Dataset Information

A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.

ABSTRACT: Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ?185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

SUBMITTER: Nikpay M

PROVIDER: S-EPMC4589895 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.

Nikpay Majid M Goel Anuj A Won Hong-Hee HH Hall Leanne M LM Willenborg Christina C Kanoni Stavroula S Saleheen Danish D Kyriakou Theodosios T Nelson Christopher P CP Hopewell Jemma C JC Webb Thomas R TR Zeng Lingyao L Dehghan Abbas A Alver Maris M Armasu Sebastian M SM Auro Kirsi K Bjonnes Andrew A Chasman Daniel I DI Chen Shufeng S Ford Ian I Franceschini Nora N Gieger Christian C Grace Christopher C Gustafsson Stefan S Huang Jie J Hwang Shih-Jen SJ Kim Yun Kyoung YK Kleber Marcus E ME Lau King Wai KW Lu Xiangfeng X Lu Yingchang Y Lyytikäinen Leo-Pekka LP Mihailov Evelin E Morrison Alanna C AC Pervjakova Natalia N Qu Liming L Rose Lynda M LM Salfati Elias E Saxena Richa R Scholz Markus M Smith Albert V AV Tikkanen Emmi E Uitterlinden Andre A Yang Xueli X Zhang Weihua W Zhao Wei W de Andrade Mariza M de Vries Paul S PS van Zuydam Natalie R NR Anand Sonia S SS Bertram Lars L Beutner Frank F Dedoussis George G Frossard Philippe P Gauguier Dominique D Goodall Alison H AH Gottesman Omri O Haber Marc M Han Bok-Ghee BG Huang Jianfeng J Jalilzadeh Shapour S Kessler Thorsten T König Inke R IR Lannfelt Lars L Lieb Wolfgang W Lind Lars L Lindgren Cecilia M CM Lokki Marja-Liisa ML Magnusson Patrik K PK Mallick Nadeem H NH Mehra Narinder N Meitinger Thomas T Memon Fazal-Ur-Rehman FU Morris Andrew P AP Nieminen Markku S MS Pedersen Nancy L NL Peters Annette A Rallidis Loukianos S LS Rasheed Asif A Samuel Maria M Shah Svati H SH Sinisalo Juha J Stirrups Kathleen E KE Trompet Stella S Wang Laiyuan L Zaman Khan S KS Ardissino Diego D Boerwinkle Eric E Borecki Ingrid B IB Bottinger Erwin P EP Buring Julie E JE Chambers John C JC Collins Rory R Cupples L Adrienne LA Danesh John J Demuth Ilja I Elosua Roberto R Epstein Stephen E SE Esko Tõnu T Feitosa Mary F MF Franco Oscar H OH Franzosi Maria Grazia MG Granger Christopher B CB Gu Dongfeng D Gudnason Vilmundur V Hall Alistair S AS Hamsten Anders A Harris Tamara B TB Hazen Stanley L SL Hengstenberg Christian C Hofman Albert A Ingelsson Erik E Iribarren Carlos C Jukema J Wouter JW Karhunen Pekka J PJ Kim Bong-Jo BJ Kooner Jaspal S JS Kullo Iftikhar J IJ Lehtimäki Terho T Loos Ruth J F RJF Melander Olle O Metspalu Andres A März Winfried W Palmer Colin N CN Perola Markus M Quertermous Thomas T Rader Daniel J DJ Ridker Paul M PM Ripatti Samuli S Roberts Robert R Salomaa Veikko V Sanghera Dharambir K DK Schwartz Stephen M SM Seedorf Udo U Stewart Alexandre F AF Stott David J DJ Thiery Joachim J Zalloua Pierre A PA O'Donnell Christopher J CJ Reilly Muredach P MP Assimes Themistocles L TL Thompson John R JR Erdmann Jeanette J Clarke Robert R Watkins Hugh H Kathiresan Sekar S McPherson Ruth R Deloukas Panos P Schunkert Heribert H Samani Nilesh J NJ Farrall Martin M

Nature genetics 20150907 10

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ...[more]

PMID: 26343387

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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.

Dataset Information

A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.

Publications

A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.

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