
The author argues that current methods for population genetics inference may not accurately account for multiple merger events in genealogy, leading to limitations in understanding past demography and selection. By developing novel approaches like SMβC and GNNcoal, the author aims to improve accuracy in inferring complex demographic scenarios and selection effects.


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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 (SM β C) and a graph neural network (GNN coal ). 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.

### Competing Interest Statement

The authors have declared no competing interest.

Code used to generate the simulated data for analysis, training and validation alongside (trained) deep learning models can be found at <https://github.com/kevinkorfmann/> GNNcoal and <https://github.com/kevinkorfmann/GNNcoal-analysis>. Code for SMC approaches used in this manuscript are available in the R package eSMC2 <https://github.com/TPPSellinger/eSMC2>. msprime and its documentation can be found: <https://tskit.dev/msprime/docs/stable/quickstart.html>.

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

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


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