To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and display a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100-fold larger than the global digenic network, highlighting the potential for complex genetic interactions to impact the biology of inheritance, including the genotype to phenotype relationship.
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.
Summary Mycobacterium abscessus is a rapidly growing non-tuberculous mycobacterial species that has been associated with a wide spectrum of human infections. As the classification and biology of this organism is still not well understood, comparative genomic analysis on members of this species may provide further insights on their taxonomy, phylogeny, pathogenicity and other information that may contribute to better management of infections. The MabsBase described in this paper is a user-friendly database providing access to whole-genome sequences of newly discovered M. abscessus strains as well as resources for whole-genome annotations and computational predictions, to support the expanding scientific community interested in M. abscessus research. The MabsBase is freely available at http://mabscessus.um.edu.my.
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, the global genetic networks 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 discovered 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.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/182741 doi: bioRxiv preprint first posted online Aug. 30, 2017; 3 Genome-wide association studies (GWAS) have been increasingly successful at identifying single-nucleotide polymorphisms (SNPs) with statistically significant association to a variety of diseases [1][2][3][4][5] and gene sets significantly enriched for SNPs with moderate association [6][7][8][9][10] . However, for most diseases, there remains a substantial disparity between the disease risk explained by the discovered loci and the estimated total heritable disease risk based on familial aggregation [11][12][13][14][15][16] . While there are a number of possible explanations for this "missing heritability", including many loci with small effects or rare variants [11][12][13][14][15]17 , genetic interactions between loci are one potential culprit 13,14,16,18,19 . Genetic interactions generally refer to a combination of two or more genes whose contribution to a phenotype cannot be completely explained by their independent effects 16,20,21 , For example, one example of an extreme genetic interaction is synthetic lethality, which is the case where two mutations, neither of which is lethal on its own, combines to generate a lethal double mutant phenotype. Genetic interactions allow relatively benign variation to combine and generate more extreme phenotypes, including complex human diseases [11][12][13]16,22 .While several studies have reported interactions between genetic variants in various disease contexts 20,[23][24][25][26] , and though efficient and scalable computational tools have been developed for searching for interactions amongst genome wide SNPs 20,[26][27][28] , discovering them systematically with statistical significance remains a major challenge. For examp...
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