Discovering genetic interactions bridging pathways in genome-wide association studies
| Autor: | Wen Wang, Chad L. Myers, Nathan Pankratz, Michael Steinbach, Gang Fang, Michael Costanzo, Vipin Kumar, Xiaotong Liu, Hamed Heydari, Eric E. Schadt, Benjamin VanderSluis, Benjamin Oately, Xiaoye Liu, Vanja Paunic, Brian G Van Ness, Charles Boone |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
0301 basic medicine Science Gwas data General Physics and Astronomy Breast Neoplasms Genome-wide association study Locus (genetics) Computational biology Disease Biology Polymorphism Single Nucleotide Article General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 0302 clinical medicine Parkinsonian Disorders Genotype Humans Gene Regulatory Networks Genetic Predisposition to Disease lcsh:Science Gene Genetic association study Genetic association Multidisciplinary Models Genetic Genetic interaction Prostatic Neoplasms General Chemistry Phenotype Computational biology and bioinformatics 030104 developmental biology Diabetes Mellitus Type 2 Hypertension Schizophrenia Female lcsh:Q 030217 neurology & neurosurgery Genome-Wide Association Study |
| Zdroj: | Nature Communications, Vol 10, Iss 1, Pp 1-18 (2019) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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