Biology subjects

Glaszmann, J.-C.

Publications and source records attributed to Glaszmann, J.-C..

3 recordsLinked to original sources

bioRxiv2026

A hybrid approach combining a phylogenetic method and Approximate Bayesian Computation Random Forest for phylogenetic network inference: application to the rice domestication process in Asia

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 SO_SCPLOWNARFC_SCPLOW, benefits from advantages of a mathematical phylogenetic method, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, 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 SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW 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 SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOWs estimates. Secondly, ABC-RF relies on summary statistics inspired by phylogenetic network literature. We show, on simulated data, that the SO_SCPLOWNARFC_SCPLOW 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. Author summaryToday, in genomics, there is a real need for methods able to infer phylogenetic networks. A phylogenetic network is a directed graph representing events like hybridization, introgression, and horizontal gene transfer. Understanding these complex biological phenomena, essential for crop adaptation, can help breeders when facing challenges like climate change and population growth. Genome-wide diversity analysis thus requires network methods scaling for large data volumes and incorporating fundamental biological phenomena. In this context, we present a new hybrid approach, SO_SCPLOWNARFC_SCPLOW, that benefits from the potential of a powerful machine learning classifier, Approximate Bayesian Computation Random Forest, and from advantages of a mathematical phylogenetic method, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW. Consequently, SO_SCPLOWNARFC_SCPLOW is able to handle large data-sets thanks to machine learning and is also based on a deep mathematical theory. On simulated data, our hybrid approach performs very well. When applied to real rice genomic data, it supports a scenario with a single domestication event, that of Japonica. The analysis further highlights the role of early Japonica in the origin of both Indica and circumAus. Finally, it identifies an ancient admixture event, involving circumAus in the emergence of circumBasmati. Together, these findings confirm the importance of early rice history along the Himalayan region.

evolutionary biology↗

bioRxiv2020

On the inference of complex phylogenetic networks by Markov Chain Monte-Carlo

For various species, high quality sequences and complete genomes are nowadays available for many individuals. This makes data analysis challenging, as methods need not only to be accurate, but also time efficient given the tremendous amount of data to process. In this article, we introduce an efficient method to infer the evolutionary history of individuals under the multispecies coalescent model in networks (MSNC). Phylogenetic networks are an extension of phylogenetic trees that can contain reticulate nodes, which allow to model complex biological events such as horizontal gene transfer, hybridization, introgression and recombination. We present a novel way to compute the likelihood of biallelic markers sampled along genomes whose evolution involved such events. This likelihood computation is at the heart of a Bayesian network inference method called SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, as it extends the SO_SCPLOWNAPPC_SCPLOW method [1] inferring evolutionary trees under the multispecies coalescent model, to networks. SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is available as a package of the well-known beast 2 software. Recently, the MCMCBiMarkers method [2] also extended SO_SCPLOWNAPPC_SCPLOW to networks. Both methods take biallelic markers as input, rely on the same model of evolution and sample networks in a Bayesian framework, though using different methods for computing priors. However, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using extensive simulations, we compare performances of SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW and MCMCBiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is more accurate than MCMCBiMarkers on more complex network scenarios. Also, on complex networks, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW is found to be extremely faster than MCMCBiMarkers in terms of time required for the likelihood computation. We finally illustrate SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW performances on a rice data set. SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW infers a scenario that is compatible with simpler schemes proposed so far and provides additional understanding of rice evolution. Author summaryNowadays, to make the best use of the vast amount of genomic data at our disposal, there is a real need for methods able to model complex biological mechanisms such as hybridization and introgression. Understanding such mechanisms can help geneticists to elaborate strategies in crop improvement that may help reducing poverty and dealing with climate change. However, reconstructing such evolution scenarios is challenging. Indeed, the inference of phylogenetic networks, which explicitly model reticulation events such as hybridization and introgression, requires high computational resources. Then, on large data sets, biologists generally deduce reticulation events indirectly using species tree inference tools. In this context, we present a new Bayesian method, called SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW, dedicated to phylogenetic network inference. Our method is competitive in terms of execution speed with respect to its competitors. This speed gain enables us to consider more complex evolution scenarios during Bayesian analyses. When applied to rice genomic data, SO_SCPLOWNAPPC_SCPLOWNO_SCPLOWETC_SCPLOW suggested a new evolution scenario, compatible with the existing ones: it posits cAus as the result of an early combination between the Indica and Japonica lineages, followed by a later combination between the cAus and Japonica lineages to derive cBasmati. This accounts for the well-documented wide hybrid compatibility of cAus.

evolutionary biology↗

bioRxiv2020

Multiple streams of genetic diversity in Japonica rice

In-depth studies on the genetic diversity of crops indicate that domestication is likely a drawn-out process that differs from the traditional representation of a simple rapid bottleneck. Asian cultivated rice provides a clear picture of multiple foundations of crop diversity. Among them, Japonica rice is likely the group derived from the first human manipulations of this species. We make use of the 3,000 Rice Genomes (3K RG) data set, first described in 2018, to explore the genetic diversity of traditional Japonica rice. After delineating introgressions from the Indica and cAus cultivar groups, we mask these traces to analyse Japonica diversity in more depth. We find differentiation between the established "temperate", "subtropical" and "tropical" subgroups, and identify stream-like traces of highly divergent sources from broad geographic ranges and subgroups. We characterize five such streams, most visible respectively in: 1) Indonesia, 2) continental Southeast Asia, 3) China, 4) uplands of Japan, and 5) Bhutan. These streams likely consist of ancient alien introgressions propagated through geneflow to different degrees. They currently appear as long genome segments conserved among specific germplasm groups, as well as shorter segments more broadly distributed across diverse germplasm along what could be adaptive corridors. They are all represented in the Japonica component of cBasmati varieties, thought to have emerged over two millennia ago. We thus provide strong evidence that Japonica, the group posited as being the most direct product of a simple domestication process in China, is an aggregate derived from multiple waves of admixture and represents a composite gene pool with ancient Asia-wide population dynamics.

genomics↗