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A Systems Genetics Approach Provides a Bridge from Discovered Genetic Variants to Biological Pathways in Rheumatoid Arthritis

Author

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

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.

Suggested Citation

  • Hirofumi Nakaoka & Tailin Cui & Atsushi Tajima & Akira Oka & Shigeki Mitsunaga & Koichi Kashiwase & Yasuhiko Homma & Shinji Sato & Yasuo Suzuki & Hidetoshi Inoko & Ituro Inoue, 2011. "A Systems Genetics Approach Provides a Bridge from Discovered Genetic Variants to Biological Pathways in Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-16, September.
  • Handle: RePEc:plo:pone00:0025389
    DOI: 10.1371/journal.pone.0025389
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    1. Noriko Sakaguchi & Takeshi Takahashi & Hiroshi Hata & Takashi Nomura & Tomoyuki Tagami & Sayuri Yamazaki & Toshiko Sakihama & Takaji Matsutani & Izumi Negishi & Syuichi Nakatsuru & Shimon Sakaguchi, 2003. "Altered thymic T-cell selection due to a mutation of the ZAP-70 gene causes autoimmune arthritis in mice," Nature, Nature, vol. 426(6965), pages 454-460, November.
    2. Kristin C. Gunsalus & Hui Ge & Aaron J. Schetter & Debra S. Goldberg & Jing-Dong J. Han & Tong Hao & Gabriel F. Berriz & Nicolas Bertin & Jerry Huang & Ling-Shiang Chuang & Ning Li & Ramamurthy Mani &, 2005. "Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis," Nature, Nature, vol. 436(7052), pages 861-865, August.
    3. Alexandra Zhernakova & Eli A Stahl & Gosia Trynka & Soumya Raychaudhuri & Eleanora A Festen & Lude Franke & Harm-Jan Westra & Rudolf S N Fehrmann & Fina A S Kurreeman & Brian Thomson & Namrata Gupta &, 2011. "Meta-Analysis of Genome-Wide Association Studies in Celiac Disease and Rheumatoid Arthritis Identifies Fourteen Non-HLA Shared Loci," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-13, February.
    4. A Cecile J W Janssens & John P A Ioannidis & Cornelia M van Duijn & Julian Little & Muin J Khoury & for the GRIPS Group, 2011. "Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS Statement," PLOS Medicine, Public Library of Science, vol. 8(3), pages 1-4, March.
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    1. Song-Yao Zhang & Shao-Wu Zhang & Lian Liu & Jia Meng & Yufei Huang, 2016. "m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-31, December.

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