ArticleGenetics2023
Estimating linkage disequilibrium and selection from allele frequency trajectories.
Article in Genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- 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
- Back-projection improves inference from sparsely sampled genomic surveillance data.bioRxiv : the preprint server for biology · 2025Article
- Genomics of Experimental Adaptive Radiation in the Cryptic Coloration of Feather Lice.Genome biology and evolution · 2025Article
- Causal association between varicose veins and atrial fibrillation: A 2-sample bidirectional Mendelian randomization study.Medicine · 2025Article
- Efficient epistasis inference via higher-order covariance matrix factorization.bioRxiv : the preprint server for biology · 2024Article
- Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data.Molecular biology and evolution · 2024Article
- Bézier interpolation improves the inference of dynamical models from data.Physical review. E · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.
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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.