A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis.

Hirofumi Nakaoka,Shigeki Mitsunaga,Shinji Sato,Ituro Inoue,Koichi Kashiwase,Hidetoshi Inoko,Atsushi Tajima,Yasuo Suzuki,Akira Oka,Tailin Cui,Yasuhiko Homma,Amr H Sawalha

doi:10.1371/journal.pone.0025389

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.

Highlights

Genome-wide association studies (GWAS) have identified a large number of novel genetic loci underlying susceptibility to common diseases [1], which leads to an interest in how these discoveries may be translated into improvement in health care and public health
Electronic database searches We sought published meta-analyses that had evaluated the association between genetic variants and rheumatoid arthritis (RA) risk in populationbased studies through two electronic databases: PubMed and NHGRI GWAS catalog
CORs and 95% CIs were calculated by meta-analyses of published studies: HLA-DRB1 from [62]; CD40, SLC22A4, STAT4, CTLA4, TRAF1, TNFAIP3, and IRF5 from re-analysis of meta-analyses shown in Table S4; PADI4 and FCRL3 from re-analysis of ethnicity-specific meta-analyses shown in Table S6; and CCR6, BLK, C5orf30, AFF3, SPRED2, and

Summary

Introduction

Genome-wide association studies (GWAS) have identified a large number of novel genetic loci underlying susceptibility to common diseases [1], which leads to an interest in how these discoveries may be translated into improvement in health care and public health. Most of the genetic variants used by direct-to-consumer genetic testing to predict an individual’s risk to common diseases have been shown to lack consistent evidence of gene-disease associations [15]. Systematic validation and characterization of the evidence of genetic associations at both discovery and translational phases of human genomics are required [18,19] In these circumstances, meta-analysis can be a useful tool to improve the estimation of effect sizes of genetic variants by combining results from individual studies, thereby making it possible to evaluate variants for model inclusion in a rigorous way [20]. Genetic factors may contribute to the phenotypic diversity in RA [26]

Results

Discussion

Materials and Methods