Evidence map›Paper›PMID 26269570›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

Comparison of predicted and actual consequences of missense mutations.

Lisa A Miosge, Matthew A Field, Yovina Sontani, Vicky Cho, Simon Johnson, Anna Palkova, Bhavani Balakishnan, Rong Liang, Yafei Zhang, Stephen Lyon and 6 more

Open access · bronzeAbstract readComparative Study
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
147citing papers in PubMed, 1 pooled it
19.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

147 citing papers in PubMed, 1 synthesis or guideline pooled it, 231 citations in OpenAlex.

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  16. Identification ofAnimal biotechnology · 2024
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  18. A fitness distribution law for amino-acid replacements.bioRxiv : the preprint server for biology · 2024
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87 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 2 institutions in 2 countries.

Lisa A MiosgeImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Matthew A FieldImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Yovina SontaniImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Vicky ChoImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia; Australian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Simon JohnsonImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia; Australian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Anna PalkovaImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia; Australian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Bhavani BalakishnanAustralian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Rong LiangAustralian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Yafei ZhangAustralian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Stephen LyonCenter for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390;
Bruce BeutlerCenter for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390;
Belinda WhittleAustralian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Edward M BertramAustralian Phenomics Facility, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Anselm EndersRamaciotti Immunisation Genomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia;
Christopher C GoodnowImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia; Immunogenomics Laboratory, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia c.goodnow@garvan.org.au dan.andrews@anu.edu.au.
T Daniel AndrewsImmunogenomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia; c.goodnow@garvan.org.au dan.andrews@anu.edu.au.
Australian National University · AUThe University of Texas Southwestern Medical Center · US

Funding

Systems Biology CoreU19AI100627 · NIAID · SCRIPPS RESEARCH INSTITUTE, THE · PI ULEVITCH, RICHARD J · 2012 to 2021
$49.1M
NIAID NIH HHS AI100627NIAID NIH HHS U19 AI100627Wellcome TrustWellcome Trust 082030/B/07/Z
6 · The paper itself

Abstract

Each person's genome sequence has thousands of missense variants. Practical interpretation of their functional significance must rely on computational inferences in the absence of exhaustive experimental measurements. Here we analyzed the efficacy of these inferences in 33 de novo missense mutations revealed by sequencing in first-generation progeny of N-ethyl-N-nitrosourea-treated mice, involving 23 essential immune system genes. PolyPhen2, SIFT, MutationAssessor, Panther, CADD, and Condel were used to predict each mutation's functional importance, whereas the actual effect was measured by breeding and testing homozygotes for the expected in vivo loss-of-function phenotype. Only 20% of mutations predicted to be deleterious by PolyPhen2 (and 15% by CADD) showed a discernible phenotype in individual homozygotes. Half of all possible missense mutations in the same 23 immune genes were predicted to be deleterious, and most of these appear to become subject to purifying selection because few persist between separate mouse substrains, rodents, or primates. Because defects in immune genes could be phenotypically masked in vivo by compensation and environment, we compared inferences by the same tools with the in vitro phenotype of all 2,314 possible missense variants in TP53; 42% of mutations predicted by PolyPhen2 to be deleterious (and 45% by CADD) had little measurable consequence for TP53-promoted transcription. We conclude that for de novo or low-frequency missense mutations found by genome sequencing, half those inferred as deleterious correspond to nearly neutral mutations that have little impact on the clinical phenotype of individual cases but will nevertheless become subject to purifying selection.

Indexed as

Mutation, MissenseAnimalsCodonComputational BiologyComputer SimulationExomeGenetic VariationGenomeGenome, HumanGenotypeHumansImmune SystemImmunologic Deficiency SyndromesMiceMice, Inbred C57BLModels, GeneticCodonTP53 protein, humanTumor Suppressor Protein p53cancerde novo mutationevolutionimmunodeficiencynearly neutral

Identifiers

PMID26269570
PMCPMC4577149
OpenAlexW1939472951

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.