ArticlePLoS computational biology2013
Amino acid changes in disease-associated variants differ radically from variants observed in the 1000 genomes project dataset.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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
40 citing papers in PubMed, 68 citations in OpenAlex.
- Mitochondrial proteins and congenital birth defect risk: a mendelian randomization study.BMC pregnancy and childbirth · 2025Article
- VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.Computational and structural biotechnology journal · 2025Article
- Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Cell research · 2024Article
- A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites.Communications biology · 2024Article
- Exploring Novel Variants of the Cytochrome P450 Reductase Gene (Biomolecules · 2023Article
- Intact Transition Epitope Mapping-Force Differences between Original and Unusual Residues (ITEM-FOUR).Biomolecules · 2023Article
- Pathogenic missense variants altering codon 336 of GARS1 lead to divergent dominant phenotypes.Human mutation · 2022Article
- Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum.Frontiers in pharmacology · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Compensatory epistasis explored by molecular dynamics simulations.Human genetics · 2021Article
- Protein-protein and protein-nucleic acid binding residues important for common and rare sequence variants in human.BMC bioinformatics · 2020Article
- Variant effect predictions capture some aspects of deep mutational scanning experiments.BMC bioinformatics · 2020Article
- Association of small-fiber polyneuropathy with three previously unassociated rare missense SCN9A variants.Canadian journal of pain = Revue canadienne de la douleur · 2020Article
- Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces.International journal of molecular sciences · 2019Article
- A resource of variant effect predictions of single nucleotide variants in model organisms.Molecular systems biology · 2018Article
- RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions.Human mutation · 2018Article
- Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures.American journal of human genetics · 2018Article
- Genetic variants affecting equivalent protein family positions reflect human diversity.Scientific reports · 2017Article
- Spatial distribution of disease-associated variants in three-dimensional structures of protein complexes.Oncogenesis · 2017Article
- Protein structure and phenotypic analysis of pathogenic and population missense variants inMolecular genetics & genomic medicine · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids) rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%), with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.
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.