Analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases

• Published in: PLoS ONE
• Main Authors: Eleftherohorinou, H, Wright, V, Hoggart, C, Hartikainen, AL, Jarvelin, MR, Coin, LJ, Levin, M
• Other Authors: Medical Research Council (MRC)
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Public Library of Science (PLoS) 2009
• Subjects: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; GENOME-WIDE ASSOCIATION; NF-KAPPA-B; TYPE-1 DIABETES RISK; RHEUMATOID-ARTHRITIS; AUTOIMMUNE-DISEASE; PROMOTER REGION; LOCI; PREDICTION; POLYMORPHISMS; PATHOGENESIS; Algorithms; Area Under Curve; Cohort Studies; Diabetes Mellitus; Type 1; Finland; Genetic Predisposition to Disease; Genetic Variation; Genome; Genome-Wide Association Study; Humans; Inflammation; Polymorphism; Single Nucleotide; ROC Curve; Risk; General Science & Technology; Northern Finland
• Online Access: http://hdl.handle.net/10044/1/85529; https://doi.org/10.1371/journal.pone.0008068

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.