Dataset Information

Genome-wide association study of coronary artery disease among individuals with diabetes: the UK Biobank.

ABSTRACT: AIMS/HYPOTHESIS:Coronary artery disease (CAD) is a common complication among individuals with diabetes. A better understanding of the genetic background of CAD in this population has the potential to suggest novel molecular targets for screening, risk assessment and drug development. METHODS:We performed a genome-wide association study of CAD in 15,666 unrelated individuals (3,968 CAD cases and 11,698 controls) of white British ancestry with diabetes at inclusion in the UK Biobank study. Our results were compared with results from participants without diabetes. RESULTS:We found genome-wide significant evidence for association with CAD at the previously well-established LPA locus (lead variant: rs74617384; OR 1.38 [95% CI 1.26, 1.51], p?=?3.2?×?10-12) and at 9p21 (lead variant: rs10811652; OR 1.19 [95% CI 1.13, 1.26], p?=?6.0?×?10-11). Moreover, other variants previously associated with CAD showed similar effects in the participants with and without diabetes, indicating that the genetic architecture of CAD is largely the same. CONCLUSIONS/INTERPRETATION:Our results indicate large similarities between the genetic architecture of CAD in participants with and without diabetes. Larger studies are needed to establish whether there are important diabetes-specific CAD loci.

SUBMITTER: Fall T

PROVIDER: S-EPMC6133153 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Genome-wide association study of coronary artery disease among individuals with diabetes: the UK Biobank.

Fall Tove T Gustafsson Stefan S Orho-Melander Marju M Ingelsson Erik E

Diabetologia 20180712 10

<h4>Aims/hypothesis</h4>Coronary artery disease (CAD) is a common complication among individuals with diabetes. A better understanding of the genetic background of CAD in this population has the potential to suggest novel molecular targets for screening, risk assessment and drug development.<h4>Methods</h4>We performed a genome-wide association study of CAD in 15,666 unrelated individuals (3,968 CAD cases and 11,698 controls) of white British ancestry with diabetes at inclusion in the UK Biobank ...[more]

PMID: 30003307

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Project description:Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD.Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD.Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05).We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Project description:ImportanceA lack of internationally agreed standards for combining available data sources at scale risks inconsistent disease phenotyping limiting research reproducibility.ObjectiveTo develop and then evaluate if a rules-based algorithm can identify coronary artery disease (CAD) sub-phenotypes using electronic health records (EHR) and questionnaire data from UK Biobank (UKB).DesignCase-control and cohort study.SettingProspective cohort study of 502K individuals aged 40-69 years recruited between 2006-2010 into the UK Biobank with linked hospitalization and mortality data and genotyping.ParticipantsWe included all individuals for phenotyping into 6 predefined CAD phenotypes using hospital admission and procedure codes, mortality records and baseline survey data. Of these, 408,470 unrelated individuals of European descent had a polygenic risk score (PRS) for CAD estimated.ExposureCAD Phenotypes.Main outcomes and measuresAssociation with baseline risk factors, mortality (n = 14,419 over 7.8 years median f/u), and a PRS for CAD.ResultsThe algorithm classified individuals with CAD into prevalent MI (n = 4,900); incident MI (n = 4,621), prevalent CAD without MI (n = 10,910), incident CAD without MI (n = 8,668), prevalent self-reported MI (n = 2,754); prevalent self-reported CAD without MI (n = 5,623), yielding 37,476 individuals with any type of CAD. Risk factors were similar across the six CAD phenotypes, except for fewer men in the self-reported CAD without MI group (46.7% v 70.1% for the overall group). In age- and sex- adjusted survival analyses, mortality was highest following incident MI (HR 6.66, 95% CI 6.07-7.31) and lowest for prevalent self-reported CAD without MI at baseline (HR 1.31, 95% CI 1.15-1.50) compared to disease-free controls. There were similar graded associations across the six phenotypes per SD increase in PRS, with the strongest association for prevalent MI (OR 1.50, 95% CI 1.46-1.55) and the weakest for prevalent self-reported CAD without MI (OR 1.08, 95% CI 1.05-1.12). The algorithm is available in the open phenotype HDR UK phenotype library (https://portal.caliberresearch.org/).ConclusionsAn algorithmic, EHR-based approach distinguished six phenotypes of CAD with distinct survival and PRS associations, supporting adoption of open approaches to help standardize CAD phenotyping and its wider potential value for reproducible research in other conditions.

Dataset Information

Genome-wide association study of coronary artery disease among individuals with diabetes: the UK Biobank.

Publications

Genome-wide association study of coronary artery disease among individuals with diabetes: the UK Biobank.

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