Rheumatoid arthritis (RA) is a genetically complex disease of immune dysregulation characterized by painful inflammation of synovial joints. Despite advances in its management afforded by biologic drug development, efforts to improve outcomes for patients are confounded by the condition's heterogeneous pathobiology, and consequent variability in therapeutic responses. Great strides have been made in understanding the genetic epidemiology of rheumatoid arthritis since its association with the HLA locus was established in the 1980s, with over 100 additional disease-associated variants now confirmed through cumulative genome-wide association studies. Yet translation of this new knowledge for patient benefit – whether as a route to predicting disease risk, drug development or personalized medicine – has been slow. To address this, collaborating teams of interdisciplinary scientists will need to pool resources, including ever larger, well-characterized patient cohorts and sophisticated biostatistical approaches. Recent advances suggest that the fruits of these endeavors are beginning to come within reach.