Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.
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We acknowledge CSC (IT Center for Science) and the Technology Centre of the Institute for Molecular Medicine for computational services. The High-Throughput Biomedicine Unit of the Institute for Molecular Medicine Finland, R. Kovanen and A. Uro are acknowledged for technical expertise. M. Jauhiainen is acknowledged for sharing his expertise in the manuscript writing process. This research was supported through funds from the European Union's Seventh Framework Programme (FP7/2007–2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. I.S. was partly funded by the Helsinki University Doctoral Programme in Biomedicine (DPBM). M.H. was funded by a Manpei Suzuki Diabetes Foundation Grant-in-Aid for young scientists working abroad. A.P.M. and A. Mahajan acknowledge funding from the Wellcome Trust under awards WT098017, WT090532 and WT064890. V. Lagou, L.M., S.H. and I.P. were funded in part through the European Union's Seventh Framework Programme (FP7/2007–2013), ENGAGE project, grant agreement HEALTH-F4-2007- 201413. L.M. was in part sponsored by '5 per mille' contribution assigned to the University of Ferrara, income tax return year 2009, and in part by the ENGAGE Exchange and Mobility Program for ENGAGE training funds. M.D.T. holds a UK Medical Research Council Senior Clinical Fellowship (G0902313). S.T. is supported by the Sigrid Juselius Foundation. J.S.R. and C.G. have received funding from a grant from the RFBR (Russian Foundation for Basic Research)–Helmholtz Joint Research Group (12-04-91322). C.G. received funding from the European Union's Seventh Framework Programme (FP7-Health-F5-2012) under grant agreement 305280 (MIMOmics). M. Perola has been supported by the European Union's Seventh Framework Programme (grant agreements 313010; BBMRI-LPC, 305280; MIMOmics, and 261433; BioSHaRE-EU), Finnish Academy grant 269517, the Yrjö Jahnsson Foundation and the Juho Vainio Foundation. N.J.S. holds a chair funded by the British Heart Foundation (BHF) and is an NIHR Senior Investigator. C.P.N. is funded by the BHF and was preciously funded by the NIHR Leicester Cardiovascular Biomedical Research Unit. V. Salomaa was supported by the Finnish Foundation for Cardiovascular Research and the Finnish Academy (grant 139635). E. Ikonen was supported by the Academy of Finland Centre of Excellence in Biomembrane Research (272130), the Academy of Finland (263841) and the Sigrid Juselius Foundation. V.P. was supported by a University of Helsinki Postdoctoral Researcher grant, the Magnus Ehrnrooth Foundation and the Kymenlaakso Cultural Foundation. O.K., J.-P.M. and V.P. have received funding from the European Union's Seventh Framework Programme (FP7/2007–2013) under grant agreement 258068; EU-FP7-Systems Microscopy Network of Excellence. S.R. was supported by the Academy of Finland (251217 and 255847), the Center of Excellence in Complex Disease Genetics, the European Union's Seventh Framework Programme projects ENGAGE (201413) and BioSHaRE (261433), the Finnish Foundation for Cardiovascular Research, Biocentrum Helsinki and the Sigrid Juselius Foundation. Cohort-specific acknowledgments are provided in the Supplementary Note.
Lists of genes with higher MGI-based predictions in the GeneNetwork database for the knockout phenotypes listed in Supplementary Table 8a,b.