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

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

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


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 (US)
Article number441
JournalNature communications
Issue number1
StatePublished - Dec 1 2018

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)


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