Discovering genetic interactions bridging pathways in genome-wide association studies

Abstract

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...

Systematic analysis of bypass suppression of essential genes

“…Genetic suppression is also relevant to the resilience of healthy people carrying highly penetrant disease variants and may identify novel strategies for therapeutic intervention (Riazuddin et al , ; Chen et al , 2016b). Mapping genetic interactions, including suppression, in model organisms provides a powerful approach for dissecting gene function and pathway connectivity and for defining conserved properties of genetic interactions that can elucidate genotype‐to‐phenotype relationships (Costanzo et al , ; Wang et al , ; Fang et al , ).…”

Publicly available hiPSC lines with extreme polygenic risk scores for modeling schizophrenia

“…Third, and most importantly, this collection is unlikely to prove to be adequately powered to resolve sex-specific and/or high versus low PRS effects, and is mostly intended for the validation of isogenic functional genomic studies across a diverse collection of donor backgrounds. Of course, experimentally deconvolving the extent to which individual risk factors exert variable effects across extreme PRS donor backgrounds will help to resolve the extent to which polygenic risk reflects additive 26,72 or more complex epistatic 28 or omnigenic models 29 of inheritance, and will in turn shape future refinements of PRS.…”

“…Is there pathway level convergence of these disorder-associated genes, and if so, does convergence occur in a cell-typespecific manner? Do these common variants of small effect interact in a strictly additive fashion 26 , or through more complex epistatic 28 or omnigenic models 29 ? Systematic functional validation of these many risk variants requires a more tractable and amendable model.…”

Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives

“…The PRS should also consider complex genetic interactions, which affect individual’s phenotypes and might determine the genetic bases of variation in quantitative traits and individual’s risk to complex diseases (Fang et al 2019 ; Hill et al 2008 ; Sackton and Hartl 2016 ; Zuk et al 2012 ). Epistasis is one example, according to which gene–gene interactions result in masked or altered genotype–phenotype relationships.…”