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.
Highlights
Allele-specific expression (ASE) concerns the divergent expression quantity of alternative allelic copies[1,2]
At a threshold of 0.5, we find a positive predictive value (PPV) of 0.73, a negative predictive value (NPV) of 0.91, a sensitivity of 0.29, and a specificity of 0.99
We have proven that Allele specific expression (ASE) can be predicted from DNA features using machine learning models, with high specificity, albeit with low sensitivity
Summary
Allele-specific expression (ASE) concerns the divergent expression quantity of alternative allelic copies[1,2]. Around one-third of all non-synonymous single nucleotide polymorphisms are allelically imbalanced and nonsense variants are consistently lower expressed than control s ites[9], establishing a clear link between pathogenic DNA variation and ASE. In absence of RNA measurements, we must resort to predicting ASE effects to inform genome diagnostics. Estimated ASE effects could help to identify or reject candidate pathogenic variants, including coding variants that cause nonsense-mediated decay detected as ASE29, and cis-acting non-coding variants that regulate transcription of pathogenic a lleles[30]. Heterozygous pathogenic variants in recessive disease genes could be prioritized if the ASE effect of a cis-acting variant is predicted to silence the ’healthy’ allele. When testing for pathogenic variants in families, incomplete penetrance may be explained if the ASE effect of a cis-acting variant is predicted to silence the pathogenic. RNA sequencing or other biochemical tests such as PCR can be performed on the suspected functional defect to reach a final molecular diagnosis
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