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Fig. 3 | BMC Medical Genomics

Fig. 3

From: Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities

Fig. 3

Network of disease-pairs prioritized as comorbid and sharing convergent genetic mechanisms through cis- and trans-eQTL associations of their coding and intergenic polymorphisms. Convergent molecular mechanisms were confirmed at FDR < 0.05 (Methods- Calculation of disease comorbidity based on HCUP). Disease comorbidities were confirmed in either clinical datasets NIS13 or NEDS13 at FDR < 0.05 (Panel a with OR > 3; panel b with OR > 1.5; Methods- Statistical overlap of eQTL-associated RNAs between distinct disease-associated SNPs). Known clinical syndromes with common genetic risks are recapitulated (e.g., metabolic syndrome), as well as less known monogenic diseases modulated with SNPs unrelated to their monogenic cause (e.g., SNPs worsening cystic fibrosis associated by eQTL studies to those of the metabolic syndrome for which the comorbidity is known but not the underpinning biological mechanisms). Many eQTL mechanisms relate known co-classified diseases (e.g., cancers, immune-mediated diseases), however many cross classes provide intriguing novel comorbidities linked by genetics that had eluded discovery by both clinicians and geneticists (e.g., Parkinson’s disease and Allergic Dermatitis). In most cases, though, the comorbidity was known and explained to clinicians by non-genetic pathophysiology (e.g., duodenal cancer and pancreatic cancer), and yet this study implies that previously undiscovered genetic mechanisms further amplify these comorbid conditions in predisposed individuals. Legend. Edge widths are proportional to the number of tissues that yielded eQTL RNA associations with SNPs by eQTL analyses (19 tissues, eQTL RNA and SNPs not shown; details in Fig. 5 for two examples). Diseases classifications are color-colored (e.g., autoimmune disorders in blue)

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