Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database

Tao Wang, Chunhui Bu, Sara Hildebrand, Gaoxiang Jia, Owen M. Siggs, Stephen Lyon, David Pratt, Lindsay Scott, Jamie L. Russell, Sara Ludwig, Anne R. Murray, Eva Marie Y. Moresco, Bruce Beutler

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16 Citations (Scopus)
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Abstract

Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Programs such as PolyPhen-2 predict the relative severity of damage caused by missense mutations, but not the actual probability that a mutation will reduce/eliminate protein function. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified "probably damaging", "possibly damaging", or "probably benign" mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state. We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. We estimate the proportion of essential autosomal genes in Mus musculus (C57BL/6J) and show that viable mutations in essential genes are more likely to induce phenotype than mutations in non-essential genes.

Original languageEnglish
Article number441
Number of pages10
JournalNature Communications
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Dec 2018
Externally publishedYes

Bibliographical note

' This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.'

Keywords

  • Data processing
  • Genetic linkage study

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