Genome-wide association studies (GWAS) in coronary artery disease (CAD) had identified 66 loci at 'genome-wide significance' (P < 5 × 10−8) at the time of this analysis, but a much larger number of putative loci at a false discovery rate (FDR) of 5% (refs. 1,2,3,4). Here we leverage an interim release of UK Biobank (UKBB) data to evaluate the validity of the FDR approach. We tested a CAD phenotype inclusive of angina (SOFT; ncases = 10,801) as well as a stricter definition without angina (HARD; ncases = 6,482) and selected cases with the former phenotype to conduct a meta-analysis using the two most recent CAD GWAS2, 3. This approach identified 13 new loci at genome-wide significance, 12 of which were on our previous list of loci meeting the 5% FDR threshold2, thus providing strong support that the remaining loci identified by FDR represent genuine signals. The 304 independent variants associated at 5% FDR in this study explain 21.2% of CAD heritability and identify 243 loci that implicate pathways in blood vessel morphogenesis as well as lipid metabolism, nitric oxide signaling and inflammation.
At a glance
- CARDIoGRAMplusC4D Consortium. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat. Genet. 45, 25–33 (2013).
- A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015). et al.
- Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134–1144 (2016).
- Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. Systematic evaluation of pleiotropy identifies six further loci associated with coronary artery disease. J. Am. Coll. Cardiol. 69, 823–836 (2017).
- Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017). et al.
- Cardiovascular Disease Statistics 2015 (British Heart Foundation, 2015). , , &
- Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012). , , & et al.
- EPIC-Heart: the cardiovascular component of a prospective study of nutritional, lifestyle and biological factors in 520,000 middle-aged participants from 10 European countries. Eur. J. Epidemiol. 22, 129–141 (2007). et al.
- Myofibroblast contraction activates latent TGF-β1 from the extracellular matrix. J. Cell Biol. 179, 1311–1323 (2007). , , &
- Targeting of αv integrin identifies a core molecular pathway that regulates fibrosis in several organs. Nat. Med. 19, 1617–1624 (2013). et al.
- TGF-β signaling in vascular biology and dysfunction. Cell Res. 19, 116–127 (2009). , &
- CDKN2B regulates TGFβ signaling and smooth muscle cell investment of hypoxic neovessels. Circ. Res. 118, 230–240 (2016). et al.
- Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases. Science 353, 827–830 (2016). et al.
- Cyclic guanosine monophosphate signaling and phosphodiesterase-5 inhibitors in cardioprotection. J. Am. Coll. Cardiol. 59, 1921–1927 (2012). , &
- The GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multi-tissue gene regulation in humans. Science 348, 648–660 (2015).
- The guanine-nucleotide exchange factor SGEF plays a crucial role in the formation of atherosclerosis. PLoS One 8, e55202 (2013). et al.
- Absence of regulated splicing of fibronectin EDA exon reduces atherosclerosis in mice. Atherosclerosis 197, 534–540 (2008). et al.
- Plasma fibronectin deficiency impedes atherosclerosis progression and fibrous cap formation. EMBO Mol. Med. 4, 564–576 (2012). et al.
- Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015). et al.
- The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016). et al.
- The impact of low-frequency and rare variants on lipid levels. Nat. Genet. 47, 589–597 (2015). et al.
- Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms. Nat. Genet. 49, 1113–1119 (2017). et al.
- Including known covariates can reduce power to detect genetic effects in case–control studies. Nat. Genet. 44, 848–851 (2012). , &
- GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010). &
- QQVALUE: Stata module to generate quasi-q-values by inverting multiple-test procedures S457100 (Boston College Department of Economics, 2013).
- A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans. Bioinformatics 32, 2598–2603 (2016). et al.
- Multiple-test procedures and smile plots. Stata J. 3, 109–132 (2003).
- The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765–776 (2014). , &
- Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank. Nat. Genet. 48, 980–983 (2016). et al.
- HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 40, D930–D934 (2012). &
- ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
- Presenting the Epigenome Roadmap. Nature 518, 313 (2015). et al.
- TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378 (2003). et al.
- JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 38, D105–D110 (2010). et al.
- A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). et al.
- Functional annotation of noncoding sequence variants. Nat. Methods 11, 294–296 (2014). , , &
- Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012). et al.
- PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016). et al.
- Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015). et al.