Inference of the Cultural Transmission of Reproductive Success from human genomic data: ABC and machine learning methods

Arnaud Quelin (1, 2, 3) , Jérémy Guez (1, 2) , Ferdinand Petit (1, 2) , Flora Jay (2, 3, 4) , Frédéric Austerlitz (1)

1 EA - Éco-Anthropologie
2 LISN - Laboratoire Interdisciplinaire des Sciences du Numérique
3 TAU - TAckling the Underspecified
4 BioInfo - BioInformatique - LISN

The Cultural Transmission of Reproductive Success (CTRS) is one of the various cultural processes that can impact human genetic evolution. In this process, individuals from large families have more children on average. Here, we develop and evaluate methods to infer this process from genomic data, using two approaches: (1) Approximate Bayesian computation, which uses summary statistics computed on inferred genealogies from genomic data and (2) deep neural networks, which are directly trained on genomic data. These methods rely on large simulated datasets incorporating varying levels of CTRS. Both competing approaches show a good ability to infer CTRS on genomic data and worth investigating under more complex evolutionary histories.

Mots clés: Human Genetics; Machine learning methods; Genomics; Cultural Transmission of Reproductive Success (CTRS); Human genomic data; Genetic evolution; Process genomic data; Approximate Bayesian computation ABC; Genealogies; Deep neural networks; Simulated datasets

Domaines: Génétique des populations [q-bio.PE]; Modélisation et simulation; Génétique humaine; Anthropologie biologique

Arnaud Quelin, Jérémy Guez, Ferdinand Petit, Flora Jay, Frédéric Austerlitz. Inference of the Cultural Transmission of Reproductive Success from human genomic data: ABC and machine learning methods. Alphy/AIEM 2023 - Rencontres Alphy & AIEM, Jan 2023, Grenoble, France. ⟨hal-03960408⟩