Feasibility of predicting allele specific expression from DNA sequencing using machine learning
Year: 2021
Type: article
Abstract: Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.
Source: Scientific Reports
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,990
Funders: Van Andel Research Institute, University of Miami, University of Pennsylvania, Harvard University, University of Chicago, North Carolina State University, Broad Institute, Leidos, ZonMw, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, China Scholarship Council, Universitair Medisch Centrum Groningen, Université de Genève, Nederlandse Federatie van Universitair Medische Centra, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Neurological Disorders and Stroke, Common Fund
Awards: G1028663679, Methods for high-resolution analysis of genetic effects on gene expression, G1206703408, G1217436835, G138129434, G1534570039, G1561323845, G1650773652, G1883398070, G2158443041, G2204307168, G2568082621, G2651523388, G275953476, G2835668146, G2877753951, G292137090, G3002430358, G3121967442, G3128394570, G3221727586, G32319252, G3387558946, G3443372292, G347407575, G3503792436, G3547560438, G3722710735, G3793778294, G386607036, G3952396931, Genetic Regulation of Gene Expression and its Impact on Phenotypes, G4110903388, G4124418613, G4382062407, G4395118462, G4478373029, G4510456360, G4544423771, G4606556408, G4715193760, G4729234894, G4770637985, G5096786130, G5097752872, G5286804338, G5425049328, G563538711, G5737582129, G5865458551, G5888954722, Methods for high-resolution analysis of genetic effects on gene expression, G6317516893, G633025773, G634178657, G6619812508, G664140718, G6678493692, G7075004892, G7274654584, G7353525908, FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPING, G7665525972, G7761642487, G7937240049, G7953026188, G8308451750, G8494855175, G8602100876, G8719970897, G8788848806, G8810271873, G8906494432, G9000036744, G9000103821, G9000118502, G9000191240, G9000221753, G9000288902
OpenAlex ID: W3162253102