Comparing ARG inference methods under transmission of reproductive success: Tree imbalance matters
Abstract
iInferring coalescent trees from genomic data has become a major subject in population genetics, particularly with the recent advances in tree sequence reconstruction methods. Yet, how well these methods perform under imbalanced genealogies remains unclear. Such imbalances can arise from processes such as cultural transmission of reproductive success (CTRS) or positive selection. Using simulated genomic data, we benchmarked two major software packages, Relate and tsinfer, by comparing the imbalance of reconstructed trees by these methods with that of the true simulated trees, for three indices that quantify this imbalance. Both methods performed well under scenarios yielding balanced trees. However, their accuracy declined as imbalance increased. Performances also varied with mutation rate, recombination rate, and sample size. This study opens possibilities for applying these methods to infer CTRS or positive selection in large-scale genomic datasets, using simulation-based inference such as approximate Bayesian computation.
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BibTeX
@article{pouyet2026comparing,
title = {{Comparing ARG inference methods under transmission of reproductive success: Tree imbalance matters}},
author = {Fanny Pouyet and Ferdinand Petit and Jérémy Guez and Léo Planche and Evelyne Heyer and Bruno Toupance and Flora Jay and Frédéric Austerlitz},
journal = {Genome Biology and Evolution},
year = {2026},
month = aug,
doi = {10.1093/gbe/evag215},
url = {https://cnrs.hal.science/hal-05347078v2},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Comparing ARG inference methods under transmission of reproductive success: Tree imbalance matters
AU - Fanny Pouyet
AU - Ferdinand Petit
AU - Jérémy Guez
AU - Léo Planche
AU - Evelyne Heyer
AU - Bruno Toupance
AU - Flora Jay
AU - Frédéric Austerlitz
PY - 2026
DA - 2026/08/26
JO - Genome Biology and Evolution
DO - 10.1093/gbe/evag215
UR - https://cnrs.hal.science/hal-05347078v2
AB - iInferring coalescent trees from genomic data has become a major subject in population genetics, particularly with the recent advances in tree sequence reconstruction methods. Yet, how well these methods perform under imbalanced genealogies remains unclear. Such imbalances can arise from processes such as cultural transmission of reproductive success (CTRS) or positive selection. Using simulated genomic data, we benchmarked two major software packages, Relate and tsinfer, by comparing the imbalance of reconstructed trees by these methods with that of the true simulated trees, for three indices that quantify this imbalance. Both methods performed well under scenarios yielding balanced trees. However, their accuracy declined as imbalance increased. Performances also varied with mutation rate, recombination rate, and sample size. This study opens possibilities for applying these methods to infer CTRS or positive selection in large-scale genomic datasets, using simulation-based inference such as approximate Bayesian computation.
SN - 1759-6653
SN - 1759-6653
ER -