ArticleGenetics2022
Robust detection of natural selection using a probabilistic model of tree imbalance.
Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Comparing ARG Inference Methods Under Transmission of Reproductive Success: Tree Imbalance Matters.Genome biology and evolution · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Population Genomic Scans for Natural Selection and Demography.Annual review of genetics · 2024Review
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2 authors.
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Abstract
Neutrality tests such as Tajima's D and Fay and Wu's H are standard implements in the population genetics toolbox. One of their most common uses is to scan the genome for signals of natural selection. However, it is well understood that D and H are confounded by other evolutionary forces-in particular, population expansion-that may be unrelated to selection. Because they are not model-based, it is not clear how to deconfound these tests in a principled way. In this article, we derive new likelihood-based methods for detecting natural selection, which are robust to fluctuations in effective population size. At the core of our method is a novel probabilistic model of tree imbalance, which generalizes Kingman's coalescent to allow certain aberrant tree topologies to arise more frequently than is expected under neutrality. We derive a frequency spectrum-based estimator that can be used in place of D, and also extend to the case where genealogies are first estimated. We benchmark our methods on real and simulated data, and provide an open source software implementation.
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