title: A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis.
abstract: Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: "leukocyte activation and differentiation", "pattern-recognition receptor signaling pathway", and "chemokines and their receptors".These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate biological pathways.
authors: Division of Human Genetics, Department of Integrated Genetics, National Institute of Genetics, Mishima, Shizuoka, Japan.
authors: Hirofumi
authors: H
authors: Nakaoka
authors: Tailin
authors: T
authors: Cui
authors: Atsushi
authors: A
authors: Tajima
authors: Akira
authors: A
authors: Oka
authors: Shigeki
authors: S
authors: Mitsunaga
authors: Koichi
authors: K
authors: Kashiwase
authors: Yasuhiko
authors: Y
authors: Homma
authors: Shinji
authors: S
authors: Sato
authors: Yasuo
authors: Y
authors: Suzuki
authors: Hidetoshi
authors: H
authors: Inoko
authors: Ituro
authors: I
authors: Inoue
mesh_terms: D000818:Animals; D001172:Arthritis, Rheumatoid / Q000235:genetics* / Q000276:immunology / Q000378:metabolism*; D053263:Gene Regulatory Networks / Q000235:genetics; D056426:Genetic Loci / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005821:Genetic Techniques; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D060066:Protein Interaction Maps; D049490:Systems Biology / Q000379:methods*