ArticleNature genetics2016
An expanded sequence context model broadly explains variability in polymorphism levels across the human genome.
Article in Nature genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.
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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.
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Who cites it
123 citing papers in PubMed, 225 citations in OpenAlex.
- Sequence effects on mutation rates investigated in whole-genome sequenced UK Biobank participants.G3 (Bethesda, Md.) · 2026Article
- Massively parallel characterization and predictive modelling of neuronal regulatory variation.bioRxiv : the preprint server for biology · 2026Article
- Extended sequence context shapes mutational bias inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity.bioRxiv : the preprint server for biology · 2026Article
- Sequence context and methylation interact to shape germline mutation rate variation at CpG sites.PLoS genetics · 2026Article
- Sequence context and methylation interact to shape germline mutation rate variation at CpG sites.bioRxiv : the preprint server for biology · 2026Article
- Beyond Exons: Linking Noncoding Heritability and Polygenicity across Complex Human Traits and Disorders.bioRxiv : the preprint server for biology · 2026Article
- Methylation-associated mutagenesis underlies variation in the mutation spectrum across eukaryotes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Gene4Denovo2: an updated platform for human de novo mutations discovery and interpretation.Nucleic acids research · 2026Article
- Prior knowledge on context-driven DNA fragmentation probabilities can improve de novo genome assembly algorithms.BMC bioinformatics · 2025Article
- Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models.bioRxiv : the preprint server for biology · 2025Article
- Estimation of demography and mutation rates from one million haploid genomes.American journal of human genetics · 2025Article
- MUTAGENESISFORGE: A FRAMEWORK FOR MODELING CODON-LEVEL MUTATIONAL BIASES AND CALCULATING dN/dS.bioRxiv : the preprint server for biology · 2025Article
- Methylation-associated mutagenesis underlies variation in the mutation spectrum across eukaryotes.bioRxiv : the preprint server for biology · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- Landscape of human protein-coding somatic mutations across tissues and individuals.bioRxiv : the preprint server for biology · 2025Article
- Towards the genomic sequence code of DNA fragility for machine learning.Nucleic acids research · 2024Article
- A modeling of complex trait phenotypic variance determinants.PNAS nexus · 2024Article
- Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns.Molecular biology and evolution · 2024Article
- Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.Genome medicine · 2024Article
63 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
The rate of single-nucleotide polymorphism varies substantially across the human genome and fundamentally influences evolution and incidence of genetic disease. Previous studies have only considered the immediately flanking nucleotides around a polymorphic site--the site's trinucleotide sequence context--to study polymorphism levels across the genome. Moreover, the impact of larger sequence contexts has not been fully clarified, even though context substantially influences rates of polymorphism. Using a new statistical framework and data from the 1000 Genomes Project, we demonstrate that a heptanucleotide context explains >81% of variability in substitution probabilities, highlighting new mutation-promoting motifs at ApT dinucleotide, CAAT and TACG sequences. Our approach also identifies previously undocumented variability in C-to-T substitutions at CpG sites, which is not immediately explained by differential methylation intensity. Using our model, we present informative substitution intolerance scores for genes and a new intolerance score for amino acids, and we demonstrate clinical use of the model in neuropsychiatric diseases.
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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.