Pathway analysis: For the pathway analysis, we used GSEA implemented in MAGENTA (http://www.broadinstitute.org/mpg/magenta/) (33). In short, each SNP is mapped to a gene considering a window of 110 kb upstream and 40 kb downstream around the genes. Subsequently, each gene is assigned a gene association score based on the SNP with the lowest P-value, which is mapped to that gene, and this score is adjusted for confounding factors like gene size and the amount of SNPs per kb. Genes within the HLA region were removed from analysis owing to high LD and high gene density in that region. The GSEA algorithm tests for over-representation of adjusted gene scores in a given pathway using a pre-defined score rank cutoff (in our case, the 95th and 75th percentile). The generated statistic is then compared with 10,000-1,000,000 gene sets of identical size randomly sampled from the genome to generate an empirical P-value for each pathway. In total, 3216 pathways from Gene Ontology, PANTHER, Ingenuity, KEGG, REACTOME, and BIOCARTA were tested. Pathways were considered significant if the FDR-adjusted P-value (the 95th or 75th percentile) was <0.05.

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` LEnergie Atomique (CEA)-Centre National de Genotypage (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 Government (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 Rhone-Alpes Lyon CERAL (2004–2007); Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health (NIMH, grant MH081802); GenomEUtwin (EU/QLRT-2001-01254; QLG2-CT-2002-01254); Guys & 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 Medicale (INSERM); INTERREG 4A programme Syddanmark-Schleswig-K.E.R.N (with EU funds from the European Regional Development Fund); Kings College London; Medical Research Council (MRC, grant G0500997 and G0601333); Ministere de l'Enseignement superieur 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 (NISorry, the text you provided seems to be incomplete. Please provide the complete text.A.G., Magnusson, P.K., Pedersen, N.L., Ingelsson, E., and Tiemeier, H. () Genetic determinants of mortality: Can findings from genome-wide association studies explain variation in human mortality? Hum. Genet.,  ,

Beekman, M., Nederstigt, C., Suchiman, H.E., Kremer, D., van der Reggen, R., Lakenberg, N., Alemayehu, W.G., de Craen, A.J., Westendorp, R.G., Zeggini, E., Panoutsopoulou, K., Southam, L., Rayner, N.W., Day-Williams, A.G., Lopes, M.C., Boraska, V., Esko, T., Evangelou, E., Hoffman, A. et al. () Identification of new susceptibility loci for osteoarthritis (arGEN): a genome-wide association study. Lancet,

Hollingworth, P., Harold, D., Sims, R., Gerrish, A., Lambert, J.C., Carrasquillo, M.M., Abraham,R., Hamshere,M.L., Pahwa,J.S. Moskvina,V. et al. () Common variants at ABCA7,M S4A6A/M S4A6E,EPHA1 , CD33 and CD2AP are associated with Alzheimer's disease. Nat. Genet.

Naj,A.C. , Jun,G. , Beecham,G.W. , Wang,L.S. , Vardarajan,B.N. , Buros,J. , Gallins,P.J. , Buxbaum,J.D. , Jarvik,G.P. , Crane,P.K. et al. () Common variants at MS4A4/MS4A6E,C D33 ,and EPHA1 are associated with late-onset Alzheimer's disease. Nat. Genet.,

Uh, H.W., Deelen, J., Beekman, M., Helmer, Q., Rivadeneira, F., Hottenga, J.J., Boomsma, D.I., Hofman, A., Uitterlinden, A.G., Slagboom, P.E. et al. () How to deal with the early WAS data when imputing and combining different arrays is necessary. Eur. J. Hum. Genet.,

Peer,I. , Yelensky,R. , Altshuler,D. and Daly,M.J. () Estimation of the multiple testing burden for genome-wide association studies of nearly all common variants. Genet. Epidemiol.,

Zeller,T., Wild,P., Szymczak,S., Rotival,M., Schillert,A., Castagne,R., Maouche,S., Germain,M., Lackner,K., Rossmann,H. et al. () Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. PLoS One,

Yang,T.P. , Beazley,C. , Montgomery,S.B. , Dimas,A.S. , Gutierrez-Arcelus,M. , Stranger,B.E. , Deloukas,P. and Dermitzakis,E.T. () Genevar: a database and Java application for the analysis and visualization of NP-gene associations in transcriptome-wide association studies.Bioinformatics,

Westra,H.J., Peters,M.J., Esko,T., Yaghootkar,H., Schurmann,C., Kettunen,J., Christiansen,M.W ., Fairfax,B.P ., Schramm,K ., Powell,J.E . et al. () Systematic identification of trans-eQTLs as putative drivers of known disease associations.Nat.Genet.