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Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies

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  • Stephanie N Lewis
  • Elaine Nsoesie
  • Charles Weeks
  • Dan Qiao
  • Liqing Zhang

Abstract

Background: Genome wide association studies (GWAS) have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP). The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases. Methodology/Principal Findings: Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD) and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs) were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits. Conclusions/Significance: The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.

Suggested Citation

  • Stephanie N Lewis & Elaine Nsoesie & Charles Weeks & Dan Qiao & Liqing Zhang, 2011. "Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-10, November.
  • Handle: RePEc:plo:pone00:0027175
    DOI: 10.1371/journal.pone.0027175
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    1. Hiroshi Furukawa & Shomi Oka & Kota Shimada & Shoji Sugii & Jun Ohashi & Toshihiro Matsui & Tatsuoh Ikenaka & Hisanori Nakayama & Atsushi Hashimoto & Hirokazu Takaoka & Yoshiyuki Arinuma & Yuko Okazak, 2012. "Association of Human Leukocyte Antigen with Interstitial Lung Disease in Rheumatoid Arthritis: A Protective Role for Shared Epitope," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-7, May.
    2. Shomi Oka & Hiroshi Furukawa & Aya Kawasaki & Kota Shimada & Shoji Sugii & Atsushi Hashimoto & Akiko Komiya & Naoshi Fukui & Satoshi Ito & Tadashi Nakamura & Koichiro Saisho & Masao Katayama & Shinich, 2014. "Protective Effect of the HLA-DRB1*13:02 Allele in Japanese Rheumatoid Arthritis Patients," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    3. Hiroshi Furukawa & Shomi Oka & Kota Shimada & Shoji Sugii & Atsushi Hashimoto & Akiko Komiya & Naoshi Fukui & Tatsuo Nagai & Shunsei Hirohata & Keigo Setoguchi & Akira Okamoto & Noriyuki Chiba & Eiich, 2013. "Association of Increased Frequencies of HLA-DPB1*05∶01 with the Presence of Anti-Ro/SS-A and Anti-La/SS-B Antibodies in Japanese Rheumatoid Arthritis and Systemic Lupus Erythematosus Patients," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    4. Hiroshi Furukawa & Shomi Oka & Kota Shimada & Atsushi Hashimoto & Akiko Komiya & Shinichiro Tsunoda & Akiko Suda & Satoshi Ito & Koichiro Saisho & Masao Katayama & Satoshi Shinohara & Takeo Sato & Kat, 2018. "Independent association of HLA-DPB1*02:01 with rheumatoid arthritis in Japanese populations," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-12, September.

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