Document Type
Article
Publication Date
10-18-2019
Publication Title
Science
Volume
366
Issue
6463
Pages
351-356
Publisher Name
American Association for the Advancement of Science
Abstract
Transcriptome data can facilitate the interpretation of the effects of rare genetic variants. Here, we introduce ANEVA (analysis of expression variation) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application of ANEVA to the Genotype-Tissues Expression (GTEx) data showed that this variance estimate is robust and correlated with selective constraint in a gene. Using these variance estimates in a dosage outlier test (ANEVA-DOT) applied to AE data from 70 Mendelian muscular disease patients showed accuracy in detecting genes with pathogenic variants in previously resolved cases and led to one confirmed and several potential new diagnoses. Using our reference estimates from GTEx data, ANEVA-DOT can be incorporated in rare disease diagnostic pipelines to use RNA-sequencing data more effectively.
Recommended Citation
Mohammadi, Pejman; Castel, Stephane E.; Cummings, Beryl B.; Einson, Jonah; Sousa, Christina; Hoffman, Paul; Donkervoort, Sandra; Jiang, Zhuoxun; Mohassel, Payam; Foley, A. Reghan; Wheeler, Heather E.; Im, Hae Kyung; Bonnemann, Carsten G.; MacArthur, Daniel G.; and Lappalainen, Tuuli. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science, 366, 6463: 351-356, 2019. Retrieved from Loyola eCommons, Biology: Faculty Publications and Other Works, http://dx.doi.org/10.1126/science.aay0256
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Copyright Statement
© The Authors, 2019.
Comments
Author Posting © The Authors, 2019. This is the author's version of the work. It is posted here by permission of The Authors for personal use, not for redistribution. The definitive version was published in Science, Volume 366, Issue 6463, July 2019. https://doi.org/10.1126/science.aay0256