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

Haiquan Li, Jung Wei Fan, Francesca Vitali, Joanne Berghout, Dillon Aberasturi, Jianrong Li, Liam Wilson, Wesley Chiu, Minsu Pumarejo, Jiali Han, Colleen Kenost, Pradeep C. Koripella, Nima Pouladi, Dean Billheimer, Edward J. Bedrick, Yves A. Lussier

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Article number112
JournalBMC Medical Genomics
Volume11
DOIs
StatePublished - Dec 31 2018
Externally publishedYes

Fingerprint

Comorbidity
Quantitative Trait Loci
Single Nucleotide Polymorphism
Genome-Wide Association Study
Drug Repositioning
Intergenic DNA
Health Care Costs
Mortality

Keywords

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

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities. / Li, Haiquan; Fan, Jung Wei; Vitali, Francesca; Berghout, Joanne; Aberasturi, Dillon; Li, Jianrong; Wilson, Liam; Chiu, Wesley; Pumarejo, Minsu; Han, Jiali; Kenost, Colleen; Koripella, Pradeep C.; Pouladi, Nima; Billheimer, Dean; Bedrick, Edward J.; Lussier, Yves A.

In: BMC Medical Genomics, Vol. 11, 112, 31.12.2018.

Research output: Contribution to journalArticle

Li, H, Fan, JW, Vitali, F, Berghout, J, Aberasturi, D, Li, J, Wilson, L, Chiu, W, Pumarejo, M, Han, J, Kenost, C, Koripella, PC, Pouladi, N, Billheimer, D, Bedrick, EJ & Lussier, YA 2018, 'Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities', BMC Medical Genomics, vol. 11, 112. https://doi.org/10.1186/s12920-018-0428-9
Li, Haiquan ; Fan, Jung Wei ; Vitali, Francesca ; Berghout, Joanne ; Aberasturi, Dillon ; Li, Jianrong ; Wilson, Liam ; Chiu, Wesley ; Pumarejo, Minsu ; Han, Jiali ; Kenost, Colleen ; Koripella, Pradeep C. ; Pouladi, Nima ; Billheimer, Dean ; Bedrick, Edward J. ; Lussier, Yves A. / Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities. In: BMC Medical Genomics. 2018 ; Vol. 11.
@article{dd8f5ab071964a26a9650bb3a3e6d6b1,
title = "Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities",
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, eQTL, Genetic network, GWAS studies, Intergenic, Non-coding variants, RNA, SNP",
author = "Haiquan Li and Fan, {Jung Wei} and Francesca Vitali and Joanne Berghout and Dillon Aberasturi and Jianrong Li and Liam Wilson and Wesley Chiu and Minsu Pumarejo and Jiali Han and Colleen Kenost and Koripella, {Pradeep C.} and Nima Pouladi and Dean Billheimer and Bedrick, {Edward J.} and Lussier, {Yves A.}",
year = "2018",
month = "12",
day = "31",
doi = "10.1186/s12920-018-0428-9",
language = "English (US)",
volume = "11",
journal = "BMC Medical Genomics",
issn = "1755-8794",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities

AU - Li, Haiquan

AU - Fan, Jung Wei

AU - Vitali, Francesca

AU - Berghout, Joanne

AU - Aberasturi, Dillon

AU - Li, Jianrong

AU - Wilson, Liam

AU - Chiu, Wesley

AU - Pumarejo, Minsu

AU - Han, Jiali

AU - Kenost, Colleen

AU - Koripella, Pradeep C.

AU - Pouladi, Nima

AU - Billheimer, Dean

AU - Bedrick, Edward J.

AU - Lussier, Yves A.

PY - 2018/12/31

Y1 - 2018/12/31

N2 - 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.

AB - 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.

KW - Common diseases

KW - Complex diseases

KW - Disease comorbidities

KW - Diseases

KW - eQTL

KW - Genetic network

KW - GWAS studies

KW - Intergenic

KW - Non-coding variants

KW - RNA

KW - SNP

UR - http://www.scopus.com/inward/record.url?scp=85059267726&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85059267726&partnerID=8YFLogxK

U2 - 10.1186/s12920-018-0428-9

DO - 10.1186/s12920-018-0428-9

M3 - Article

C2 - 30598089

AN - SCOPUS:85059267726

VL - 11

JO - BMC Medical Genomics

JF - BMC Medical Genomics

SN - 1755-8794

M1 - 112

ER -