ArticleMolecular biology and evolution2022
Inferring Epistasis from Genetic Time-series Data.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Optimal control and the dynamics of ancestral lineages.Genetics · 2026Article
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.Virus evolution · 2026Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
- Article
- Article
- Article
- Article
- Inferring fitness seascapes from evolutionary histories.bioRxiv : the preprint server for biology · 2025Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Efficient epistasis inference via higher-order covariance matrix factorization.bioRxiv : the preprint server for biology · 2024Article
- A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.bioRxiv : the preprint server for biology · 2024Article
- popDMS infers mutation effects from deep mutational scanning data.Bioinformatics (Oxford, England) · 2024Article
- Fast and Accurate Estimation of Selection Coefficients and Allele Histories from Ancient and Modern DNA.Molecular biology and evolution · 2024Article
- Lightweight Low-Rank Adaptation Vision Transformer Framework for Cervical Cancer Detection and Cervix Type Classification.Bioengineering (Basel, Switzerland) · 2024Article
- Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data.Molecular biology and evolution · 2024Article
- In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- popDMS infers mutation effects from deep mutational scanning data.bioRxiv : the preprint server for biology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.
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.