Genetic variants primarily associated with type 2 diabetes are related to coronary artery disease risk (original) (raw)
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Additional value of a combined genetic risk score to standard cardiovascular stratification
Genetics and Molecular Biology
The utility of genetic risk scores (GRS) as independent risk predictors remains inconclusive. Here, we evaluate the additive value of a multi-locus GRS to the Framingham risk score (FRS) in coronary artery disease (CAD) risk prediction. A total of 2888 individuals (1566 coronary patients and 1322 controls) were divided into three subgroups according to FRS. Multiplicative GRS was determined for 32 genetic variants associated to CAD. Logistic Regression and Area Under the Curve (AUC) were determined first, using the TRF for each FRS subgroup, and secondly, adding GRS. Different models (TRF, TRF+GRS) were used to classify the subjects into risk categories for the FRS 10-year predicted risk. The improvement offered by GRS was expressed as Net Reclassification Index and Integrated Discrimination Improvement. Multivariate analysis showed that GRS was an independent predictor for CAD (OR = 1.87; p<0.0001). Diabetes, arterial hypertension, dyslipidemia and smoking status were also independent CAD predictors (p<0.05). GRS added predictive value to TRF across all risk subgroups. NRI showed a significant improvement in all categories. In conclusion, GRS provided a better incremental value in intermediate subgroup. In this subgroup, inclusion of genotyping may be considered to better stratify cardiovascular risk.
Annals of Human Genetics, 2007
Although the risk for coronary heart disease (CHD) associated with single SNPs is modest it has been suggested that, in combination, several common risk-associated alleles could lead to a substantially better heart disease risk prediction. We have modelled this using 10 SNPs in ten candidate genes (APOB, NOS3, APOE, ACE, SERPINE1, MTHFR, ITGA2B, PON 1, LPL, and CETP) and their predicted summary risk estimates from meta-analysis. Based on published allele frequencies, ∼ 29% of the general population would be expected to carry less than three risk alleles, approximately 55% would carry 3 or 4 risk alleles, 4% would have 6 and 1% 7 or more risk alleles. Compared to the mean of those with 3 or 4 risk associated genotypes, those with 6 and 7-or-more alleles have a significantly higher risk odds ratio (OR) of CHD (mean OR (95% Confidence Intervals), 1.70 (1.14 to 2.55); and 4.51 (2.89 to 7.04) respectively), while compared to those in the lowest decile of risk, those in the highest decile have a CHD odds ratio in the range of 3.05 (2.24 to 4.14). Taking into account age and the risk alleles carried, the mean 10 year probability for developing CHD for a 55 year old man was calculated to be 15% (8.6% to 24.8%), with nearly 1 in 5 having more than 20% risk. Whether this particular group of 10 SNPs will improve the accuracy of CHD predictions over the combination of classical risk factors in clinical use requires further experimental evidence.
PLOS ONE
Polygenic risk scores are gaining more and more attention for estimating genetic risks for liabilities, especially for noncommunicable diseases. They are now calculated using thousands of DNA markers. In this paper, we compare the score distributions of two previously published very large risk score models within different populations. We show that the risk score model together with its risk stratification thresholds, built upon the data of one population, cannot be applied to another population without taking into account the target population's structure. We also show that if an individual is classified to the wrong population, his/ her disease risk can be systematically incorrectly estimated.
The Use of Imputed Values in the Meta-Analysis of Genome-Wide Association Studies
In genome-wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta-analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the ''expectation-substitution'' method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation-substitution method are usually combined using inverse variance weighting (IVM) scheme in meta-analysis. However, the IVM is not optimal as the estimates given by the expectation-substitution method are generally biased. The optimal weight is, in fact, proportional to the inverse variance and the expected value of the effect size estimates. We show both theoretically and numerically that the bias of the estimates is very small under practical conditions of low effect sizes in GWAS. This finding validates the use of the expectationsubstitution method, and shows the inverse variance is a good approximation of the optimal weight. Through simulation, we compared the power of the IVM method with several methods including the optimal weight, the regular z-score meta-analysis and a recently proposed ''imputation aware'' meta-analysis method (Zaitlen and Eskin [2010] Genet Epidemiol 34:537-542). Our results show that the performance of the inverse variance weight is always indistinguishable from the optimal weight and similar to or better than the other two methods. Genet. Epidemiol. 35:597-605, 2011.
Literature-Based Genetic Risk Scores for Coronary Heart Disease
Circulation: Cardiovascular Genetics, 2012
Background— Genome-wide association studies (GWAS) have identified many single-nucleotide polymorphisms (SNPs) associated with coronary heart disease (CHD) or CHD risk factors (RF). Using a case-cohort study within the prospective Cardiovascular Registry Maastricht (CAREMA) cohort, we tested if genetic risk scores (GRS) based on GWAS-identified SNPs are associated with and predictive for future CHD. Methods and Results— Incident cases (n=742), that is, participants who developed CHD during a median follow-up of 12.1 years (range, 0.0–16.9 years), were compared with a randomly selected subcohort of 2221 participants selected from the total cohort (n=21 148). We genotyped 179 SNPs previously associated with CHD or CHD RF in GWAS as published up to May 2, 2011. The allele-count GRS, composed of all SNPs, the 153 RF SNPs, or the 29 CHD SNPs were not associated with CHD independent of CHD RF. The weighted 29 CHD SNP GRS, with weights obtained from GWAS for every SNP, were associated with...
Assessment of the value of a genetic risk score in improving the estimation of coronary risk
Atherosclerosis, 2012
Background: The American Heart Association has established criteria for the evaluation of novel markers of cardiovascular risk. In accordance with these criteria, we assessed the association between a multilocus genetic risk score (GRS) and incident coronary heart disease (CHD), and evaluated whether this GRS improves the predictive capacity of the Framingham risk function. Methods and results: Using eight genetic variants associated with CHD but not with classical cardiovascular risk factors (CVRFs), we generated a multi-locus GRS, and found it to be linearly associated with CHD in two population based cohorts: The REGICOR Study (n = 2351) and The Framingham Heart Study (n = 3537) (meta-analyzed HR [95%CI]: ∼1.13 [1.01-1.27], per unit). Inclusion of the GRS in the Framingham risk function improved its discriminative capacity in the Framingham sample (c-statistic: 72.81 vs.72.37, p = 0.042) but not in the REGICOR sample. According to both the net reclassification improvement (NRI) index and the integrated discrimination index (IDI), the GRS improved re-classification among individuals with intermediate coronary risk (meta-analysis NRI [95%CI]: 17.44 [8.04; 26.83]), but not overall. Conclusions: A multi-locus GRS based on genetic variants unrelated to CVRFs was associated with a linear increase in risk of CHD events in two distinct populations. This GRS improves risk reclassification particularly in the population at intermediate coronary risk. These results indicate the potential value of the inclusion of genetic information in classical functions for risk assessment in the intermediate risk population group.
POLARIS: Polygenic LD-adjusted risk score approach for set-based analysis of GWAS data
Genetic epidemiology, 2018
Polygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set-based analyses by leveraging public genome-wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set-based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets. We call this method POLARIS: POlygenic Ld-Adjusted RIsk Score. POLARIS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals' SNP allele counts with LD-adjusted dosages. Using a raw genotype dataset together with SNP effect sizes from a second independent dataset, POLARIS can be used for set-based analysis. MAGMA...
Coronary Heart Disease Genetic Risk Score Predicts Cardiovascular Disease Risk in Men, Not Women
Circulation. Genomic and precision medicine, 2018
enetic risk scores (GRSs) quantify an individual's risk for a specified condition using estimates derived from genome-wide association studies. Early studies evaluating the use of cardiovascular disease GRSs comprising known coronary heart disease (CHD) risk variants demonstrate that high GRSs are associated with increased risk for cardiovascular events. 1 However, none of the published CHD GRS studies directly compare the performance of the risk score between men and women. This study is an analysis of the existing genotypic data from the MESA (Multi-Ethnic Study of Atherosclerosis) in the form of a literature-derived CHD GRS calculated for the white subpopulation of this cohort to evaluate whether a high CHD GRS was associated with an increased cardiovascular disease risk in the MESA cohort of white men and women. The MESA is a study of subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease and that predict progression of subclinical disease itself, in a diverse, population-based sample of 6814 men and women aged 45 to 84 unaffected with cardiovascular disease. Since 2000, MESA participants have been routinely evaluated to assess clinical morbidity and mortality and events. Our analysis includes data through the 2012 exam. Incident CHD was defined as myocardial infarction, resuscitated cardiac arrest, definite or probable angina if followed by a revascularization and CHD death. MESA participants were genotyped using the Affymetrix Genome-Wide Human Single Nucleotide Polymorphism (SNP) array 6.0. The CHD GRS was calculated using a literature-derived list of 46 SNPs (Figure) and the β-coefficient or the odds ratio from the most recently published genomewide association studies that included the SNP. Selected SNPs were independent as determined by excluding those in linkage disequilibrium (pruned, r 2 <0.2). The GRS for each participant was calculated using the allele count at each variant identified multiplied by the beta from the published genome-wide association studies for that variant. The products of the allele count and β were summed over all 46-loci for the total GRS and divided by average effect size (the average of betas from all published SNPs included in our GRS analysis). The data, analytic methods, and study material have been made available to other researchers for purposes of reproducing the results in this correspondence, and MESA genotyping data can be accessed via dbGaP. No Institutional Review Board approval was required for this study. Because the vast majority of genome-wide association studies for CHD were among white participants, this initial analysis focused on the white subpopulation of MESA. We excluded one participant because of incomplete genotyping data, so our final analysis included 2526 white participants. Sex differences were not apparent for age or mean GRS (all P>0.1). The rate of incident CHD in males was almost twice the rate in females. Mean GRS did not differ between males and females (Figure).
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
American Journal of Human Genetics, 2015
Polygenic"risk"scores"have"shown"great"promise"in"predicting"complex"disease" risk," and" will" become" more" accurate" as" training" sample" sizes" increase." " The" standard" approach" for" calculating" risk" scores" involves" LDBpruning" markers" and" applying" a" PBvalue" threshold" to" association" statistics," but" this" discards" information"and"may"reduce"predictive"accuracy."We"introduce"a"new"method," LDpred," which" infers" the" posterior" mean" causal" effect" size" of" each" marker" using" a" prior" on" effect" sizes" and" LD" information" from" an" external" reference" panel." Theory" and" simulations" show" that" LDpred" outperforms" the" pruning/thresholding" approach," particularly" at" large" sample" sizes." Accordingly," prediction" R 2 " increased" from" 20.1%" to" 25.3%" in" a" large" schizophrenia" data" set" and" from" 9.8%" to" 12.0%" in" a" large" multiple" sclerosis" data" set." A" similar" relative" improvement" in" accuracy" was" observed" for" three" additional" large" disease" data" sets" and" when" predicting" in" nonBEuropean" schizophrenia" samples." The" advantage" of" LDpred" over" existing" methods" will" grow"as"sample"sizes"increase.""" # Introduction" Polygenic!risk!scores!(PRS)!computed!from!genomeRwide!association!study!(GWAS)! summary! statistics! have! proven! valuable! for! predicting! disease! risk! and!
Diabetologia, 2009
Aims/hypothesis Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors.