ArticleGigaScience2020
GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed.
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- Genetic structure and historical demography of inland wetland fish using the endangered Lisbon arched-mouth nase as a case-study.Scientific reports · 2025Article
- Speciation in the Peninsular Indian Flying Lizard (Draco dussumieri) Follows Climatic Transition and Not Physical Barriers.Molecular ecology · 2025Article
- Pleistocene island connectivity did not enhance dispersal or impact population size change in Galápagos geckos.Proceedings. Biological sciences · 2025Article
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5 authors.
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Abstract
backgroundThe demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS.
resultsHere, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA).
conclusionsWe demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.
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