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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/19956648
Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases - PubMed Skip to main page content
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. 2009 Nov 30;4(11):e8068.
doi: 10.1371/journal.pone.0008068.

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

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

Hariklia Eleftherohorinou et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Inflammatory response to a pathogen.
Pathogen recognised by pattern recognition receptors on phagocytic cell (A) or plasma opsonins (I). Signal induction (B) and first order inflammatory genes (C) are induced leading to release of inflammatory signals. These bind to receptors (D), leading to activation of signal transduction pathways and gene induction of second order inflammatory mediators (E, F). These act as effectors of the inflammatory response (Red Arrow) or as positive or negative regulators. Inflammation upregulates cell adhesion molecules (J) and those involved in transendothelial migration (K). Genetic variants (A–J) will interact to alter the intensity and nature of the response, and may determine different outcomes. Individuals making an excessive inflammatory response may succumb to overwhelming inflammation, while those making an inadequate response may fail to clear the pathogen. EC = endothelial cell.
Figure 2
Figure 2. Genes identified by variable selection in all 10 folds of cross-validation for T1D, RA, CD.
Key genes that showed consistent association with each disease had at least one mapping SNP selected in all 10 logistic models of CV. Genes are grouped in their pathways, which are shown as bubbles. Pathways are colour-coded in agreement with Table 1 and Supplementary Tables. Overlapping bubbles represent pathways that share key genes. Underlined genes correspond to associations that have been reported in previous association studies.
Figure 3
Figure 3. ROC curves showing the average predictive performance for T1D, RA and CD.
True positive rate and false positive rate for predicting case/control status for A) type 1 diabetes, B) rheumatoid arthritis, C) Crohn's disease on the WTCCC dataset and D) ROC showing the average predictive performance of the T1D models built on the UK WTCCC dataset and applied to the 4,763 subjects in the Northern Finland 1966 Birth Cohort. Each colored line is the average ROC of the 10 models fitted during CV. The green curves show the performance of the models, as built by the variable selection algorithm. Blue curves show the performance of the same models with all significant hits (individual trend test P<5×10−7) and SNPs in LD (formula image) removed. Red curves show the predictive performance of the models formed only by the previously excluded SNPs (significant hits and SNPs in LD). In T1D (A) the area under the average ROC curves is 91%, 71% and 84%, in RA (B) it is 85%, 81%, 70% and in CD (C) 60%, 56%, 58% for the pathway-derived models (green-curves), the pathway-derived models excluding the significant hits (blue curves) and the significant-hit models (red-curves) respectively. In (D) the AUC of the green, blue and red ROC is 0.79, 0.71 and 0.76 respectively.
Figure 4
Figure 4. Variant SNPs carried by cases with type 1 diabetes and controls.
(a) Predicted probability of being a case, (b) Actual case or control status. Patients shown in red and controls in green. The model correctly assigns the majority of cases at the extreme left, and controls at extreme right, with less predictive ability in the middle. (c) Individual patients or controls are displayed in columns and each row represents one SNP. Red indicates an adverse and green a protective SNP. Intensity of colour indicates disease log-odds from the predictive model. The magnified sections show regions where very marked differences between cases and controls can be readily seen.

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