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 journalArticle

10 Citations (Scopus)

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 (US)
Article number441
JournalNature Communications
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2018

Fingerprint

mutations
Genes
Databases
damage
proteins
Mutation
Proteins
genes
Essential Genes
phenotype
Phenotype
Missense Mutation
genome
inference
Genotype
proportion
Genome
saturation
estimates

ASJC Scopus subject areas

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

Cite this

Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database. / Wang, Tao; Bu, Chun Hui; Hildebrand, Sara; Jia, Gaoxiang; Siggs, Owen M.; Lyon, Stephen; Pratt, David; Scott, Lindsay; Russell, Jamie; Ludwig, Sara; Murray, Anne R.; Moresco, Eva Marie Y.; Beutler, Bruce.

In: Nature Communications, Vol. 9, No. 1, 441, 01.12.2018.

Research output: Contribution to journalArticle

Wang, T, Bu, CH, Hildebrand, S, Jia, G, Siggs, OM, Lyon, S, Pratt, D, Scott, L, Russell, J, Ludwig, S, Murray, AR, Moresco, EMY & Beutler, B 2018, 'Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database', Nature Communications, vol. 9, no. 1, 441. https://doi.org/10.1038/s41467-017-02806-4
Wang, Tao ; Bu, Chun Hui ; Hildebrand, Sara ; Jia, Gaoxiang ; Siggs, Owen M. ; Lyon, Stephen ; Pratt, David ; Scott, Lindsay ; Russell, Jamie ; Ludwig, Sara ; Murray, Anne R. ; Moresco, Eva Marie Y. ; Beutler, Bruce. / Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database. In: Nature Communications. 2018 ; Vol. 9, No. 1.
@article{3ad410eb65f34e45a110d362008f7168,
title = "Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database",
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.",
author = "Tao Wang and Bu, {Chun Hui} and Sara Hildebrand and Gaoxiang Jia and Siggs, {Owen M.} and Stephen Lyon and David Pratt and Lindsay Scott and Jamie Russell and Sara Ludwig and Murray, {Anne R.} and Moresco, {Eva Marie Y.} and Bruce Beutler",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41467-017-02806-4",
language = "English (US)",
volume = "9",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

TY - JOUR

T1 - Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database

AU - Wang, Tao

AU - Bu, Chun Hui

AU - Hildebrand, Sara

AU - Jia, Gaoxiang

AU - Siggs, Owen M.

AU - Lyon, Stephen

AU - Pratt, David

AU - Scott, Lindsay

AU - Russell, Jamie

AU - Ludwig, Sara

AU - Murray, Anne R.

AU - Moresco, Eva Marie Y.

AU - Beutler, Bruce

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85041325943&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041325943&partnerID=8YFLogxK

U2 - 10.1038/s41467-017-02806-4

DO - 10.1038/s41467-017-02806-4

M3 - Article

C2 - 29382827

AN - SCOPUS:85041325943

VL - 9

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 441

ER -