A genome-wide case-only test for the detection of digenic inheritance in human exomes.
Gaspard KernerMatthieu BouazizAurélie CobatBenedetta BigioAndrew T TimberlakeJacinta BustamanteRichard P LiftonJean-Laurent CasanovaLaurent AbelPublished in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Whole-exome sequencing (WES) has facilitated the discovery of genetic lesions underlying monogenic disorders. Incomplete penetrance and variable expressivity suggest a contribution of additional genetic lesions to clinical manifestations and outcome. Some monogenic disorders may therefore actually be digenic. However, only a few digenic disorders have been reported, all discovered by candidate gene approaches applied to at least one locus. We propose here a two-locus genome-wide test for detecting digenic inheritance in WES data. This approach uses the gene as the unit of analysis and tests all pairs of genes to detect pairwise gene × gene interactions underlying disease. It is a case-only method, which has several advantages over classic case-control tests, in particular by avoiding recruitment of controls. Our simulation studies based on real WES data identified two major sources of type I error inflation in this case-only test: linkage disequilibrium and population stratification. Both were corrected by specific procedures. Moreover, our case-only approach is more powerful than the corresponding case-control test for detecting digenic interactions in various population stratification scenarios. Finally, we confirmed the potential of our unbiased, genome-wide approach by successfully identifying a previously reported digenic lesion in patients with craniosynostosis. Our case-only test is a powerful and timely tool for detecting digenic inheritance in WES data from patients.
Keyphrases
- genome wide
- case control
- copy number
- dna methylation
- mitochondrial dna
- electronic health record
- end stage renal disease
- gene expression
- chronic kidney disease
- ejection fraction
- small molecule
- machine learning
- hepatitis c virus
- genome wide identification
- deep learning
- human immunodeficiency virus
- hiv infected
- data analysis
- peritoneal dialysis
- antiretroviral therapy
- genome wide association study
- induced pluripotent stem cells