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Bayesian inference of the mode of speciation and gene flow using genomic data

使用基因组数据对物种形成和基因流模式进行贝叶斯推断

基本信息

批准号：
BB/X007553/1
负责人：
Ziheng Yang
金额：
$ 82.61万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX007553%2F1
关键词：
Bayesian inference mode speciation gene

项目摘要

Genomic sequences from contemporary species contain rich information about the history of species divergence and the origin of species. By comparing DNA sequences from different species we can work out how the species are related, when they diverged from each other, and whether they have been exchanging genes. However, powerful statistical models and efficient computational algorithms are necessary to extract this information from genomic data. The multispecies coalescent model provides a natural framework for comparative analysis of genomic sequence data, as it accommodates the random fluctuations of biological reproduction when genetic materials are passed over generations, random accumulations of genetic mutations as well as possible hybridisation or introgression between species. We will extend our Bayesian inference program BPP to allow continuous gene flow between species, including isolation-with-migration, isolation-with-initial-migration, secondary contact, as well as complete isolation. Those models represent different biological hypotheses about the modes of speciation, and can be compared to further our understanding of the speciation process. The Bayesian methods to be developed in the project are efficient in extracting information in the genomic datasets, while accommodating important biological processes involved, such as the polymorphism in the ancestral species, uncertainties in the gene genealogical trees due to limited information in the sequence data from a short genomic segment. We will apply our newly developed methods to analyse genomic datasets from the big cats in the Panthera genus, Heliconius butterflies, Malagasy mouse lemurs, and North-American lizards, generated by our collaborators. We will infer the history of species divergences, the direction and timing of gene flow, as well as evolutionary parameters such as species divergence times and population sizes.

当代物种的基因组序列包含了丰富的物种分化历史和物种起源的信息。通过比较不同物种的DNA序列，我们可以弄清楚这些物种是如何联系在一起的，它们何时彼此分化，以及它们是否交换了基因。然而，强大的统计模型和高效的计算算法是必要的，从基因组数据中提取这些信息。多物种结合模型提供了一个自然的框架，基因组序列数据的比较分析，因为它适应生物繁殖的随机波动时，遗传物质通过几代人，随机积累的基因突变，以及可能的杂交或物种之间的渐渗。我们将扩展我们的贝叶斯推理程序BPP，以允许物种之间的连续基因流，包括隔离与迁移，隔离与初始迁移，二次接触，以及完全隔离。这些模型代表了关于物种形成模式的不同生物学假设，可以进行比较，以进一步了解物种形成过程。在该项目中开发的贝叶斯方法在提取基因组数据集中的信息方面是有效的，同时适应所涉及的重要生物过程，例如祖先物种中的多态性，由于短基因组片段的序列数据中的有限信息而导致的基因谱系树中的不确定性。我们将应用我们新开发的方法来分析我们的合作者生成的Panthera属大型猫科动物，Heliconius蝴蝶，马达加斯加鼠狐猴和北美蜥蜴的基因组数据集。我们将推断物种分化的历史，基因流动的方向和时间，以及物种分化时间和种群规模等进化参数。