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Section: Evolutionary Biology
Topic: Evolution, Genetics/genomics, Computer sciences

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

Korfmann, Kevin1   ; Sellinger, Thibaut Paul Patrick2, 1  ; Freund, Fabian3, 4 ; Fumagalli, Matteo5, 6 ; Tellier, Aurélien1  
1 Department of Life Science Systems, Technical University of Munich, Munich, Germany
2 Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
3 Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
4 Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
5 School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
6 The Alan Turing Institute, London, UK

10.24072/pcjournal.397 - Peer Community Journal, Volume 4 (2024), article no. e33.

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The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of offspring negligible compared to the total population size. Yet many species of plants, invertebrates, prokaryotes or fish exhibit neutrally skewed offspring distribution or strong selection events yielding few individuals to produce a number of offspring of up to the same magnitude as the population size. As a result, the genealogy of a sample is characterized by multiple individuals (more than two) coalescing simultaneously to the same common ancestor. The current methods developed to detect such multiple merger events do not account for complex demographic scenarios or recombination, and require large sample sizes. We tackle these limitations by developing two novel and different approaches to infer multiple merger events from sequence data or the ancestral recombination graph (ARG): a sequentially Markovian coalescent (SMβC) and a graph neural network (GNNcoal). We first give proof of the accuracy of our methods to estimate the multiple merger parameter and past demographic history using simulated data under the β-coalescent model. Secondly, we show that our approaches can also recover the effect of positive selective sweeps along the genome. Finally, we are able to distinguish skewed offspring distribution from selection while simultaneously inferring the past variation of population size. Our findings stress the aptitude of neural networks to leverage information from the ARG for inference but also the urgent need for more accurate ARG inference approaches.

Published online:
DOI: 10.24072/pcjournal.397
Type: Research article
Korfmann, Kevin 1; Sellinger, Thibaut Paul Patrick 2, 1; Freund, Fabian 3, 4; Fumagalli, Matteo 5, 6; Tellier, Aurélien 1

1 Department of Life Science Systems, Technical University of Munich, Munich, Germany
2 Department of Environment and Biodiversity, Paris Lodron University of Salzburg, Salzburg, Austria
3 Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
4 Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
5 School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
6 The Alan Turing Institute, London, UK
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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Korfmann, Kevin; Sellinger, Thibaut Paul Patrick; Freund, Fabian; Fumagalli, Matteo; Tellier, Aurélien. Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent. Peer Community Journal, Volume 4 (2024), article  no. e33. doi : 10.24072/pcjournal.397. https://peercommunityjournal.org/articles/10.24072/pcjournal.397/

Peer reviewed and recommended by PCI : 10.24072/pci.evolbiol.100699

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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