Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases

Younghee Lee, Haiquan Li, Jianrong Li, Ellen Rebman, Ikbel Achour, Kelly E. Regan, Eric R. Gamazon, James L. Chen, Xinan Holly Yang, Nancy J. Cox, Yves A Lussier

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Background: While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. Methods: We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Results: Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10-12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Conclusion: Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.

Original languageEnglish (US)
Pages (from-to)619-629
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume20
Issue number4
DOIs
StatePublished - 2013
Externally publishedYes

Fingerprint

Genome-Wide Association Study
Type 2 Diabetes Mellitus
Genes
Proteins
National Human Genome Research Institute (U.S.)
Inborn Genetic Diseases
Finland
Genomics
Single Nucleotide Polymorphism

ASJC Scopus subject areas

  • Health Informatics

Cite this

Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. / Lee, Younghee; Li, Haiquan; Li, Jianrong; Rebman, Ellen; Achour, Ikbel; Regan, Kelly E.; Gamazon, Eric R.; Chen, James L.; Yang, Xinan Holly; Cox, Nancy J.; Lussier, Yves A.

In: Journal of the American Medical Informatics Association, Vol. 20, No. 4, 2013, p. 619-629.

Research output: Contribution to journalArticle

Lee, Younghee ; Li, Haiquan ; Li, Jianrong ; Rebman, Ellen ; Achour, Ikbel ; Regan, Kelly E. ; Gamazon, Eric R. ; Chen, James L. ; Yang, Xinan Holly ; Cox, Nancy J. ; Lussier, Yves A. / Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases. In: Journal of the American Medical Informatics Association. 2013 ; Vol. 20, No. 4. pp. 619-629.
@article{50970df586e14f888df64462f65e19d8,
title = "Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases",
abstract = "Background: While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. Methods: We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Results: Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10-12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Conclusion: Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.",
author = "Younghee Lee and Haiquan Li and Jianrong Li and Ellen Rebman and Ikbel Achour and Regan, {Kelly E.} and Gamazon, {Eric R.} and Chen, {James L.} and Yang, {Xinan Holly} and Cox, {Nancy J.} and Lussier, {Yves A}",
year = "2013",
doi = "10.1136/amiajnl-2012-001519",
language = "English (US)",
volume = "20",
pages = "619--629",
journal = "Journal of the American Medical Informatics Association : JAMIA",
issn = "1067-5027",
publisher = "Oxford University Press",
number = "4",

}

TY - JOUR

T1 - Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases

AU - Lee, Younghee

AU - Li, Haiquan

AU - Li, Jianrong

AU - Rebman, Ellen

AU - Achour, Ikbel

AU - Regan, Kelly E.

AU - Gamazon, Eric R.

AU - Chen, James L.

AU - Yang, Xinan Holly

AU - Cox, Nancy J.

AU - Lussier, Yves A

PY - 2013

Y1 - 2013

N2 - Background: While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. Methods: We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Results: Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10-12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Conclusion: Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.

AB - Background: While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. Methods: We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. Results: Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p<2.24×10-12). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). Conclusion: Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.

UR - http://www.scopus.com/inward/record.url?scp=84882796945&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84882796945&partnerID=8YFLogxK

U2 - 10.1136/amiajnl-2012-001519

DO - 10.1136/amiajnl-2012-001519

M3 - Article

C2 - 23355459

AN - SCOPUS:84882796945

VL - 20

SP - 619

EP - 629

JO - Journal of the American Medical Informatics Association : JAMIA

JF - Journal of the American Medical Informatics Association : JAMIA

SN - 1067-5027

IS - 4

ER -