ArticleMolecular biology and evolution2022
A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.
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 58 papers.
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Who cites it
58 citing papers in PubMed.
- Enhancing detection of polygenic adaptation: a comparative study of machine learning and statistical approaches using simulated evolve-and-resequence data.BMC genomics · 2026Article
- ARGscape: a modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.Bioinformatics (Oxford, England) · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Machine learning for evolutionary genetics and molecular evolution.Trends in genetics : TIG · 2026Review
- The persistence and loss of hard selective sweeps amid admixture in ancient Eurasians.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Graph transformer for ancient ancestry inference.bioRxiv : the preprint server for biology · 2026Article
- Identifying Adaptive Footprints in the Presence of Demographic Uncertainty.Genome biology and evolution · 2026Article
- Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation.Genome biology and evolution · 2026Article
- Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.Molecular biology and evolution · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Likelihoods for a general class of ARGs under the SMC.Genetics · 2026Article
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- Selection scans and downstream analysis with selscan.Human population genetics and genomics · 2026Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
- Article
- Multiple-testing corrections in selection scans using identity-by-descent segments.American journal of human genetics · 2025Article
- Genealogical Analysis of Replicate Flower Colour Hybrid Zones in Antirrhinum.Molecular ecology · 2025Article
- The persistence and loss of hard selective sweeps amid ancient human admixture.bioRxiv : the preprint server for biology · 2025Article
- Selection scans and downstream analysis with selscan.bioRxiv : the preprint server for biology · 2025Article
- ARGscape: A modular, interactive tool for manipulation of spatiotemporal ancestral recombination graphs.ArXiv · 2025Article
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4 authors.
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Abstract
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.
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