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      Clinical Utility and Practical Considerations of a Coronary Artery Disease Genetic Risk Score

      research-article
      , BSc a , b , , BSc a , , BSc d , , BSc d , , MD c , d , e ,
      CJC Open
      Elsevier

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          Abstract

          Background

          Coronary artery disease (CAD) risk traditionally has been assessed using clinical risk factors. We evaluated whether molecular genetic markers for CAD risk could add information to traditional variables.

          Methods

          We developed a false discovery rate 267-marker genetic risk score (FDR 267) from markers that were significantly associated with CAD in the UK Biobank cohort meta-analysis. FDR 267 was tested in the Atherosclerosis Risk in Communities cohort using logistic regression and Cox proportional hazards analyses in the European and African American groups.

          Results

          Our genetic risk score (FDR 267) was associated with a 1.45 (95% confidence interval, 1.39-1.51) increase in odds ratio and a 1.32 (95% confidence interval, 1.26-1.38) increase in hazard ratio per standard deviation of the score. The score modestly improved the area under the curve (AUC) statistic when added to a clinical model (ΔAUC = 0.0112, P = 0.0002). FDR 267 predicted incident CAD (C-index = 0.60), although it did not improve on clinical risk factors (ΔAUC = 0.0159, P = 0.0965). Individuals in the top quintile of FDR 267 genetic risk were at approximately 2-fold increased risk compared with the bottom quintile, which is comparable to risk associated with self-reported family history. The performance of FDR 267 was less robust in the African American sample.

          Conclusions

          FDR 267 is significantly associated with CAD in the European sample, with an effect size comparable to self-reported family history. FDR 267 discriminated between individuals with and without CAD, but did not improve CAD risk prediction over clinical variables. FDR 267 was less predictive of CAD risk in African Americans.

          Résumé

          Introduction

          L’évaluation du risque de maladie coronarienne (MC) a traditionnellement reposé sur les facteurs de risque cliniques. Nous avons évalué si les marqueurs génétiques moléculaires de risque de MC pourraient servir de complément aux variables traditionnelles.

          Méthodes

          Nous avons élaboré un taux de fausses découvertes (FDR pour false discovery rate) du score de risque génétique du marqueur 267 (FDR 267) provenant des marqueurs qui étaient associés de manière significative à la MC dans la méta-analyse de cohortes de la UK Biobank. Le FDR 267 a été testé dans la cohorte du Atherosclerosis Risk in Communities à l’aide de la régression logistique et des analyses selon le modèle à risques proportionnels de Cox dans des groupes européens et afro-américains.

          Résultats

          Notre score de risque génétique (FDR 267) a été associé à une augmentation de 1,45 (intervalle de confiance [IC] à 95 %, 1,39-1,51) du rapport des cotes et d’une augmentation de 1,32 (IC à 95 %, 1,26-1,38) du risque relatif par l’écart-type des scores. Le score a modestement amélioré l’aire sous la courbe (ASC) lorsqu’il a été ajouté à un modèle clinique (ΔASC = 0,0112, P = 0,0002). Le FDR 267 a prédit les nouveaux cas de MC (C-index [indice de concordance] = 0,60), mais il n’a pas amélioré les facteurs de risque cliniques (ΔASC = 0,0159, P = 0,0965). Les individus dans le quintile supérieur du risque génétique du FDR 267 ont montré un risque accru d’environ 2 fois par rapport au quintile inférieur, soit un risque comparable au risque associé aux antécédents familiaux auto-rapportés. La performance du FDR 267 s’est révélée moins robuste chez les Afro-Américains.

          Conclusions

          Le FDR 267 est associé de manière significative à la MC dans l’échantillon d’Européens et a une taille de l’effet comparable aux antécédents familiaux auto-rapportés. Le FDR 267 a fait la discrimination entre les individus atteints ou non atteints de MC, mais n’a pas amélioré la prédiction du risque de MC par rapport aux variables cliniques. Le FDR 267 a moins bien prédit le risque de MC chez les Afro-Américains.

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          Most cited references15

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults

            Background Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated comprehensively, because available studies have involved limited genomic scope and limited sample sizes. Objectives This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention. Methods Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank. Results The hazard ratio (HR) for CAD was 1.71 (95% confidence interval [CI]: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age. Conclusions The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.
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              Accounting for ancestry: population substructure and genome-wide association studies.

              Accounting for the genetic substructure of human populations has become a major practical issue for studying complex genetic disorders. Allele frequency differences among ethnic groups and subgroups and admixture between different ethnic groups can result in frequent false-positive results or reduced power in genetic studies. Here, we review the problems and progress in defining population differences and the application of statistical methods to improve association studies. It is now possible to take into account the confounding effects of population stratification using thousands of unselected genome-wide single-nucleotide polymorphisms or, alternatively, selected panels of ancestry informative markers. These methods do not require any demographic information and therefore can be widely applied to genotypes available from multiple sources. We further suggest that it will be important to explore results in homogeneous population subsets as we seek to define the extent to which genomic variation influences complex phenotypes.
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                Author and article information

                Contributors
                Journal
                CJC Open
                CJC Open
                CJC Open
                Elsevier
                2589-790X
                29 March 2019
                March 2019
                29 March 2019
                : 1
                : 2
                : 69-75
                Affiliations
                [a ]Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
                [b ]University of Waterloo, Waterloo, Ontario, Canada
                [c ]Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
                [d ]Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
                [e ]Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
                Author notes
                []Corresponding author: Dr Robert A. Hegele, Robarts Research Institute, 4288A - 1151 Richmond Street North, London, Ontario N6A 5B7, Canada. Tel.: +1-519-931-5271; fax: +1-519-931-5218. hegele@ 123456robarts.ca
                Article
                S2589-790X(19)30003-4
                10.1016/j.cjco.2019.01.003
                7063618
                32159086
                87bb7a32-a5fe-47bd-a392-30ff38eb00c5
                © 2019 Canadian Cardiovascular Society. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 6 December 2018
                : 16 January 2019
                Categories
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