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Genetic regulatory variation in populations informs transcriptome analysis in rare disease.
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; Lappalainen, Tuuli.
Afiliación
  • Mohammadi P; New York Genome Center, New York, NY, USA. pejman@scripps.edu tlappalainen@nygenome.org.
  • Castel SE; Department of Systems Biology, Columbia University, New York, NY, USA.
  • Cummings BB; Scripps Research Translational Institute, La Jolla, CA, USA.
  • Einson J; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
  • Sousa C; New York Genome Center, New York, NY, USA.
  • Hoffman P; Department of Systems Biology, Columbia University, New York, NY, USA.
  • Donkervoort S; Analytical and Translation Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
  • Jiang Z; Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Mohassel P; New York Genome Center, New York, NY, USA.
  • Foley AR; Department of Systems Biology, Columbia University, New York, NY, USA.
  • Wheeler HE; Scripps Research Translational Institute, La Jolla, CA, USA.
  • Im HK; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
  • Bonnemann CG; New York Genome Center, New York, NY, USA.
  • MacArthur DG; Department of Systems Biology, Columbia University, New York, NY, USA.
  • Lappalainen T; Neuromuscular and Neurogenetic Disorders of Childhood Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Science ; 366(6463): 351-356, 2019 10 18.
Article en En | MEDLINE | ID: mdl-31601707
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Variación Genética / Enfermedades Raras / Transcriptoma / Enfermedades Musculares / Distrofias Musculares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Science Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Variación Genética / Enfermedades Raras / Transcriptoma / Enfermedades Musculares / Distrofias Musculares Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Science Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos