Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).
Google Scholar
Gaziano, J. M. et al. Million veteran program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).
Google Scholar
Denny, J. C. et al. The “All of Us” research program. N. Engl. J. Med. 381, 668–676 (2019).
Google Scholar
Consortium, I. M. S. G. Low-frequency and rare-coding variation contributes to multiple sclerosis risk. Cell 175, 1679–1687.e1677 (2018).
Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).
Google Scholar
Lee, A. et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet. Med. 21, 1708–1718 (2019).
Google Scholar
Curtis, D. Construction of an exome-wide risk score for schizophrenia based on a weighted burden test. Ann. Hum. Genet. 82, 11–22 (2018).
Google Scholar
Biddinger, K. J. et al. Rare and common genetic variation underlying the risk of hypertrophic cardiomyopathy in a national biobank. JAMA Cardiol. 7, 715–722 (2022).
Google Scholar
Wright, C. F. et al. Genomic diagnosis of rare pediatric disease in the United Kingdom and Ireland. N. Engl. J. Med. 388, 1559–1571 (2023).
Google Scholar
Lunke, S. et al. Integrated multi-omics for rapid rare disease diagnosis on a national scale. Nat. Med. 29, 1681–1691 (2023).
Google Scholar
Beck, D. B. et al. Somatic mutations in UBA1 and severe adult-onset autoinflammatory disease. N. Engl. J. Med. 383, 2628–2638 (2020).
Google Scholar
Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).
Google Scholar
Whiffin, N. et al. The effect of LRRK2 loss-of-function variants in humans. Nat. Med. 26, 869–877 (2020).
Google Scholar
Akbari, P. et al. Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity. Science 373, eabf8683 (2021).
Google Scholar
Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).
Google Scholar
Myasoedova, E., Davis, J., Matteson, E. L. & Crowson, C. S. Is the epidemiology of rheumatoid arthritis changing? Results from a population-based incidence study, 1985-2014. Ann. Rheum. Dis. 79, 440–444 (2020).
Google Scholar
Frisell, T. et al. Familial risks and heritability of rheumatoid arthritis: role of rheumatoid factor/anti-citrullinated protein antibody status, number and type of affected relatives, sex, and age. Arthritis Rheum. 65, 2773–2782 (2013).
Google Scholar
Svendsen, A. J. et al. On the origin of rheumatoid arthritis: the impact of environment and genes — a population based twin study. PLoS One 8, e57304 (2013).
Google Scholar
Stastny, P. Mixed lymphocyte cultures in rheumatoid arthritis. J. Clin. Invest. 57, 1148–1157 (1976).
Google Scholar
Gough, S. C. & Simmonds, M. J. The HLA region and autoimmune disease: associations and mechanisms of action. Curr. Genomics 8, 453–465 (2007).
Google Scholar
Kulski, J. K., Suzuki, S. & Shiina, T. Human leukocyte antigen super-locus: nexus of genomic supergenes, SNPs, indels, transcripts, and haplotypes. Hum. Genome Var. 9, 49 (2022).
Google Scholar
Gregersen, P. K., Silver, J. & Winchester, R. J. The shared epitope hypothesis. An approach to understanding the molecular genetics of susceptibility to rheumatoid arthritis. Arthritis Rheum. 30, 1205–1213 (1987).
Google Scholar
Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).
Google Scholar
Begovich, A. B. et al. A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am. J. Hum. Genet. 75, 330–337 (2004).
Google Scholar
Rieck, M. et al. Genetic variation in PTPN22 corresponds to altered function of T and B lymphocytes. J. Immunol. 179, 4704–4710 (2007).
Google Scholar
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678 (2007).
Ishigaki, K. et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat. Genet. 54, 1640–1651 (2022).
Google Scholar
Sparks, J. A. et al. Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history. Ann. Rheum. Dis. 74, 1522–1529 (2015).
Google Scholar
Rostami, S., Hoff, M., Brown, M. A., Hveem, K. & Videm, V. Comparison of methods to construct a genetic risk score for prediction of rheumatoid arthritis in the population-based Nord-Trondelag Health Study, Norway. Rheumatology 59, 1743–1751 (2020).
Google Scholar
Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Google Scholar
Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).
Google Scholar
Rapaport, F. et al. Negative selection on human genes underlying inborn errors depends on disease outcome and both the mode and mechanism of inheritance. Proc. Natl Acad. Sci. USA 118, e2001248118 (2021).
Google Scholar
Lee, S., Abecasis, G. R., Boehnke, M. & Lin, X. Rare-variant association analysis: study designs and statistical tests. Am. J. Hum. Genet. 95, 5–23 (2014).
Google Scholar
Momozawa, Y. & Mizukami, K. Unique roles of rare variants in the genetics of complex diseases in humans. J. Hum. Genet. 66, 11–23 (2021).
Google Scholar
Diogo, D. et al. TYK2 protein-coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non-autoimmune complex traits. PLoS One 10, e0122271 (2015).
Google Scholar
National Human Genome Research Institute. Human Genomic Variation. https://www.genome.gov/about-genomics/educational-resources/fact-sheets/human-genomic-variation (2023).
Lee, S. et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am. J. Hum. Genet. 91, 224–237 (2012).
Google Scholar
Eichler, E. E. Genetic variation, comparative genomics, and the diagnosis of disease. N. Engl. J. Med. 381, 64–74 (2019).
Google Scholar
National Library of Medicine. Overview of Structural Variation. https://www.ncbi.nlm.nih.gov/dbvar/content/overview/ (2022).
International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).
Lelieveld, S. H., Spielmann, M., Mundlos, S., Veltman, J. A. & Gilissen, C. Comparison of exome and genome sequencing technologies for the complete capture of protein-coding regions. Hum. Mutat. 36, 815–822 (2015).
Google Scholar
Diogo, D. et al. Rare, low-frequency, and common variants in the protein-coding sequence of biological candidate genes from GWASs contribute to risk of rheumatoid arthritis. Am. J. Hum. Genet. 92, 15–27 (2013).
Google Scholar
Hu, T., Chitnis, N., Monos, D. & Dinh, A. Next-generation sequencing technologies: an overview. Hum. Immunol. 82, 801–811 (2021).
Google Scholar
Logsdon, G. A., Vollger, M. R. & Eichler, E. E. Long-read human genome sequencing and its applications. Nat. Rev. Genet. 21, 597–614 (2020).
Google Scholar
Gudmundsson, S. et al. Variant interpretation using population databases: lessons from gnomAD. Hum. Mutat. 43, 1012–1030 (2022).
Google Scholar
Simpfendorfer, K. R. et al. Autoimmune disease-associated haplotypes of BLK exhibit lowered thresholds for B cell activation and expansion of Ig class-switched B cells. Arthritis Rheumatol. 67, 2866–2876 (2015).
Google Scholar
Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genom. 2, 100168 (2022).
Google Scholar
Saevarsdottir, S. et al. Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset. Ann. Rheum. Dis. 81, 1085–1095 (2022).
Google Scholar
González-Serna, D. et al. Association of a rare variant of the TNFSF13B gene with susceptibility to rheumatoid arthritis and systemic lupus erythematosus. Sci. Rep. 8, 8195 (2018).
Google Scholar
Mitsunaga, S. et al. Exome sequencing identifies novel rheumatoid arthritis-susceptible variants in the BTNL2. J. Hum. Genet. 58, 210–215 (2013).
Google Scholar
Mitsunaga, S. et al. Aggregation of rare/low-frequency variants of the mitochondria respiratory chain-related proteins in rheumatoid arthritis patients. J. Hum. Genet. 60, 449–454 (2015).
Google Scholar
Bang, S. Y. et al. Targeted exon sequencing fails to identify rare coding variants with large effect in rheumatoid arthritis. Arthritis Res. Ther. 16, 447 (2014).
Google Scholar
Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).
Google Scholar
Pang Leong, K. et al. Missense variant in interleukin-6 signal transducer identified as susceptibility locus for rheumatoid arthritis in Chinese patients. Arch. Rheumatol. 36, 603–610 (2021).
Google Scholar
Pernaa, N. et al. Heterozygous premature termination in zinc-finger domain of Krüppel-like factor 2 gene associates with dysregulated immunity. Front. Immunol. 13, 819929 (2022).
Google Scholar
Wang, Y. et al. Germline genetic patterns underlying familial rheumatoid arthritis, systemic lupus erythematosus and primary Sjögren’s syndrome highlight T cell-initiated autoimmunity. Ann. Rheum. Dis. 79, 268–275 (2020).
Google Scholar
Veyssiere, M. et al. A novel nonsense variant in SUPT20H gene associated with rheumatoid arthritis identified by whole exome sequencing of multiplex families. PLoS One 14, e0213387 (2019).
Google Scholar
Okada, Y. et al. Integration of sequence data from a consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk gene. PLoS One 9, e87645 (2014).
Google Scholar
Arnett, F. C. et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 31, 315–324 (1988).
Google Scholar
Aletaha, D. et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 62, 2569–2581 (2010).
Google Scholar
Barbulescu, A. et al. Effectiveness of baricitinib and tofacitinib compared with bDMARDs in RA: results from a cohort study using nationwide Swedish register data. Rheumatology 61, 3952–3962 (2022).
Google Scholar
Hu, X., Li, J., Fu, M., Zhao, X. & Wang, W. The JAK/STAT signaling pathway: from bench to clinic. Signal. Transduct. Target. Ther. 6, 402 (2021).
Google Scholar
Muromoto, R., Oritani, K. & Matsuda, T. Current understanding of the role of tyrosine kinase 2 signaling in immune responses. World J. Biol. Chem. 13, 1–14 (2022).
Google Scholar
López-López, S. et al. NOTCH4 exhibits anti-inflammatory activity in activated macrophages by interfering with interferon-γ and TLR4 signaling. Front. Immunol. 12, 734966 (2021).
Google Scholar
Yang, K. et al. The mammalian SKIV2L RNA exosome is essential for early B cell development. Sci. Immunol. 7, eabn2888 (2022).
Google Scholar
Matsumoto, Y. et al. Tankyrase represses autoinflammation through the attenuation of TLR2 signaling. J. Clin. Invest. 132, e140869 (2022).
Google Scholar
Lapenna, A., Omar, I. & Berger, M. A novel spontaneous mutation in the TAP2 gene unravels its role in macrophage survival. Immunology 150, 432–443 (2017).
Google Scholar
Hoff, N. P., Degrandi, D., Hengge, U., Pfeffer, K. & Wurthner, J. U. Carboxypeptidase D: a novel TGF-β target gene dysregulated in patients with lupus erythematosus. J. Clin. Immunol. 27, 568–579 (2007).
Google Scholar
Wang, S., Wang, S., Li, H., Zhu, L. & Wang, Y. Inhibition of the TGF-β/Smads signaling pathway attenuates pulmonary fibrosis and induces anti-proliferative effect on synovial fibroblasts in rheumatoid arthritis. Int. J. Clin. Exp. Pathol. 12, 1835–1845 (2019).
Google Scholar
Xing, S. et al. Tcf1 and Lef1 are required for the immunosuppressive function of regulatory T cells. J. Exp. Med. 216, 847–866 (2019).
Google Scholar
Wilson, K. R., Villadangos, J. A. & Mintern, J. D. Dendritic cell Flt3 — regulation, roles and repercussions for immunotherapy. Immunol. Cell Biol. 99, 962–971 (2021).
Google Scholar
Fan, H. et al. Plasma TNFSF13B and TNFSF14 function as inflammatory indicators of severe adenovirus pneumonia in pediatric patients. Front. Immunol. 11, 614781 (2020).
Google Scholar
Simpfendorfer, K. R. et al. The autoimmunity-associated BLK haplotype exhibits cis-regulatory effects on mRNA and protein expression that are prominently observed in B cells early in development. Hum. Mol. Genet. 21, 3918–3925 (2012).
Google Scholar
Morris, A. P., Zeggini, E. & Lindgren, C. M. Identification of novel putative rheumatoid arthritis susceptibility genes via analysis of rare variants. BMC Proc. 3, S131 (2009).
Google Scholar
Bowes, J. et al. Rare variation at the TNFAIP3 locus and susceptibility to rheumatoid arthritis. Hum. Genet. 128, 627–633 (2010).
Google Scholar
Franke, L. et al. Association analysis of copy numbers of FC-γ receptor genes for rheumatoid arthritis and other immune-mediated phenotypes. Eur. J. Hum. Genet. 24, 263–270 (2016).
Google Scholar
Fatumo, S. et al. A roadmap to increase diversity in genomic studies. Nat. Med. 28, 243–250 (2022).
Google Scholar
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
Google Scholar
Carress, H., Lawson, D. J. & Elhaik, E. Population genetic considerations for using biobanks as international resources in the pandemic era and beyond. BMC Genomics 22, 351 (2021).
Google Scholar
Peterson, R. E. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179, 589–603 (2019).
Google Scholar
Li, Y. R. & Keating, B. J. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 6, 91 (2014).
Google Scholar
Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).
Google Scholar
Johnson, J. L. Genetic Association Study Power Calculator. University of Michigan http://csg.sph.umich.edu/abecasis/cats/gas_power_calculator/index.html (2017).
Chung, C. P., Rohan, P., Krishnaswami, S. & McPheeters, M. L. A systematic review of validated methods for identifying patients with rheumatoid arthritis using administrative or claims data. Vaccine 31, K41–K61 (2013).
Google Scholar
Klareskog, L. et al. A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 54, 38–46 (2006).
Google Scholar
Xiao, R. & Boehnke, M. Quantifying and correcting for the winner’s curse in genetic association studies. Genet. Epidemiol. 33, 453–462 (2009).
Google Scholar
Zeggini, E. & Ioannidis, J. P. Meta-analysis in genome-wide association studies. Pharmacogenomics 10, 191–201 (2009).
Google Scholar
Hunt, K. A. et al. Negligible impact of rare autoimmune-locus coding-region variants on missing heritability. Nature 498, 232–235 (2013).
Google Scholar
Jiang, S. H. et al. Functional rare and low frequency variants in BLK and BANK1 contribute to human lupus. Nat. Commun. 10, 2201 (2019).
Google Scholar
Bhagwat, M. Searching NCBI’s dbSNP database. Curr. Protoc. Bioinformatics https://doi.org/10.1002/0471250953.bi0119s32 (2010).
Google Scholar
Kocher, J. P. et al. The Biological Reference Repository (BioR): a rapid and flexible system for genomics annotation. Bioinformatics 30, 1920–1922 (2014).
Google Scholar
Münz, M. et al. CSN and CAVA: variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting. Genome Med. 7, 76 (2015).
Google Scholar
Yang, H. & Wang, K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat. Protoc. 10, 1556–1566 (2015).
Google Scholar
Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
Google Scholar
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–d894 (2019).
Google Scholar
de Sainte Agathe, J. M. et al. SpliceAI-visual: a free online tool to improve SpliceAI splicing variant interpretation. Hum. Genomics 17, 7 (2023).
Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. https://doi.org/10.1002/0471142905.hg0720s76 (2013).
Google Scholar
Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9 (2016).
Google Scholar
Wai, H. A. et al. Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance. Genet. Med. 22, 1005–1014 (2020).
Google Scholar
Wiel, L. et al. MetaDome: pathogenicity analysis of genetic variants through aggregation of homologous human protein domains. Hum. Mutat. 40, 1030–1038 (2019).
Google Scholar
Zhou, X. et al. Exploring genomic alteration in pediatric cancer using ProteinPaint. Nat. Genet. 48, 4–6 (2016).
Google Scholar
Chattopadhyay, S. et al. High frequency of hotspot mutations in core genes of Escherichia coli due to short-term positive selection. Proc. Natl Acad. Sci. USA 106, 12412–12417 (2009).
Google Scholar
Ying, P. et al. Genome-wide enhancer-gene regulatory maps link causal variants to target genes underlying human cancer risk. Nat. Commun. 14, 5958 (2023).
Google Scholar
Battle, A., Brown, C. D., Engelhardt, B. E. & Montgomery, S. B. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Google Scholar
GTEx Portal. Bulk tissue gene expression for PTPN22 (ENSG00000134242.15). https://gtexportal.org/home/gene/PTPN22 (2021).
Simmons, D. P. et al. SLAMF7 engagement superactivates macrophages in acute and chronic inflammation. Sci. Immunol. 7, eabf2846 (2022).
Google Scholar
National Institutes of Health. Accelerating Medicines Partnership (AMP). https://www.nih.gov/research-training/accelerating-medicines-partnership-amp#:~:text=Launched%20in%202014%2C%20the%20Accelerating,to%20transform%20the%20current%20model (2022).
The Arthritis and Autoimmune and Related Diseases Portal. Rheumatoid Arthritis Phase II (RA_PhaseII). ARK https://arkportal.synapse.org/Explore/Projects/DetailsPage?Project=RA_PhaseII (2024).
Kronzer, V. L. et al. Timing of sinusitis and other respiratory tract diseases and risk of rheumatoid arthritis. Semin. Arthritis Rheum. 52, 151937 (2022).
Google Scholar
Kronzer, V. L., Crowson, C. S., Sparks, J. A., Vassallo, R. & Davis, J. M. III Investigating asthma, allergic disease, passive smoke exposure, and risk of rheumatoid arthritis. Arthritis Rheumatol. 71, 1217–1224 (2019).
Google Scholar
National Human Genome Research Institute. The Cost of Sequencing a Human Genome. https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost [online] (2021).
Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).
Google Scholar
Wu, C. et al. Transcriptome-wide association study identifies susceptibility genes for rheumatoid arthritis. Arthritis Res. Ther. 23, 38 (2021).
Google Scholar
Asquith, M. et al. HLA alleles associated with risk of ankylosing spondylitis and rheumatoid arthritis influence the gut microbiome. Arthritis Rheumatol. 71, 1642–1650 (2019).
Google Scholar
van Nies, J. A., Tsonaka, R., Gaujoux-Viala, C., Fautrel, B. & van der Helm-van Mil, A. H. Evaluating relationships between symptom duration and persistence of rheumatoid arthritis: does a window of opportunity exist? Results on the Leiden early arthritis clinic and ESPOIR cohorts. Ann. Rheum. Dis. 74, 806–812 (2015).
Google Scholar
Cook, D. et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat. Rev. Drug. Discov. 13, 419–431 (2014).
Google Scholar
Koskinas, K. C. et al. Eligibility for PCSK9 inhibitors based on the 2019 ESC/EAS and 2018 ACC/AHA guidelines. Eur. J. Prev. Cardiol. 28, 59–65 (2021).
Google Scholar
Fleischmann, R. et al. Placebo-controlled trial of tofacitinib monotherapy in rheumatoid arthritis. N. Engl. J. Med. 367, 495–507 (2012).
Google Scholar
Hasni, S. A. et al. Phase 1 double-blind randomized safety trial of the Janus kinase inhibitor tofacitinib in systemic lupus erythematosus. Nat. Commun. 12, 3391 (2021).
Google Scholar
Sandborn, W. J. et al. Tofacitinib as induction and maintenance therapy for ulcerative colitis. N. Engl. J. Med. 376, 1723–1736 (2017).
Google Scholar
