文献类型： 外文期刊

第一作者： Rabier, Charles-Elie

作者： Rabier, Charles-Elie;Stoltz, Marnus;Scornavacca, Celine;Rabier, Charles-Elie;Berry, Vincent;Pardi, Fabio;Rabier, Charles-Elie;Santos, Joao D.;Glaszmann, Jean-Christophe;Santos, Joao D.;Glaszmann, Jean-Christophe;Wang, Wensheng

作者机构：

期刊名称：PLOS COMPUTATIONAL BIOLOGY （ 影响因子：4.779； 五年影响因子：5.916 ）

ISSN： 1553-734X

年卷期： 2021 年 17 卷 9 期

页码：

收录情况： SCI

摘要： Author summary Nowadays, 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 SnappNet, 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, SnappNet retrieved an evolution scenario that confirms the global triple foundation of the species and the origin of cBasmati as a hybrid derivative between Japonica cultivars and a local Indian form. It suggests that this hybridization is ancient and probably precedes the domestication of cAus. 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 and introgression. 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 SnappNet, as it extends the Snapp method inferring evolutionary trees under the multispecies coalescent model, to networks. SnappNet is available as a package of the well-known beast 2 software. Recently, the MCMC_BiMarkers method, implemented in PhyloNet, also extended Snapp 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, SnappNet relies on algorithms that are exponentially more time-efficient on non-trivial networks. Using simulations, we compare performances of SnappNet and MCMC_BiMarkers. We show that both methods enjoy similar abilities to recover simple networks, but SnappNet is more accurate than MCMC_BiMarkers on more complex network scenarios. Also, on complex networks, SnappNet is found to be extremely faster than MCMC_BiMarkers in terms of time required for the likelihood computation. We finally illustrate SnappNet performances on a rice data set. SnappNet infers a scenario that is consistent with previous results and provides additional understanding of rice evolution.

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