A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7,8,9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
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R.M.P. is supported by National Institutes of Health (NIH) grants R01-AR057108, R01-AR056768, U01-GM092691 and R01-AR059648, and holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund. Y.O. is supported by a grant from the Japan Society of the Promotion of Science. D.W. is supported by a grant from the Australian National Health and Medical Research Council (1036541). G.T. is supported by the Rubicon grant from the Netherlands Organization for Scientific Research. A.Z. is supported by a grant from the Dutch Reumafonds (11-1-101) and from the Rosalind Franklin Fellowship, University of Groningen. S.-C.B., S.-Y.B. and H.-S.L. are supported by the Korea Healthcare technology R&D project, Ministry for Health and Welfare (A121983). J.M., M.A.G.-G. and L.R.-R. are funded by the RETICS program, RIER, RD12/0009 from the Instituto de Salud Carlos III, Health Ministry. S.R.-D. and L.Ä.’s work is supported by the Medical Biobank of Northern Sweden. H.K.C. is supported by NIH (NIAMS) grants R01-AR056291, R01-AR065944, R01-AR056768, P60 AR047785 and R21 AR056042. L.P. and L.K. are supported by a senior investigator grant from the European Research Council. S.R. is supported by NIH grants R01AR063759-01A1 and K08-KAR055688A. P.M.V. is a National Health and Medical Research Council Senior Principal Research Fellow. M.A.B. is funded by the National Health and Medical Research Foundation Senior Principal Research Fellowship, and a Queensland State Government Premier’s Fellowship. H.X. is funded by the China Ministry of Science and Technology (973 program grant 2011CB946100), the National Natural Science Foundation of China (grants 30972339, 81020108029 and 81273283), and the Science and Technology Commission of Shanghai Municipality (grants 08XD1400400, 11410701600 and 10JC1418400). K.A.S. is supported by a Canada Research Chair, The Sherman Family Chair in Genomics Medicine, Canadian Institutes for Health Research grant 79321 and Ontario Research Fund grant 05-075. S.M. is supported by Health and Labour Sciences Research Grants. The BioBank Japan Project is supported by the Ministry of Education, Culture, Sports, Science and Technology of the Japanese government. This study is supported by the BE THE CURE (BTCure) project. We thank K. Akari, K. Tokunaga and N. Nishida for supporting the study.
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About this article
Genome Medicine (2018)