Cindy Boer

Genetics of Osteoarthritis Consortium GWAS Meta-Analyses | 161 4.1 Introduction Osteoarthritis is one of the leading causes of disability and pain worldwide, with over 300 million people affected[1]. As one of the most rapidly-rising conditions globally, the societal burden of osteoarthritis is enormous[1, 2], and is accompanied by substantial multimorbidity[3]. Currently no curative treatments are available, and management strategies focus on symptom alleviation through pain relief and joint replacement sur- gery. A detailed understanding of disease aetiopathology and novel drug targets are therefore urgently needed and eagerly anticipated. Osteoarthritis is a complex degenerative disease of the whole joint, characterised by cartilage degeneration, subchondral bone thickening, osteophyte formation, synovi- al inflammation and structural alterations the joint capsule, ligaments and associated muscles[4]. Risk for developing osteoarthritis consists of a complex interplay between environmental factors and underlying genetic variation[5]. Recently, major advances were made in elucidating the genetic background of osteoarthritis, using genome-wide association studies (GWAS)[6-8], with 96 risk single nucleotide variants (SNVs) estab- lished to date. However, these SNVs only explain a small proportion of the phenotypic variance[8] and are mainly associated with osteoarthritis affecting the knee and hip joints. Osteoarthritis can affect every synovial joint and few general osteoarthritis SNVs have been identified to date. An increase in body mass index (BMI) is associated with risk of disease. A better understanding of the genetic differences between weight-bear- ing (knee, hip and spine) and non-weight-bearing joints (hand, finger, thumb) is need- ed to help disentangle the metabolic and biomechanical effects in osteoarthritis. Here, we conducted a GWAS meta-analysis across knee, hip, finger, thumb and spine osteo- arthritis phenotypes in 826,690 individuals of European and East Asian descent. We integrated functional genomics analyses from disease-relevant tissue, including gene expression and protein abundance, coupled to mouse knockout phenotyping data and complementary computational fine-mapping, colocalization and causal inference ap- proaches, to identify likely effector genes and facilitate much-needed translation into therapies by enhancing our understanding of disease aetiopathology.