van Kempen TS, Wenink MH, Leijten EF, Radstake TR, Boes M. Perception of self: distinguishing autoimmunity from autoinflammation. Nat Rev Rheumatol. 2015;11(8):483–92. https://doi.org/10.1038/nrrheum.2015.60.
CAS
Article
PubMed
Google Scholar
Cho JH, Feldman M. Heterogeneity of autoimmune diseases: pathophysiologic insights from genetics and implications for new therapies. Nature Med. 2015;21(7):730–8. https://doi.org/10.1038/nm.3897.
CAS
Article
PubMed
Google Scholar
Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. 2020;14:1177932219899051.
Article
Google Scholar
Hamburg MA, Collins FS. The path to personalized medicine. N Eng J Med. 2010;363(4):301–4. https://doi.org/10.1056/NEJMp1006304.
CAS
Article
Google Scholar
Marson FAL, Bertuzzo CS, Ribeiro JD. Personalized or precision medicine? The example of cystic fibrosis. Front Pharmacol. 2017;8:390.
Article
Google Scholar
Li P, Zheng Y, Chen X. Drugs for autoimmune inflammatory diseases: from small molecule compounds to anti-TNF biologics. Front Pharmacol. 2017;8:460. https://doi.org/10.3389/fphar.2017.00460.
CAS
Article
PubMed
PubMed Central
Google Scholar
Sepriano A, Kerschbaumer A, Smolen JS, van der Heijde D, Dougados M, van Vollenhoven R, et al. Safety of synthetic and biological DMARDs: a systematic literature review informing the 2019 update of the EULAR recommendations for the management of rheumatoid arthritis. Ann Rheum Dis. 2020;79(6):760–70. https://doi.org/10.1136/annrheumdis-2019-216653.
CAS
Article
PubMed
Google Scholar
Smolen JS, Landewé RBM, Bijlsma JWJ, Burmester GR, Dougados M, Kerschbaumer A, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update. Ann Rheum Dis. 2020;79(6):685–99. https://doi.org/10.1136/annrheumdis-2019-216655.
CAS
Article
PubMed
PubMed Central
Google Scholar
Rein P, Mueller RB. Treatment with biologicals in rheumatoid arthritis: an overview. Rheumatol Ther. 2017;4(2):247–61. https://doi.org/10.1007/s40744-017-0073-3.
Article
PubMed
PubMed Central
Google Scholar
Shi Q, Li KJ, Treuer T, Wang BCM, Gaich CL, Lee CH, et al. Estimating the response and economic burden of rheumatoid arthritis patients treated with biologic disease-modifying antirheumatic drugs in Taiwan using the National Health Insurance Research Database (NHIRD). PLoS One. 2018;13(4):e0193489. https://doi.org/10.1371/journal.pone.0193489.
CAS
Article
PubMed
PubMed Central
Google Scholar
Romão VC, Vital EM, Fonseca JE, Buch MH. Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis? Arthritis Res Ther. 2017;19(1):239. https://doi.org/10.1186/s13075-017-1445-3.
CAS
Article
PubMed
PubMed Central
Google Scholar
Isaacs JD, Cohen SB, Emery P, Tak PP, Wang J, Lei G, et al. Effect of baseline rheumatoid factor and anticitrullinated peptide antibody serotype on rituximab clinical response: a meta-analysis. Ann Rheum Dis. 2013;72(3):329–36. https://doi.org/10.1136/annrheumdis-2011-201117.
CAS
Article
PubMed
Google Scholar
Chatzidionysiou K, Lie E, Nasonov E, Lukina G, Hetland ML, Tarp U, et al. Highest clinical effectiveness of rituximab in autoantibody-positive patients with rheumatoid arthritis and in those for whom no more than one previous TNF antagonist has failed: pooled data from 10 European registries. Ann Rheum Dis. 2011;70(9):1575–80. https://doi.org/10.1136/ard.2010.148759.
CAS
Article
PubMed
Google Scholar
Gottenberg JE, Courvoisier DS, Hernandez MV, Iannone F, Lie E, Canhão H, et al. Brief report: association of rheumatoid factor and anti-citrullinated protein antibody positivity with better effectiveness of abatacept: results from the pan-European registry analysis. Arthritis Rheumatol. 2016;68(6):1346–52. https://doi.org/10.1002/art.39595.
CAS
Article
PubMed
Google Scholar
Maneiro RJ, Salgado E, Carmona L, Gomez-Reino JJ. Rheumatoid factor as predictor of response to abatacept, rituximab and tocilizumab in rheumatoid arthritis: systematic review and meta-analysis. Semin Arthritis Rheum. 2013;43(1):9–17. https://doi.org/10.1016/j.semarthrit.2012.11.007.
CAS
Article
PubMed
Google Scholar
Lv Q, Yin Y, Li X, Shan G, Wu X, Liang D, et al. The status of rheumatoid factor and anti-cyclic citrullinated peptide antibody are not associated with the effect of anti-TNFα agent treatment in patients with rheumatoid arthritis: a meta-analysis. PLoS One. 2014;9(2):e89442. https://doi.org/10.1371/journal.pone.0089442.
CAS
Article
PubMed
PubMed Central
Google Scholar
Sanayama Y, Ikeda K, Saito Y, Kagami S, Yamagata M, Furuta S, et al. Prediction of therapeutic responses to tocilizumab in patients with rheumatoid arthritis: biomarkers identified by analysis of gene expression in peripheral blood mononuclear cells using genome-wide DNA microarray. Arthritis Rheumatol. 2014;66(6):1421–31. https://doi.org/10.1002/art.38400.
CAS
Article
PubMed
Google Scholar
Thurlings RM, Boumans M, Tekstra J, van Roon JA, Vos K, van Westing DM, et al. Relationship between the type I interferon signature and the response to rituximab in rheumatoid arthritis patients. Arthritis Rheum. 2010;62(12):3607–14. https://doi.org/10.1002/art.27702.
CAS
Article
PubMed
Google Scholar
Acosta-Colman I, Palau N, Tornero J, Fernández-Nebro A, Blanco F, González-Alvaro I, et al. GWAS replication study confirms the association of PDE3A-SLCO1C1 with anti-TNF therapy response in rheumatoid arthritis. Pharmacogenomics. 2013;14(7):727–34. https://doi.org/10.2217/pgs.13.60.
CAS
Article
PubMed
Google Scholar
Cui J, Stahl EA, Saevarsdottir S, Miceli C, Diogo D, Trynka G, et al. Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet. 2013;9(3):e1003394. https://doi.org/10.1371/journal.pgen.1003394.
CAS
Article
PubMed
PubMed Central
Google Scholar
Cui J, Saevarsdottir S, Thomson B, Padyukov L, van der Helm-van Mil AH, Nititham J, et al. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy. Arthritis Rheum. 2010;62(7):1849–61. https://doi.org/10.1002/art.27457.
CAS
Article
PubMed
PubMed Central
Google Scholar
Spiliopoulou A, Colombo M, Plant D, Nair N, Cui J, Coenen MJ, et al. Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39. Ann Rheum Dis. 2019;78(8):1055–61. https://doi.org/10.1136/annrheumdis-2018-214877.
CAS
Article
PubMed
Google Scholar
Ferreiro-Iglesias A, Montes A, Perez-Pampin E, Cañete JD, Raya E, Magro-Checa C, et al. Evaluation of 12 GWAS-drawn SNPs as biomarkers of rheumatoid arthritis response to TNF inhibitors. A potential SNP association with response to etanercept. PLoS One. 2019;14(2):e0213073.
CAS
Article
Google Scholar
Sieberts SK, Zhu F, García-García J, Stahl E, Pratap A, Pandey G, et al. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis. Nat Commun. 2016;7(1):12460. https://doi.org/10.1038/ncomms12460.
CAS
Article
PubMed
PubMed Central
Google Scholar
Guan Y, Zhang H, Quang D, Wang Z, Parker SCJ, Pappas DA, et al. Machine learning to predict anti-tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheumatol. 2019;71(12):1987–96. https://doi.org/10.1002/art.41056.
CAS
Article
PubMed
Google Scholar
Nair N, Wilson AG. Can machine learning predict responses to TNF inhibitors? Nat Rev Rheumatol. 2019;15(12):702–4. https://doi.org/10.1038/s41584-019-0320-9.
Article
PubMed
Google Scholar
Ota M, Nagafuchi Y, Hatano H, Ishigaki K, Terao C, Takeshima Y, et al. Dynamic landscape of immune cell-specific gene regulation in immune-mediated diseases. Cell. 2021;184(11):3006–3021.e17. https://doi.org/10.1016/j.cell.2021.03.056.
Nygaard G, Firestein GS. Restoring synovial homeostasis in rheumatoid arthritis by targeting fibroblast-like synoviocytes. Nat Rev Rheumatol. 2020;16(6):316–33. https://doi.org/10.1038/s41584-020-0413-5.
Article
PubMed
PubMed Central
Google Scholar
Tsuchiya H, Ota M, Sumitomo S, Ishigaki K, Suzuki A, Sakata T, et al. Parsing multiomics landscape of activated synovial fibroblasts highlights drug targets linked to genetic risk of rheumatoid arthritis. Ann Rheum Dis. 2020;80(4):440–50. https://doi.org/10.1136/annrheumdis-2020-218189. Epub ahead of print.
CAS
Article
Google Scholar
Lewis MJ, Barnes MR, Blighe K, Goldmann K, Rana S, Hackney JA, et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell Rep. 2019;28(9):2455–2470.e2455.
CAS
Article
Google Scholar
Nerviani A, Di Cicco M, Mahto A, Lliso-Ribera G, Rivellese F, Thorborn G, et al. A pauci-immune synovial pathotype predicts inadequate response to TNFα-blockade in rheumatoid arthritis patients. Front Immunol. 2020;11:845. https://doi.org/10.3389/fimmu.2020.00845.
CAS
Article
PubMed
PubMed Central
Google Scholar
Humby F, Durez P, Buch MH, Lewis MJ, Rizvi H, Rivellese F, et al. Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial. Lancet. 2021;397(10271):305–17. https://doi.org/10.1016/S0140-6736(20)32341-2.
CAS
Article
PubMed
PubMed Central
Google Scholar
Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17(7):392–406. https://doi.org/10.1038/nrg.2016.27.
CAS
Article
PubMed
PubMed Central
Google Scholar
Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24. https://doi.org/10.1038/s41588-018-0183-z.
CAS
Article
PubMed
PubMed Central
Google Scholar
Damask A, Steg PG, Schwartz GG, Szarek M, Hagström E, Badimon L, et al. Patients with high genome-wide polygenic risk scores for coronary artery disease may receive greater clinical benefit from alirocumab treatment in the ODYSSEY OUTCOMES trial. Circulation. 2020;141(8):624–36. https://doi.org/10.1161/CIRCULATIONAHA.119.044434.
Article
PubMed
Google Scholar
Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 2013;9(3):e1003348. https://doi.org/10.1371/journal.pgen.1003348.
CAS
Article
PubMed
PubMed Central
Google Scholar
Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491(7422):119–24. https://doi.org/10.1038/nature11582.
CAS
Article
PubMed
PubMed Central
Google Scholar
Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47(9):979–86. https://doi.org/10.1038/ng.3359.
CAS
Article
PubMed
PubMed Central
Google Scholar
Gettler K, Levantovsky R, Moscati A, Giri M, Wu Y, Hsu NY, et al. Common and rare variant prediction and penetrance of IBD in a large, multi-ethnic, health system-based biobank cohort. Gastroenterol. 2021;160(5):1546–57. https://doi.org/10.1053/j.gastro.2020.12.034.
Tragnone A, Valpiani D, Miglio F, Elmi G, Bazzocchi G, Pipitone E, et al. Dietary habits as risk factors for inflammatory bowel disease. Eur J Gastroenterol Hepatol. 1995;7(1):47–51.
CAS
PubMed
Google Scholar
Principi M, Losurdo G, Iannone A, Contaldo A, Deflorio V, Ranaldo N, et al. Differences in dietary habits between patients with inflammatory bowel disease in clinical remission and a healthy population. Ann Gastroenterol. 2018;31(4):469–73. https://doi.org/10.20524/aog.2018.0273.
Article
PubMed
PubMed Central
Google Scholar
Knevel R, le Cessie S, Terao CC, Slowikowski K, Cui J, Huizinga TWJ, et al. Using genetics to prioritize diagnoses for rheumatology outpatients with inflammatory arthritis. Sci Transl Med. 2020;12(545):eaay1548. https://doi.org/10.1126/scitranslmed.aay1548.
Zhao M, Zhou Y, Zhu B, Wan M, Jiang T, Tan Q, et al. IFI44L promoter methylation as a blood biomarker for systemic lupus erythematosus. Ann Rheum Dis. 2016;75(11):1998–2006. https://doi.org/10.1136/annrheumdis-2015-208410.
CAS
Article
PubMed
Google Scholar
Márquez-Luna C, Loh PR, Price AL. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol. 2017;41(8):811–23. https://doi.org/10.1002/gepi.22083.
Article
PubMed
PubMed Central
Google Scholar
Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91. https://doi.org/10.1038/s41588-019-0379-x.
CAS
Article
PubMed
PubMed Central
Google Scholar
Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet. 2020;52(12):1346–54. https://doi.org/10.1038/s41588-020-00740-8.
CAS
Article
PubMed
PubMed Central
Google Scholar
Cavazos TB, Witte JS. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2021;2(1):100017. https://doi.org/10.1016/j.xhgg.2020.100017.
Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019;10(1):3328. https://doi.org/10.1038/s41467-019-11112-0.
CAS
Article
PubMed
PubMed Central
Google Scholar
ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020;583(7818):699–710.
Article
Google Scholar
Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–30.
Article
Google Scholar
Lappalainen T, Sammeth M, Friedländer MR, t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501(7468):506–11. https://doi.org/10.1038/nature12531.
CAS
Article
PubMed
PubMed Central
Google Scholar
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369(6509):1318–30. https://doi.org/10.1126/science.aaz1776.
CAS
Article
Google Scholar
Schmiedel BJ, Singh D, Madrigal A, Valdovino-Gonzalez AG, White BM, Zapardiel-Gonzalo J, et al. Impact of genetic polymorphisms on human immune cell gene expression. Cell. 2018;175(6):1701–1715.e1716.
CAS
Article
Google Scholar
Võsa U, Claringbould A, Westra H-J, Bonder MJ, Deelen P, Zeng B, et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. bioRxiv. 2018:447367. https://doi.org/10.1101/447367.
Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245–52. https://doi.org/10.1038/ng.3506.
CAS
Article
PubMed
PubMed Central
Google Scholar
Mancuso N, Shi H, Goddard P, Kichaev G, Gusev A, Pasaniuc B. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am J Hum Genet. 2017;100(3):473–87. https://doi.org/10.1016/j.ajhg.2017.01.031.
CAS
Article
PubMed
PubMed Central
Google Scholar
Ishigaki K, Kochi Y, Suzuki A, Tsuchida Y, Tsuchiya H, Sumitomo S, et al. Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis. Nat Genet. 2017;49(7):1120–5. https://doi.org/10.1038/ng.3885.
CAS
Article
PubMed
Google Scholar
Marigorta UM, Denson LA, Hyams JS, Mondal K, Prince J, Walters TD, et al. Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn’s disease. Nat Genet. 2017;49(10):1517–21. https://doi.org/10.1038/ng.3936.
CAS
Article
PubMed
PubMed Central
Google Scholar
Lee JC, Biasci D, Roberts R, Gearry RB, Mansfield JC, Ahmad T, et al. Genome-wide association study identifies distinct genetic contributions to prognosis and susceptibility in Crohn’s disease. Nat Genet. 2017;49(2):262–8. https://doi.org/10.1038/ng.3755.
CAS
Article
PubMed
PubMed Central
Google Scholar