Abstract
BackgroundForty-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.MethodsIn 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.ResultsOur 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).ConclusionsThese 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.
Highlights
Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs
Comparative studies between expression quantitative trait loci (eQTL) and Healthcare Cost and Utilization Project (HCUP) To verify the hypothesis that diseases sharing convergent downstream are more likely to show comorbidities and that an association exists between disease comorbidity and genetic/genomic architectures, we investigated the concordance between disease pairs showing significant downstream eQTL convergence in genome-wide association studies (GWAS) (Methods- Statistical overlap of eQTL-associated RNAs between distinct disease-associated Single nucleotide polymorphism (SNP)) and the pairs of diseases resulted prioritized as comorbid using the HCUP clinical data (Methods- Calculation of disease comorbidity based on HCUP)
Disease pairs with convergent eQTL-mechanisms of genetic polymorphisms The eQTL RNA overlap model allowed for the identification of shared RNAs associated with eQTL SNPs that were significantly associated with two distinct diseases (FETeQTL)
Summary
Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Very few studies have validated their predictions of comorbid syndromes (a group of medical conditions consistently occurring together) by observation in clinical datasets [7] These few computational biology studies have shown to correlate with clinical comorbidity: (i) the presence of shared gene expression and flux coupling in metabolic pathways of disease-causing genes [8], (ii) the overlap of disease-associated host genes of polymorphisms and their interacting proteins or functional annotations [9, 10], (iii) the comorbidity of diseases sharing Mendelian genetics [11], (iv) the overrepresentation of Mendelian disease genes in differentially expressed genes of cancers [12], and (v) the genetic or phenotypic network proximity observed in databases of complex and Mendelian genetics [13]. Despite this abundance of noncoding genetic signals, the role of these intergenic polymorphisms in the pathology of clinical comorbidities remains insufficiently characterized [15, 16]
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