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马斯特:植物遗传推理与混合物跨站点和树木

Thomas K F Wong¹, Caitlin Cherryh², Allen G Rodrigo³

¹School of Computing, Australian National University, Canberra, ACT 2601, Australia.

Systematic biology

|February 29, 2024

概括

遗传学研究现在可以使用新的多树混合模型 (MAST) 来分析复杂的进化历史. 这种方法准确地推断出进化关系,并解释了诸如不完整的血统排序和内进化等因素.

科学领域:

计算生物学计算生物学
人类遗传学是一个学科.
进化生物学进化生物学

背景情况:

现代的家族基因学研究经常分析数百或数千个位置.
连锁方法假设一个单一的进化树,但由于不完整的血统分类 (ILS),内进化或水平基因转移,基因位置可以有不同的历史.
位点内的重组也可能违反单一树假设.

研究的目的:

引入一个多树混合模型的实施,称为跨站点和树木混合 (MAST).
扩展先前的混合模型,允许在单一对齐中预先指定分叉树的重量估计.
为分析具有复杂进化历史的连接对齐提供一个最大概率框架.

主要方法:

在最大概率框架内使用IQ-TREE软件实现MAST模型.
MAST模型允许每个树具有独立的参数,包括拓,分支长度,替代模型和速率异质性.
利用模拟来测试模型准确性和统计推理能力,以区分单树和多树场景.

主要成果:

模拟显示了模型参数的准确恢复,包括分支长度和树重,在各种生物现实的场景中.
标准的统计方法可以有效地拒绝单一树模型,当数据模拟在多个树下,反之亦然.
对灵长类动物数据集的应用揭示了MAST检测大的ILS和的内进信号的能力.

结论:

MAST模型成功地确定了Platyrrhine数据集中的主导树拓,其中连接和基因树方法不同意.
MAST提供了一个强大的工具,用于分析连接的对齐,使用最大的可能性,同时减轻偏差从假设一个单一的树.
该模型为未来的扩展提供了一个灵活的框架,以进一步增强遗传学推断.

关键词:

不完整的血统排序不完整的血统排序这是一个内进的进攻.混合物模型模型的混合物模型多树模型是多树模型.人类遗传学是个学科.

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