Rabier, C.-E.; Berry, V.; Glaszmann, J.-C.

Asian rice is one of the best documented crops in terms of genetic diversity. The domestication process, that probably started 9000 years ago in China, remains difficult to infer since the main vertical signal is blurred by horizontal signals related to gene flow among cultivars and wild relatives. Consequently, a large number of hypotheses on the domestication process of rice have been published. Besides, most of the methods used to infer these scenarios do not model all the known biological phenomena at stake. Here, we present a methodological study based on a rich stochastic model, that incorporates introgression events, incomplete lineage sorting, and mutations that happen over time. The global evolutionary scenario is represented by a phylogenetic network. Furthermore, each locus scenario is modeled according to a locus tree through the Multispecies Network Coalescent. More importantly, for inferring the phylogenetic network, we propose a new hybrid approach combining a phylogenetic network method and a machine learning technique. In particular, our hybrid approach, named Snarf, benefits from advantages of a mathematical phylogenetic method, SnappNet, and from the potential of a powerful machine learning classifier, i.e. Approximate Bayesian Computation Random Forest (ABC-RF). These two methods are complementary since SnappNet reconstructs network accurately, whereas ABC-RF is able to handle a large amount of data. The originality is twofold. First, prior distributions required for ABC-RF are calibrated thanks to SnappNet's estimates. Secondly, ABC-RF relies on summary statistics inspired by phylogenetic network literature. We show, on simulated data, that the Snarf hybrid approach enjoys very good performances. On rice real data, it infers a scenario with a unique domestication (that of Japonica), followed by three reticulation events involving early Japonica. It highlights two introgression events at the origin of Indica and cAus, and one admixture event responsible for the emergence of cBas.