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Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescentuse asterix (*) to get italics
Kevin Korfmann, Thibaut Sellinger, Fabian Freund, Matteo Fumagalli, Aurélien TellierPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p style="text-align: justify;">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.</p>
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kingman coalescent, beta coalescent, selective sweep, deep learning, graph neural networks, population genetics, multiple merger coalescent, sequentially markovian coalescent, ancestral recombination graph
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Adaptation, Bioinformatics & Computational Biology, Evolutionary Applications, Evolutionary Theory, Life History, Population Genetics / Genomics
Pavlos Pavlidis (, Flora Jay (, Jere Koskela (, Jeffrey Jensen (, Franz Baumdicker (, Jere Koskela suggested: I was a coauthor of Thibaut Sellinger as recently as last year. I'd recommend you ask Julia Palacios ( or Patrick Hoscheit (, Stephan Schiffels suggested: You could try Jerome Kelleher (Big Data Institute, Oxford) or Harald Ringbauer (Harald Ringbauer <>) from my department. No need for them to be recommenders of PCIEvolBiol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2023-07-31 13:11:22
Julien Yann Dutheil