Crowdsourcing genetic prediction of clinical utility in the Rheumatoid Arthritis Responder Challenge

• Published in: Nature genetics Vol. 45; no. 5; pp. 468 - 469
• Main Authors: Plenge, Robert M, Greenberg, Jeffrey D, Mangravite, Lara M, Derry, Jonathan M J, Stahl, Eli A, Coenen, Marieke J H, Barton, Anne, Padyukov, Leonid, Klareskog, Lars, Gregersen, Peter K, Mariette, Xavier, Moreland, Larry W, Bridges, Jr, S Louis, de Vries, Niek, Huizinga, Tom W J, Guchelaar, Henk-Jan, Friend, Stephen H, Stolovitzky, Gustavo
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.05.2013
• Subjects: Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - genetics; Arthritis, Rheumatoid - therapy; Biomedical research; Care and treatment; Crowdsourcing; Crowdsourcing - statistics & numerical data; Drug therapy; Genetic aspects; Genetics; Humans; Medicin och hälsovetenskap; Methods; Models, Genetic; Peer review; Physiological aspects; Predictive Value of Tests; R&D; Research & development; Reverse engineering; Rheumatoid arthritis; Rheumatoid factor; Risk factors; United States
• ISSN: 1061-4036; 1546-1718; 1546-1718
• DOI: 10.1038/ng.2623

Disease-modifying antirheumatic drugs such as those that block the inflammatory cytokine tumor necrosis factor-a (known as anti-TNF therapy) are not effective in all patients with RA, with up to one-third of such patients failing to enter clinical remission after a standard course of therapy5. [...]the biological mechanisms underlying this failure are unknown, limiting the development of clinical biomarkers to guide either this therapy or the development of new drugs to target refractory cases. [...]we believe that the Rheumatoid Arthritis Responder Challenge is an apt use of crowdsourcing in human genetics to gain insight into clinical prediction and disease biology.