ReviewHuman mutation2017
Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests.
Review in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-evolutionary exome analysis identifies novel type 2 diabetes mellitus genes in the UK Biobank and all of us.PLoS genetics · 2025Pooled it
- Structural determinants of ligand response specificity in the mast cell activating GPCR, MRGPRX2.The Journal of biological chemistry · 2026Article
- PIEZO1-mediated mechanosensation links aging to bladder dysfunction.bioRxiv : the preprint server for biology · 2026Article
- Ultra-rare functional variants reveal early-onset breast cancer risk genes and pathways in the UK Biobank and All of Us Research Program.American journal of human genetics · 2026Article
- Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.Human genetics · 2025Article
- QAFI: a novel method for quantitative estimation of missense variant impact using protein-specific predictors and ensemble learning.Human genetics · 2025Article
- Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects.Nature communications · 2025Article
- Variants in ZFX are associated with an X-linked neurodevelopmental disorder with recurrent facial gestalt.American journal of human genetics · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Functional variants identify sex-specific genes and pathways in Alzheimer's Disease.Nature communications · 2023Article
- Genome interpretation using in silico predictors of variant impact.Human genetics · 2022Review
- Identification of risk genes for Alzheimer's disease by gene embedding.Cell genomics · 2022Article
- EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.Nucleic acids research · 2022Article
- A general calculus of fitness landscapes finds genes under selection in cancers.Genome research · 2022Article
- A method to delineate de novo missense variants across pathways prioritizes genes linked to autism.Science translational medicine · 2021Article
- Identification of evolutionarily stable functional and immunogenic sites across the SARS-CoV-2 proteome and the greater coronavirus family.Research square · 2021Article
- Decoding Cancer Variants of Unknown Significance for Helicase-Nuclease-RPA Complexes Orchestrating DNA Repair During Transcription and Replication.Frontiers in molecular biosciences · 2021Article
- Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge.Human mutation · 2020Article
- Article
- Predicting pathogenicity of missense variants with weakly supervised regression.Human mutation · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree among themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of evolutionary action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.