Research Article

Genetic regulatory variation in populations informs transcriptome analysis in rare disease

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Science  18 Oct 2019:
Vol. 366, Issue 6463, pp. 351-356
DOI: 10.1126/science.aay0256

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A statistical model to find disease genes

Genetic variation is high among individuals, which makes it difficult to identify any one specific pathogenetic variant in patients with idiopathic disease, especially those that are in noncoding regions of the genome. Examining tissue-specific and population-level RNA sequencing data, Mohammadi et al. developed a statistical test, analysis of expression variation (ANEVA), that can quantify how one individual's gene expression fits in the context of the variation within the general population. By applying ANEVA to a dosage outlier test, the authors identified pathogenic gene transcripts in patients with Mendelian muscle dystrophy.

Science, this issue p. 351


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.

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