Abstract P408: A Meta-Genetic Risk Score Identifies Individuals at High Risk for Intracerebral Hemorrhage

Introduction: Genome-wide association studies (GWAS) have identified genetic associations for many common diseases and traits. However, incorporating genetic information into disease risk prediction has been challenging because any single collection of variants explains a small proportion of risk. We explored whether combining genetic information from related traits could improve our ability to predict ICH. Methods: We constructed an ICH meta-Genetic Risk Score (metaGRS) using 21 polygenic risk scores (PRSs) from GWASs of traits associated with ICH risk: systolic/diastolic blood pressure, pulse pressure, diabetes, hemoglobin A1c, total cholesterol, high/low-density lipoprotein, triglycerides, body mass index, waist-hip ratio, urine albumin-creatinine ratio, kidney disease, eGFR, white matter hyperintensities, small vessel stroke, insomnia, sleep duration, education attainment, alcohol use and smoking. Each PRS contained common, independent variants at p≤5x10 -4 with each trait. PRSs were calculated in 1,867 ICH cases/1,722 controls, using 1,019 cases/928 controls as a training dataset to derive logORs of the PRSs with ICH status, and a validation dataset of 848 cases/794 controls to construct the metaGRS as a weighted average. Results: Patients in higher metaGRS percentiles had higher odds of ICH (Table) , and a one standard deviation increase in metaGRS was associated with odds of ICH (OR 1.42; 95% CI: 1.28-1.57; p=1.6x10 -11 ). Compared to patients in the middle decile of the metaGRS distribution, patients in the top 10% and 5% had increased odds of ICH (OR 1.87; 95% CI: 1.07-3.30; p=0.03, OR 2.72; 95% CI: 1.54-4.92; p=7.4x10 -4 respectively). Conclusions: A metaGRS identified individuals at high risk for ICH with an odds ratio comparable to traditional risk factors, such as hypertension (OR ~1.6). Further studies are needed to investigate the role of incorporating genetic information into clinical care.

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Abstract 175: Performance of a Multilocus Genetic Risk Score Derived from Top Signals in the Cardiogram+C4d Consortium in Predicting Incident Coronary Disease in the Atherosclerosis Risk in Communities Study

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvb.32.suppl_1.a175 ◽

2012 ◽

Vol 32 (suppl_1) ◽

Author(s):

Themistocles L Assimes ◽

Benjamin Goldstein ◽

Keyword(s):

Risk Prediction ◽

Risk Score ◽

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genotype Information ◽

Atherosclerosis Risk

Genome wide association studies (GWAS) to date have identified 30 CAD susceptibility loci but the ability to use this information to improve risk prediction remains limited. A meta-analysis of the GWAS and Cardio Metabochip data produced by the CARDIoGRAM+C4D consortium representing 63,253 cases and 126,820 controls has identified 1885 SNPs passing a False Discovery Rate (FDR) threshold of 0.5%. We hypothesized that an expanded multi locus genetic risk score (GRS) incorporating genotype information at all loci below an FDR of 0.5% would perform better than a GRS restricted to 42 loci reaching genome wide significance and tested this hypothesis in subjects of European ancestry participating in the Atherosclerosis Risk in the Community (ARIC) study. Models testing the GRS were either minimally (age and sex) or fully adjusted for traditional risk factors (TRFs). The Figure shows the hazard ratio (HZ) and 95% CI for incident events comparing each quintile of GRS to the middle quintile. The GRS including genotype information at all loci with an FDR of 0.5% noticeably improves risk prediction over the GRS restricted to genome wide significant loci in both the minimally and fully adjusted models based on several metrics including i) HR per GRS quintile, ii) the HR per SD of the GRS, and iii) the logistic regression pseudo R2, and iv) the c statistic. The HR per GRS quintile and per SD of GRS were all lower in the fully adjusted models compared to the respective minimally adjusted models but the reduction of the HR was more striking for the models that tested the more expansive GRS. These findings suggest that a larger proportion of novel GWAS CAD loci are mediating their effects through TRFs. While these findings demonstrate some progress in risk prediction using GWAS loci, both the limited and the expanded GRS continues to explain a relatively small proportion of the overall variance compared to TRF. Thus, the clinical utility of a CAD GRS remains to be determined.

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Abstract P258: Traditional Risk Factors and a Genetic Risk Score Are Associated with Age of First Acute Coronary Syndrome

Circulation ◽

10.1161/circ.129.suppl_1.p258 ◽

2014 ◽

Vol 129 (suppl_1) ◽

Author(s):

Christopher Labos ◽

Leo Rui Wang ◽

Louise Pilote ◽

Peter Bogaty ◽

James M Brophy ◽

...

Keyword(s):

Risk Factors ◽

Myocardial Infarction ◽

Acute Coronary Syndrome ◽

Risk Score ◽

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

Genome Wide Association Studies ◽

Coronary Syndrome ◽

Traditional Risk Factors

Background: Early onset myocardial infarction (MI) is frequently attributed to genetic factors that may accelerate the atherosclerotic process. However, early MI may also occur due to a high burden of traditional risk factors. We sought to examine the association between traditional risk factors as well as a genetic risk score on the age of a first acute coronary syndrome (ACS). Methods and Results: We included 460 participants (mean age 59 +/- 12 years, 22.4% female) with a first ACS enrolled in the Recurrence and Inflammation in the Acute Coronary Syndromes (RISCA) cohort. Participants were genotyped for 30 single nucleotide polymorphisms identified from prior myocardial infarction genome-wide association studies to construct a multilocus genetic risk score (GRS). Linear regression models were fit to estimate the association between traditional risk factors (TRFs) and the GRS with age of first ACS. Several TRFs were significantly associated with earlier age of first ACS (all β coefficients in years; p<0.05 for all) : male sex [β=-6.9 (95%CI -9.7,-4.1)], current cigarette smoking [β=-8.1 (95% confidence interval [CI] -10.0, -6.1)], overweight (BMI>25) [β=-2.6 (95%CI -4.8, -0.3)] and obesity (BMI>30) [β=-5.24 (95%CI -7.9, -2.6)]. Use of hormone replacement therapy [β=-4.3 (95%CI -8.4, -0.3) ] and aspirin use were also associated with age of first ACS [β=3.7 (95%CI 0.3, 7.0)]. After multivariable adjustment for TRFs, a one standard deviation increment in the GRS was associated with a 1.0 (95%CI 0.1-2.0) year earlier age of first ACS. Conclusion: Among individuals with a first ACS, a GRS composed of 30 SNPs is associated with a younger age of presentation. Although common genetic predisposition modestly contributes to earlier ACS, a heavy burden of traditional risk factors is strongly associated with markedly earlier ACS.

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Personalized Risk Prediction for Type 2 Diabetes: the Potential of Genetic Risk Scores

10.1101/041731 ◽

2016 ◽

Cited By ~ 1

Author(s):

Kristi Lall ◽

Reedik Magi ◽

Andrew Morris ◽

Andres Metspalu ◽

Krista Fischer

Keyword(s):

Type 2 Diabetes ◽

Risk Prediction ◽

Risk Score ◽

Cardiovascular Mortality ◽

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

Genome Wide Association Studies ◽

Net Reclassification Improvement

Purpose: The study aims to develop a Genetic Risk Score (GRS) for the prediction of Type 2 Diabetes (T2D) that could be used for risk assessment in general population. Methods: Using the results of genome-wide association studies, we develop a doubly-weighted GRS for the prediction of T2D risk, aiming to capture the effect of 1000 single nucleotide polymorphisms. The GRS is evaluated in the Estonian Biobank cohort (n=10273), analysing its effect on prevalent and incident T2D, while adjusting for other predictors. We assessed the effect of GRS on all-cause and cardiovascular mortality and its association with other T2D risk factors, and conducted the reclassification analysis. Results: The adjusted hazard for incident T2D is 1.90 (95% CI 1.48, 2.44) times higher and for cardiovascular mortality 1.27 (95% CI 1.10, 1.46) times higher in the highest GRS quintile compared to the rest of the cohort. No significant association between BMI and GRS is found in T2D-free individuals. Adding GRS to the prediction model for 5-year T2D risks results in continuous Net Reclassification Improvement of 0.26 (95% CI 0.15, 0.38). Conclusion: The proposed GRS would considerably improve the accuracy of T2D risk prediction when added to the set of predictors used so far. Keywords: genetic risk score, Type 2 Diabetes, risk prediction, genetic risk, precision medicine

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Pharmacogenetic Polygenic Risk Score for Bronchodilator Response in Children and Adolescents with Asthma: Proof-of-Concept

Journal of Personalized Medicine ◽

10.3390/jpm11040319 ◽

2021 ◽

Vol 11 (4) ◽

pp. 319

Author(s):

Joanne E. Sordillo ◽

Sharon M. Lutz ◽

Michael J. McGeachie ◽

Jessica Lasky-Su ◽

Scott T. Weiss ◽

...

Keyword(s):

Standard Deviation ◽

Risk Score ◽

Association Studies ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Linear Regression Models ◽

Standard Deviation Increase ◽

Polygenic Risk ◽

Bronchodilator Response ◽

Children With Asthma

Genome-wide association studies (GWAS) of response to asthma medications have primarily focused on Caucasian populations, with findings that may not be generalizable to minority populations. We derived a polygenic risk score (PRS) for response to albuterol as measured by bronchodilator response (BDR), and examined the PRS in a cohort of Hispanic school-aged children with asthma. We leveraged a published GWAS of BDR to identify relevant genetic variants, and ranked the top variants according to their Combined Annotation Dependent Depletion (CADD) scores. Variants with CADD scores greater than 10 were used to compute the PRS. Once we derived the PRS, we determined the association of the PRS with BDR in a cohort of Hispanic children with asthma (the Genetics of Asthma in Costa Rica Study (GACRS)) in adjusted linear regression models. Mean BDR in GACRS participants was5.6% with a standard deviation of 10.2%. We observed a 0.63% decrease in BDR in response to albuterol for a standard deviation increase in the PRS (p = 0.05). We also observed decreased odds of a BDR response at or above the 12% threshold for a one standard deviation increase in the PRS (OR = 0.80 (95% CI 0.67 to 0.95)). Our findings show that combining variants from a pharmacogenetic GWAS into a PRS may be useful for predicting medication response in asthma.

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Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

Nature Communications ◽

10.1038/s41467-021-24082-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ganna Leonenko ◽

Emily Baker ◽

Joshua Stevenson-Hoare ◽

Annerieke Sierksma ◽

Mark Fiers ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Prediction Accuracy ◽

Genetic Risk Score ◽

Low Risk ◽

Risk Scores ◽

Sample Mean ◽

Polygenic Risk ◽

Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

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A Weighted Genetic Risk Score Predicts Surgical Recurrence Independent of High-Risk Clinical Features in Dupuytren’s Disease

Plastic & Reconstructive Surgery ◽

10.1097/prs.0000000000005208 ◽

2019 ◽

Vol 143 (2) ◽

pp. 512-518 ◽

Cited By ~ 3

Author(s):

Sophie A. Riesmeijer ◽

Oliver W. G. Manley ◽

Michael Ng ◽

Ilja M. Nolte ◽

Dieuwke C. Broekstra ◽

...

Keyword(s):

High Risk ◽

Clinical Features ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Dupuytren’S Disease ◽

Dupuytren's Disease ◽

Surgical Recurrence ◽

Weighted Genetic Risk Score

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Coronary artery disease, genetic risk and the metabolome in young individuals

Wellcome Open Research ◽

10.12688/wellcomeopenres.14788.1 ◽

2018 ◽

Vol 3 ◽

pp. 114 ◽

Cited By ~ 5

Author(s):

Thomas Battram ◽

Luke Hoskins ◽

David A. Hughes ◽

Johannes Kettunen ◽

Susan M. Ring ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Genetic Variants ◽

Genetic Risk ◽

Association Studies ◽

Genetic Risk Score ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Genome Wide Association Studies ◽

Artery Disease

Background: Genome-wide association studies have identified genetic variants associated with coronary artery disease (CAD) in adults – the leading cause of death worldwide. It often occurs later in life, but variants may impact CAD-relevant phenotypes early and throughout the life-course. Cohorts with longitudinal and genetic data on thousands of individuals are letting us explore the antecedents of this adult disease. Methods: 149 metabolites, with a focus on the lipidome, measured using nuclear magnetic resonance (1H-NMR) spectroscopy, and genotype data were available from 5,905 individuals at ages 7, 15, and 17 years from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Linear regression was used to assess the association between the metabolites and an adult-derived genetic risk score (GRS) of CAD comprising 146 variants. Individual variant-metabolite associations were also examined. Results: The CAD-GRS associated with 118 of 149 metabolites (false discovery rate [FDR] < 0.05), the strongest associations being with low-density lipoprotein (LDL) and atherogenic non-LDL subgroups. Nine of 146 variants in the GRS associated with one or more metabolites (FDR < 0.05). Seven of these are within lipid loci: rs11591147 PCSK9, rs12149545 HERPUD1-CETP, rs17091891 LPL, rs515135 APOB, rs602633 CELSR2-PSRC1, rs651821 APOA5, rs7412 APOE-APOC1. All associated with metabolites in the LDL or atherogenic non-LDL subgroups or both including aggregate cholesterol measures. The other two variants identified were rs112635299 SERPINA1 and rs2519093 ABO. Conclusions: Genetic variants that influence CAD risk in adults are associated with large perturbations in metabolite levels in individuals as young as seven. The variants identified are mostly within lipid-related loci and the metabolites they associated with are primarily linked to lipoproteins. This knowledge could allow for preventative measures, such as increased monitoring of at-risk individuals and perhaps treatment earlier in life, to be taken years before any symptoms of the disease arise.

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Abstract 010: Favorable Associations of a Healthy Diet with Fasting Glucose or Insulin are Not Modified by Fasting Glucose- or Insulin-Associated Genotypes in 51,289 Non-Diabetic Individuals from 15 Cohorts

Circulation ◽

10.1161/circ.125.suppl_10.a010 ◽

2012 ◽

Vol 125 (suppl_10) ◽

Author(s):

Jennifer A Nettleton ◽

Marie-France Hivert ◽

Nicola M McKeown ◽

Dariush Mozaffarian ◽

Toshiko Tanaka ◽

...

Keyword(s):

Effect Size ◽

Genetic Risk ◽

Genetic Model ◽

Fasting Glucose ◽

Association Studies ◽

Genetic Risk Score ◽

Healthy Diet ◽

Genome Wide Association Studies ◽

Food Groups ◽

Diet Score

Background: Genome-wide association studies (GWAS) have identified several genetic loci that influence fasting glucose (FG) and insulin (FI). Whether the favorable relation between eating a healthy diet and FG or FI is the same regardless of genetic risk is unknown. Objective: We studied 15 well-characterized U.S., Northern European and Mediterranean cohorts that had dietary and genetic data (maximum N = 51,289) to test whether genotype and healthy diet interact to influence FG or FI concentrations. Design: Within each cohort, we constructed a healthy diet score comprising foods with previous evidence of associations with metabolic risk: whole grains, fish, fruits, vegetables, nuts/seeds (favorable food groups) and red meat, sweets, sugared beverages, fried potatoes (unfavorable food groups). Intakes of each food group were categorized in quartiles and assigned ascending values (0, 1, 2, 3) for favorable foods and descending values (3, 2, 1, 0) for unfavorable foods. These values were summed to generate an overall diet score (range: 0 to 27 points), with higher scores representing healthier diets. We used multivariable linear regression including an additive genetic model within cohorts followed by inverse weighted meta-analysis of all cohorts to quantify 1) associations between healthy diet and FG and FI and 2) interactions of healthy diet with 16 established FG- or two FI-associated loci on FG and FI concentrations. Results: Healthier diets (per additional diet score unit) were associated with lower FG (β: -0.004; 95% CI: -0.005, -0.003 mmol/L, p: <0.001) and lower ln(FI) (β: -0.008; 95% CI: -0.009, -0.007 pmol/L, p: <0.001) with adjustment for demographic, lifestyle and physiological factors including body mass index. The relations between healthy diet and FG and FI were the same regardless of genotype for any individual SNP (interaction p: 0.22 - 0.99) or the sum of risk alleles across the 16 FG-related SNPs (unweighted genetic risk score, p: 0.71). We estimated that modest differences in diet score could offset the small genetic risk associated with per risk allele increases in common variants associated with FG. For example, the mean effect size across all 16 FG-raising alleles was ∼0.03 mmol/L greater FG per FG-raising allele, which compares in magnitude to the effect size of an approximate 1.5-SD increase in diet score, i.e., towards a healthier diet: ∼7 score units x diet score β -0.004 = -0.028 mmol/L lower FG). Conclusions: A healthy diet score allowing summarization of dietary intake as an environmental exposure across diverse cohorts is favorably associated with FG and FI concentrations regardless of genotype at FG or FI-associated loci. Modest dietary differences are far larger than an individual’s apparent genetic risk at these loci.

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Abstract 03: LDL-C Genetic Risk Score Predicts Hyperlipidemia and Modifies Lipid Trajectory Over the Life Course: The Bogalusa Heart Study

Circulation ◽

10.1161/circ.141.suppl_1.03 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Cited By ~ 1

Author(s):

Alexander C Razavi ◽

Mengyao Shi ◽

Lydia A Bazzano ◽

Jiang He ◽

Jovia L Nierenberg ◽

...

Keyword(s):

Life Course ◽

Risk Score ◽

Genetic Risk ◽

Genetic Information ◽

Regression Models ◽

Genetic Risk Score ◽

African Ancestry ◽

Bogalusa Heart Study ◽

Heart Study ◽

The Life Course

Introduction: Variability in lipid levels is decreased in childhood compared to adulthood. However, few studies have assessed whether genetic information may help to predict hyperlipidemia beyond measured lipid values at this critical developmental stage. Hypothesis: We hypothesized that a low-density lipoprotein cholesterol (LDL-C) genetic risk score (GRS) would predict hyperlipidemia and LDL-C over the life course, even after adjustment for childhood measures. Methods: The analysis included 651 Bogalusa Heart Study participants (63% women, 31% African American, baseline age=9.8 + 3.1 years, median follow-up=40 years) with genome-wide association study (GWAS) data and at least one childhood and one adulthood measure of LDL-C. A weighted LDL-C GRS was constructed using loci from previous GWAS meta-analyses. Hyperlipidemia was defined as an LDL-C > 130 mg/dL or statin use. Cox proportional hazards regression models examined the associations between GRS and hyperlipidemia. Linear and mixed linear regression models were employed to examine the associations of GRS with adulthood LDL-C and life-course LDL-C trajectory, respectively. All models adjusted for age, sex, and childhood LDL-C. Results: Among participants of European ancestry, increasing GRS tertile conferred strong, dose-response increases in the hazards of hyperlipidemia ( Figure ). Similarly, the highest GRS tertile was associated with a 20 mg/dl increased LDL-C level in middle-aged adults compared to the lowest tertile (P<0.0001). Each tertile increase in GRS was also associated with 5.5 mg/dL larger 10-year increase in LDL-C (interaction-P=0.04). No associations were observed among participants of African ancestry. Conclusions: A LDL-C GRS was associated with adulthood LDL-C phenotypes independently of childhood LDL-C values in participants of European but not African ancestry. These findings highlight a need for increased genomics research in diverse populations and suggest a predictive utility of genetic information in childhood.

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The Association Between Genetic Risk Factors and the Size of Intracranial Aneurysms at Time of Rupture

Neurosurgery ◽

10.1227/neu.0000000000000078 ◽

2013 ◽

Vol 73 (4) ◽

pp. 705-708 ◽

Cited By ~ 5

Author(s):

Rachel Kleinloog ◽

Femke N.G. van 't Hof ◽

Franciscus J. Wolters ◽

Ingeborg Rasing ◽

Irene C. van der Schaaf ◽

...

Keyword(s):

Risk Factors ◽

Genetic Risk ◽

Intracranial Aneurysms ◽

Aneurysmal Subarachnoid Hemorrhage ◽

Association Studies ◽

Genetic Risk Score ◽

Genetic Risk Factors ◽

Genome Wide Association Studies ◽

Aneurysm Size ◽

Genome Wide

Abstract BACKGROUND: Genetic risk factors for intracranial aneurysms may influence the size of aneurysms. OBJECTIVE: To assess the association between genetic risk factors and the size of aneurysms at the time of rupture. METHODS: Genotypes of 7 independent single-nucleotide polymorphisms (SNPs) of the 6 genetic risk loci identified in genome-wide association studies of patients with intracranial aneurysms were obtained from 700 Dutch patients with an aneurysmal subarachnoid hemorrhage (1997-2007) previously genotyped in the genome-wide association studies; 255 additional Dutch patients with an aneurysmal subarachnoid hemorrhage (2007-2011) were genotyped for these SNPs. Aneurysms were measured on computerized tomography angiography or digital subtraction angiography. The mean aneurysm size (with standard error) was compared between patients with and without a genetic risk factor by the use of linear regression. The association between SNPs and size was assessed for single SNPs and for the combined effect of SNPs by using a weighted genetic risk score. RESULTS: Single SNPs showed no association with aneurysm size, nor did the genetic risk score. CONCLUSION: The 6 genetic risk loci have no major influence on the size of aneurysms at the time of rupture. Because these risk loci explain no more than 5% of the genetic risk, other genetic factors for intracranial aneurysms may influence aneurysm size and thereby proneness to rupture.

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