Data from: Detecting hybridization by likelihood calculation of gene tree extra lineages given explicit models

Explanations for gene tree discordance with respect to a species tree are commonly attributed to deep coalescence (also known as incomplete lineage sorting [ILS]), as well as different evolutionary processes such as hybridization, horizontal gene transfer and gene duplication. Among these, deep coalescence is usually quantified as the number of extra lineages and has been studied as the principal source of discordance among gene trees, while the other processes that could contribute to gene tree discordance have not been fully explored. This is an important issue for hybridization because interspecific gene flow is well documented and widespread across many plant and animal groups. Here, we propose a new way to detect gene flow when ILS is present that evaluates the likelihood of different models with various levels of gene flow, by comparing the expected gene tree discordance, using the number of extra lineages. This approach consists of proposing a model, simulating a set of gene trees to infer a distribution of expected extra lineages given the model, and calculating a likelihood function by comparing the fit of the real gene trees to the simulated distribution. To count extra lineages, the gene tree is first reconciled within the species tree, and for a given species tree branch the number of gene lineages minus one is counted. We develop a set of R functions to parallelize software to allow simulations, and to compare hypotheses via a likelihood ratio test to evaluate the presence of gene flow when ILS is present, in a fast and simple way. Our results show high accuracy under very challenging scenarios of high impact of ILS and low gene flow levels, even using a modest dataset of five to ten loci and five to ten individuals per species. We present a powerful and fast method to detect hybridization in presence of ILS. We discuss its advantage with large dataset (such as genomic scale), and also identifies possible issues that should be explored with more complex models in future studies.针对物种树（species tree）的基因树冲突（gene tree discordance）现象，其成因通常被归因于深度趋同演化（deep coalescence，即不完全谱系分选（incomplete lineage sorting, ILS）），以及杂交（hybridization）、水平基因转移（horizontal gene transfer）、基因复制（gene duplication）等不同演化过程。其中，深度趋同演化通常以额外谱系数（number of extra lineages）进行量化，且被认为是引发基因树冲突的主要成因；而其他可能导致基因树冲突的演化过程尚未得到充分探究。这一问题在杂交研究中尤为重要，因为种间基因流（interspecific gene flow）已被大量文献报道，并广泛分布于众多动植物类群中。 为此，我们提出一种新方法，用于在存在不完全谱系分选（ILS）的场景下检测基因流：该方法通过比较基于额外谱系数得到的预期基因树冲突程度，评估不同基因流水平下的模型似然值。该方法的流程为：首先构建演化模型，随后模拟一组基因树以推导给定模型下的预期额外谱系数分布，最后通过比较真实基因树与模拟分布的契合度，计算似然函数。在统计额外谱系数时，需先将基因树与物种树进行基因树调和（gene tree reconciliation），针对每个物种树分支，统计该分支上的基因谱系数减一的数值，以此作为额外谱系数。我们开发了一组R语言函数，用于并行化模拟相关软件，并通过似然比检验（likelihood ratio test）比较不同假说，从而以快速简便的方式评估存在ILS时的基因流是否存在。 我们的研究结果显示，即便在ILS影响显著、基因流水平较低的高挑战性场景下，仅使用包含5至10个基因座（locus，复数形式为loci）、每个物种5至10个个体的中等规模数据集，该方法仍可达到较高的准确率。 本研究提出了一种高效且快速的方法，可用于在存在ILS的场景下检测杂交事件。我们讨论了该方法在大规模数据集（如基因组级（genomic scale）数据集）中的应用优势，同时也指出了未来研究中需借助更复杂模型进一步探究的潜在问题。