Discovering genetic interactions bridging pathways in genome-wide association studies

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing metho...

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Bibliographic Details
Published in:Nature communications Vol. 10; no. 1; pp. 4274 - 18
Main Authors: Fang, Gang, Wang, Wen, Paunic, Vanja, Heydari, Hamed, Costanzo, Michael, Liu, Xiaoye, Liu, Xiaotong, VanderSluis, Benjamin, Oately, Benjamin, Steinbach, Michael, Van Ness, Brian, Schadt, Eric E., Pankratz, Nathan D., Boone, Charles, Kumar, Vipin, Myers, Chad L.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 19.09.2019
Nature Publishing Group
Nature Portfolio
Subjects:
45/43
631/114
631/208/205
631/208/2490
631/553/2490
Breast cancer
Breast Neoplasms - genetics
Computer applications
Data processing
Diabetes mellitus
Diabetes mellitus (non-insulin dependent)
Diabetes Mellitus, Type 2 - genetics
Disease
Female
Gene mapping
Gene Regulatory Networks - genetics
Genes
Genetic Predisposition to Disease - genetics
Genome-wide association studies
Genome-Wide Association Study - statistics & numerical data
Genomes
Genotype & phenotype
Genotypes
Humanities and Social Sciences
Humans
Hypertension
Hypertension - genetics
Identification methods
Male
Mapping
Mental disorders
Methods
Models, Genetic
Movement disorders
multidisciplinary
Mutation
Neurodegenerative diseases
Parkinson's disease
Parkinsonian Disorders - genetics
Phenotypes
Polymorphism, Single Nucleotide - genetics
Prostate cancer
Prostatic Neoplasms - genetics
Proteins
Schizophrenia
Schizophrenia - genetics
Science
Science (multidisciplinary)
Statistical power
Zebrafish
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary:Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data. Genetic interactions may contribute to phenotypic traits but are challenging to decipher. Here, the authors develop BridGE, a computational approach for identifying pathways connected by genetic interactions from GWAS data.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-12131-7