This work was supported by the Augustinus Foundation; Avera Institute for Human Genetics (AIHG); AXA Research Fund; Belfast City Hospital Trust Fund, Research and Education into Ageing-0153; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, NWO 184.021.007); Biotechnology and Biological Sciences Research Council (BBSRC); Bristol-Myers Squibb; Center for Inherited Disease Research (CIDR); Centre for Medical Systems Biology (CMSB); CERA Foundation; Commissariat a` L’Energie Atomi- que (CEA)-Centre National de Ge´notypage (CNG); Danish Agency for Science, Technology and Innovation (DASTI)/The Danish Council for Independent Research (DCIR, grant 11-107308); Danish National Research Foundation (DNRF); Department of Health and Social Services (Northern Ireland); DFG-Cluster of Excellence ‘Inflammation at Interfaces’; Dunhill Medical Trust (grant R124/0509); Egmont Foundation; Estonian Science Foundation (grant 7859); Estonian Govern- ment (grant SF0180142s08); European Research Council (ERC, advanced grant 230374); European Science Foundation (ESF, EU/QLRT-2001-01254); European Union’s Fifth/Sixth/ Seventh Framework Programmes (FP5-QLK6-CY-2001- 00128, FP6-LIFESCIHEALTH-36894, FP6-LSH M-CT-2004-503270, FP7-HEALTH-2007-B-223004, FP7- HEALTH-F4-2007-201413, FP7-HEALTH-F4-2008-202047, FP7-HEALTH-2009-single-stage-242244 and FP7-HEALTH- 2010-two-stage-259679); Fondation Caisse d’Epargne Rhoˆne- Alpes Lyon CERAL (2004–2007); Genetic Association InformationNetwork(GAIN)oftheFoundationfortheUSNation- al Institutes of Health (NIMH, grant MH081802); GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254); Guy’s & St Thomas’ NHS Foundation Trust; Health Foundation; Heart and Lung foundation (grant 20070481); Innovation-Oriented Research Program on Genomics (SenterNovem, grant IGE05007); Institute for Ageing and Health; Institut National de la Recherche Agronomique (INRA); Institut National de la Sante´ et de la Recherche Me´dicale (INSERM); INTERREG 4A programme Syddanmark-Schleswig-K.E.R.N (with EU funds from the European Regional Development Fund); King’s College London; Medical Research Council (MRC, grant G0500997 and G0601333); Ministe`re de l’Enseignement supe´rieur et de la Recherche (MESR); National Institutes of Health (NIH)/National Institute of Aging (NIA, P01AG08761, R01D0042157-01A and U01DK066134); National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre; NBIC BioAssist (NWO-NBIC/BioAssist/RK/2008. 024); Netherlands Consortium for Healthy Ageing (NCHA, grant 050-060-810); Netherlands Genomics Initiative (NGI); Netherlands Heart Foundation (NHF, grant 2001 D 032); Netherlands Organization for Scientific Research (NWO, MagW/ZonMW grant 904-61-090, 904-61-193, 480-04-004, 400-05-717, Spinozapremie 56-464-14192, 175.010.2005.011, 911-03-012, 985-10-002, Addiction-31160008 and Middelg- root-911-09-032); Netspar – Living longer for a good health; NHS North of Tyne (Newcastle Primary Care Trust); Pharmacy Foundation; Regione Autonoma della Sardegna; Rutgers Uni- versity Cell and DNA Repository (NIMH U24 MH068457-06); Swedish Research Council (grant M-2005-1112); The Competi- tive Research Funding of the Tampere University Hospital and

Todetermine therelationshipbetween lociassociated withsur- vival ≥ 90 years, we used GRAIL (http://www.broadinstitute. org/mpg/grail/) (34). In short, this program maps SNPs to genes and subsequently uses a text-mining algorithm on PubMed abstracts to determine connections between these genes. Genes from independent loci, which share informative words, receive a high GRAIL similarity score and are more likely to be function- ally related. As we only had a limited number of loci with at least oneSNPwithaP-value ≤ 1 × 1025(n ¼ 12,Table2),wedecided toperformGRAILanalysisonalllociwithatleastoneSNPwitha P-value ≤ 1 × 1024 (n ¼ 65).

eQTL analysis

Todeterminewhetherrs2149954orSNPsinLD(r2 0.8basedon 1000 Genomes CEU Phase 1 data) influenced gene expression, we searched several eQTL databases, namely (1) the Gutenberg Heart Study database (GHS_Express) (48), which is based on expression data of monocytes; (2) the Genotype-Tissue Expression (GTEx) eQTL database (http://www.ncbi.nlm.nih.gov/gtex/GTEX2/gtex. cgi), which is based on expression data of brain (cerebellum, frontalcortex,temporalcortexandpons),liverandlymphoblastoid celllines;(3)theGENeExpressionVARiation(Genevar)database (http://www.sanger.ac.uk/resources/software/genevar/), which is based on expression data of adipose tissue, fibroblasts, T cells, skin and lymphoblastoid cell lines (49) and (4) the Blood eQTL browser (http://genenetwork.nl/bloodeqtlbrowser/) (50).

SUPPLEMENTARY MATERIAL

ACKNOWLEDGEMENTS

We thank Laurens Wilming from the Wellcome Trust Sanger In- stitute for the annotation of the lincRNA RP11-524N5.1. This study was undertaken within the framework of European Union’s Seventh Framework Programme (FP7/2007-2011) under grant agreement no 259679 (IDEAL). A full list of acknowledgments, including support for each study, is provided in Supplementary Material.

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