Genetic regulatory variation in populations informs transcriptome analysis in rare disease-Reference-Cited by-同舟云学术

Genetic regulatory variation in populations informs transcriptome analysis in rare disease

Published:2019-10-18 Issue:6463 Volume:366 Page:351-356
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Mohammadi Pejman¹²³⁴^ORCID,Castel Stephane E.¹²^ORCID,Cummings Beryl B.⁵⁶^ORCID,Einson Jonah¹²^ORCID,Sousa Christina³⁴^ORCID,Hoffman Paul¹²^ORCID,Donkervoort Sandra⁷,Jiang Zhuoxun⁸,Mohassel Payam⁷^ORCID,Foley A. Reghan⁷^ORCID,Wheeler Heather E.⁹¹⁰^ORCID,Im Hae Kyung⁸,Bonnemann Carsten G.⁷^ORCID,MacArthur Daniel G.⁵⁶^ORCID,Lappalainen Tuuli¹²^ORCID

Affiliation:

1. New York Genome Center, New York, NY, USA.

2. Department of Systems Biology, Columbia University, New York, NY, USA.

3. Scripps Research Translational Institute, La Jolla, CA, USA.

4. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.

5. Analytical and Translation Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.

6. Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

7. Neuromuscular and Neurogenetic Disorders of Childhood Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.

8. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.

9. Department of Biology, Loyola University Chicago, Chicago, IL, USA.

10. Department of Computer Science, Loyola University Chicago, Chicago, IL, USA.

Abstract

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

Funder

National Institutes of Health

National Institute of Mental Health

National Human Genome Research Institute

National Institute of General Medical Sciences

National Center for Advancing Translational Sciences

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference46 articles.

1. Analysis of protein-coding genetic variation in 60,706 humans