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BMC Med Genomics. 2018 Dec 31;11(Suppl 6):112. doi: 10.1186/s12920-018-0428-9.

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

Li H1,2,3,4, Fan J5,6, Vitali F5,6,7, Berghout J5,6,8,9, Aberasturi D5,6,10, Li J5,6,7, Wilson L5, Chiu W5, Pumarejo M5, Han J5,6,11, Kenost C5,6, Koripella PC5,6, Pouladi N5,6, Billheimer D5,10,7,12, Bedrick EJ5,10,7,12, Lussier YA13,14,15,16,17,18,19.

Author information

1
Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, 85721, USA. haiquan@email.arizona.edu.
2
Department of Medicine at the College of Medicine-Tucson, The University of Arizona, Tucson, AZ, 85721, USA. haiquan@email.arizona.edu.
3
Graduate Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, 85721, USA. haiquan@email.arizona.edu.
4
Department of Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721, USA. haiquan@email.arizona.edu.
5
Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, 85721, USA.
6
Department of Medicine at the College of Medicine-Tucson, The University of Arizona, Tucson, AZ, 85721, USA.
7
University of Arizona Health Sciences, The University of Arizona, Tucson, AZ, 85721, USA.
8
The Center for Applied Genetics and Genomics Medicine, The University of Arizona, Tucson, AZ, 85721, USA.
9
The Center for Innovation in Brain Science, The University of Arizona, Tucson, AZ, 85721, USA.
10
Graduate Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, 85721, USA.
11
Department of Systems & Industrial Engineering, The University of Arizona, Tucson, AZ, 85721, USA.
12
Epidemiology and Biostatistics Department, College of Public Health, The University of Arizona, Tucson, AZ, 85721, USA.
13
Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
14
Department of Medicine at the College of Medicine-Tucson, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
15
Graduate Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
16
The Center for Applied Genetics and Genomics Medicine, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
17
The Center for Innovation in Brain Science, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
18
UA Cancer Center, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.
19
University of Arizona Health Sciences, The University of Arizona, Tucson, AZ, 85721, USA. yves@email.arizona.edu.

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.

KEYWORDS:

Common diseases; Complex diseases; Disease comorbidities; Diseases; GWAS studies; Genetic network; Intergenic; Non-coding variants; RNA; SNP; eQTL

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