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Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases

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  • Hariklia Eleftherohorinou
  • Victoria Wright
  • Clive Hoggart
  • Anna-Liisa Hartikainen
  • Marjo-Riitta Jarvelin
  • David Balding
  • Lachlan Coin
  • Michael Levin

Abstract

Although the introduction of genome-wide association studies (GWAS) have greatly increased the number of genes associated with common diseases, only a small proportion of the predicted genetic contribution has so far been elucidated. Studying the cumulative variation of polymorphisms in multiple genes acting in functional pathways may provide a complementary approach to the more common single SNP association approach in understanding genetic determinants of common disease. We developed a novel pathway-based method to assess the combined contribution of multiple genetic variants acting within canonical biological pathways and applied it to data from 14,000 UK individuals with 7 common diseases. We tested inflammatory pathways for association with Crohn's disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) with 4 non-inflammatory diseases as controls. Using a variable selection algorithm, we identified variants responsible for the pathway association and evaluated their use for disease prediction using a 10 fold cross-validation framework in order to calculate out-of-sample area under the Receiver Operating Curve (AUC). The generalisability of these predictive models was tested on an independent birth cohort from Northern Finland. Multiple canonical inflammatory pathways showed highly significant associations (p 10−3–10−20) with CD, T1D and RA. Variable selection identified on average a set of 205 SNPs (149 genes) for T1D, 350 SNPs (189 genes) for RA and 493 SNPs (277 genes) for CD. The pattern of polymorphisms at these SNPS were found to be highly predictive of T1D (91% AUC) and RA (85% AUC), and weakly predictive of CD (60% AUC). The predictive ability of the T1D model (without any parameter refitting) had good predictive ability (79% AUC) in the Finnish cohort. Our analysis suggests that genetic contribution to common inflammatory diseases operates through multiple genes interacting in functional pathways.

Suggested Citation

  • Hariklia Eleftherohorinou & Victoria Wright & Clive Hoggart & Anna-Liisa Hartikainen & Marjo-Riitta Jarvelin & David Balding & Lachlan Coin & Michael Levin, 2009. "Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases," PLOS ONE, Public Library of Science, vol. 4(11), pages 1-11, November.
  • Handle: RePEc:plo:pone00:0008068
    DOI: 10.1371/journal.pone.0008068
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    Cited by:

    1. Ohad Manor & Eran Segal, 2013. "Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-10, August.
    2. Ayellet V Segrè & DIAGRAM Consortium & MAGIC investigators & Leif Groop & Vamsi K Mootha & Mark J Daly & David Altshuler, 2010. "Common Inherited Variation in Mitochondrial Genes Is Not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits," PLOS Genetics, Public Library of Science, vol. 6(8), pages 1-19, August.
    3. Yvonne J K Edwards & Gary W Beecham & William K Scott & Sawsan Khuri & Guney Bademci & Demet Tekin & Eden R Martin & Zhijie Jiang & Deborah C Mash & Jarlath ffrench-Mullen & Margaret A Pericak-Vance &, 2011. "Identifying Consensus Disease Pathways in Parkinson's Disease Using an Integrative Systems Biology Approach," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-11, February.
    4. Ren-Hua Chung & Ying-Erh Chen, 2012. "A Two-Stage Random Forest-Based Pathway Analysis Method," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    5. Nerea Bartolomé & Sergi Segarra & Marta Artieda & Olga Francino & Elisenda Sánchez & Magdalena Szczypiorska & Joaquim Casellas & Diego Tejedor & Joaquín Cerdeira & Antonio Martínez & Alfonso Velasco &, 2015. "A Genetic Predictive Model for Canine Hip Dysplasia: Integration of Genome Wide Association Study (GWAS) and Candidate Gene Approaches," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-13, April.
    6. Silver Matt & Montana Giovanni & Alzheimer's Disease Neuroimaging Initiative, 2012. "Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-43, January.
    7. Zhang Tian-Xiao & Beaty Terri H. & Ruczinski Ingo, 2012. "Candidate Pathway Based Analysis for Cleft Lip with or without Cleft Palate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-21, January.

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