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

• Main Authors: Haiquan Li (Author), Jungwei Fan (Author), Francesca Vitali (Author), Joanne Berghout (Author), Dillon Aberasturi (Author), Jianrong Li (Author), Liam Wilson (Author), Wesley Chiu (Author), Minsu Pumarejo (Author), Jiali Han (Author), Colleen Kenost (Author), Pradeep C. Koripella (Author), Nima Pouladi (Author), Dean Billheimer (Author), Edward J. Bedrick (Author), Yves A. Lussier (Author)
• Format: Book
• Published: BMC, 2018-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12920-018-0428-9; 1755-8794
• Keywords: Disease comorbidities; GWAS studies; eQTL; Genetic network; Non-coding variants; RNA; Internal medicine; RC31-1245; Genetics; QH426-470
• Source: BMC Medical Genomics, Vol 11, Iss S6, Pp 1-15 (2018)

Abstract Background Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets. Methods In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDReRNA < 0.05). We hypothesized that disease pairs found to be molecularly convergent would also be significantly overrepresented among comorbidities in clinical datasets. To assess our hypothesis, we used clinical claims datasets from the Healthcare Cost and Utilization Project (HCUP) and calculated significant disease comorbidities (FDRcomorbidity < 0.05). We finally verified if disease pairs resulting molecularly convergent were also statistically comorbid more than by chance using the Fisher's Exact Test. Results Our approach integrates: (i) 6175 SNPs associated with 238 diseases from ~ 1000 GWAS, (ii) eQTL associations from 19 tissues, and (iii) claims data for 35 million patients from HCUP. Logistic regression (controlled for age, gender, and race) identified comorbidities in HCUP, while enrichment analyses identified cis- and trans-eQTL downstream effectors of GWAS-identified variants. Among ~ 16,000 combinations of diseases, 398 disease-pairs were prioritized by both convergent eQTL-genetics (RNA overlap enrichment, FDReRNA < 0.05) and clinical comorbidities (OR > 1.5, FDRcomorbidity < 0.05). Case studies of comorbidities illustrate specific convergent noncoding regulatory elements. An intergenic architecture of disease comorbidity was unveiled due to GWAS and eQTL-derived convergent mechanisms between distinct diseases being overrepresented among observed comorbidities in clinical datasets (OR = 8.6, p-value = 6.4 × 10− 5 FET). Conclusions These comorbid diseases with convergent eQTL genetic mechanisms suggest clinical syndromes. While it took over a decade to confirm the genetic underpinning of the metabolic syndrome, this study is likely highlighting hundreds of new ones. Further, this knowledge may improve the clinical management of comorbidities with precision and shed light on novel approaches of drug repositioning or SNP-guided precision molecular therapy inclusive of intergenic risks.