Volume 87, Issue 1
UNIT

Interpreting de novo Variation in Human Disease Using denovolyzeR

James S. Ware

Department of Genetics, Harvard Medical School, Boston, Massachusetts

Broad Institute of MIT and Harvard, Cambridge, Massachusetts

Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

NIHR Cardiovascular Biomedical Research Unit at Royal Brompton Hospital and Imperial College London, London, United Kingdom

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Kaitlin E. Samocha

Department of Genetics, Harvard Medical School, Boston, Massachusetts

Broad Institute of MIT and Harvard, Cambridge, Massachusetts

Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

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Jason Homsy

Department of Genetics, Harvard Medical School, Boston, Massachusetts

Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts

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Mark J. Daly

Department of Genetics, Harvard Medical School, Boston, Massachusetts

Broad Institute of MIT and Harvard, Cambridge, Massachusetts

Analytical and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts

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First published: 06 October 2015
Citations: 22

Abstract

Spontaneously arising (de novo) genetic variants are important in human disease, yet every individual carries many such variants, with a median of 1 de novo variant affecting the protein‐coding portion of the genome. A recently described mutational model provides a powerful framework for the robust statistical evaluation of such coding variants, enabling the interpretation of de novo variation in human disease. Here we describe a new open‐source software package, denovolyzeR, that implements this model and provides tools for the analysis of de novo coding sequence variants. © 2015 by John Wiley & Sons, Inc.

Number of times cited according to CrossRef: 22

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