Association of Genetically Predicted Levels of Circulating Blood Lipids with Coronary Artery Disease Incidence

Background Estimating the genetic risk of coronary artery disease (CAD) is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). Combining multiple PRS for specific circulating blood lipids could improve risk prediction. Here, we sought to evaluate the performance of PRS derived from CAD and blood lipids GWAS to predict the incidence of CAD. Methods This study included individuals aged between 40 and 69 recruited in UK Biobank (UKB). We conducted GWAS for blood lipids measured by nuclear magnetic resonance in individuals without lipid-lowering treatments (n=73,915). Summary statistics were used to derive and calculate PRS in the remaining participants (n=318,051). A PRSCAD was also derived using the CARDIoGRAMplusC4D GWAS. Hazard ratios (HR) for CAD (9,017 / 301,576; median follow-up time: 12.6 years) were calculated per standard deviation increase in each PRS. Discrimination capacity and goodness of fit of the models were evaluated. Results Out of 30 PRS, 28 were significantly associated with the incidence of CAD ( P <0.05). The optimal combination of PRS included PRS for CAD, VLDL-C, total cholesterol and triglycerides. Discriminative capacities were significantly increased in the model including PRSCAD and clinical risk factors (CRF) (C-statistic=0.778 [0.773-0.782]) compared to the model with CRF only (C-statistic=0.755 [0.751-0.760]). Although the C-statistic remained similar when independent lipids PRS were added to the model with PRSCAD and CRF (C-statistic=0.778 [0.773-0.783]), the goodness of fit was significantly increased (chi-square test statistic=20.18, P =1.56e-04). Conclusions Although independently associated with CAD incidence, blood lipids PRS provide modest improvement in the predictive performance when added to PRSCAD. Highlights ![Figure][1] ### Competing Interest Statement BJA is a consultant for Novartis and Silence Therapeutics and has received research funding from Pfizer and Ionis Pharmaceuticals. The other authors report no conflicts. ### Funding Statement HDM holds a doctoral research award from the Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval. BJA holds a senior scholar award from the Fonds de Recherche du Québec: Santé. YB holds a Canada Research Chair in Genomics of Heart and Lung Diseases. PM is the recipient of the Joseph C. Edwards Foundation granted to Université Laval. This work was supported by a grant from the Canadian Institutes of Health Research (PJT?162344) to ST. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data that support our findings are available through appropriate application to UK Biobank (UKB). UKB is a large-scale prospective study that received approval from the British National Health Service, North West - Haydock Research Ethics Committee (16/NW/0274). This cohort includes electronic health records of > 500,000 individuals aged between 40 and 73 years at baseline and recruited from 2006 to 2010 in the United Kingdom. All participants of UKB provided informed consent at the baseline assessment. Data access permission for this study was granted under UKB application 25205. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as [ClinicalTrials.gov][2]. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Computational codes are available, without compromising sensitive individual-level data, from the corresponding author upon reasonable request. Summary statistics of genome-wide association studies and effect sizes to calculate PRS will be deposited respectively on GWAS Catalog platform () and PGS Catalog (). * ### Non-Standard Abbreviations and Acronyms ApoA1 : Apolipoprotein A1 ApoB : Apolipoprotein B CAD : Coronary Artery Disease CRF : Clinical Risk Factors DHA : Docosahexaenoic Acid EPA : Eicosapentaenoic Acid FA : Fatty Acids FUMA : Functional Mapping and Annotation GLGC : Global Lipid Genetic Consortium GWAS : Genome Wide Association Study HES : Hospital Episode Statistics IDL : Intermediate Density Lipoproteins IS : Ischemic Stroke LA : Linoleic Acid LD : Linkage Disequilibrium LLT : Lipid Lowering Treatments Lp(a) : Lipoprotein(a) MAGMA : Multi-marker Analysis of GenoMic Annotation MI : Myocardial Infarction MUFA : Mono-Unsaturated Fatty Acids NMR : Nuclear Magnetic Resonance NRI : Net Reclassification Improvement OPSC-4 : Office Of Population, Censuses and Surveys Classification of Interventions and Procedures, Version 4 PCs : First Ten Genetic Principal Components PCE : Pooled Cohort Equation PRS : Polygenic Risk Score PUFA : Polyunsaturated Fatty Acids SFA : Saturated Fatty Acids SBP : Systolic Blood Pressure TC : Total Cholesterol TG : Triglycerides UKB : UK Biobank VLDL : Very Low-Density Lipoproteins XL-VLDL : Extra Large Very Low-Density Lipoproteins or Chylomicrons [1]: pending:yes [2]: http://ClinicalTrials.gov