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Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

By Pejman Mohammadi, Stephane E. Castel, Beryl B. Cummings, Jonah Einson, Christina Sousa, Paul Hoffman, Sandra Donkervoort, Payam Mohassel, Reghan Foley, Heather E. Wheeler, Hae Kyung Im, Carsten G Bonnemann, Daniel G. MacArthur, Tuuli Lappalainen

Posted 09 May 2019
bioRxiv DOI: 10.1101/632794

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.

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