from the pre-processing module and inputs the normalized detection results as a matrix to the calculation module; The data analysis unit further comprises: an output module that receives the genetic risk score and / or lifetime stroke risk information output from the calculation module and outputs them as a diagnostic classification result; The stroke risk assessment device according to any one of claims 3 to 5.

7. A computer storage medium storing computer program instructions that are executed to obtain an individual's stroke risk assessment result based on subject individual information, The individual information is the individual information defined in claim 1, A genetic risk score is obtained based on the information of each single nucleotide polymorphism (SNP) site according to the following calculation method: [Equation 3] (where βi means the effect value of the i-th SNP, and Ni means the number of effect alleles of the i-th SNP that an individual has) The effective alleles and effect values ​​of each SNP are the same as those defined in claim 1; The higher the genetic risk score, the higher the individual's risk of developing a stroke.

8. 1. A computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, When the processor executes the computer program, a result of stroke risk assessment for the individual is obtained based on the subject individual information; The individual information is the individual information defined in claim 1, A genetic risk score is obtained based on the information of each single nucleotide polymorphism (SNP) site according to the following calculation method: [Equation 4] (where βi means the effect value of the i-th SNP, and Ni means the number of effect alleles of the i-th SNP that an individual has) The effective alleles and effect values ​​of each SNP are the same as those defined in claim 1; The higher the genetic risk score, the higher the individual's risk of developing a stroke, the computer device.

Images