A global test of hybrid ancestry from genome-scale data.

IF 0.9 4区 数学 Q3 Mathematics
Md Rejuan Haque, Laura Kubatko
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引用次数: 0

Abstract

Methods based on the multi-species coalescent have been widely used in phylogenetic tree estimation using genome-scale DNA sequence data to understand the underlying evolutionary relationship between the sampled species. Evolutionary processes such as hybridization, which creates new species through interbreeding between two different species, necessitate inferring a species network instead of a species tree. A species tree is strictly bifurcating and thus fails to incorporate hybridization events which require an internal node of degree three. Hence, it is crucial to decide whether a tree or network analysis should be performed given a DNA sequence data set, a decision that is based on the presence of hybrid species in the sampled species. Although many methods have been proposed for hybridization detection, it is rare to find a technique that does so globally while considering a data generation mechanism that allows both hybridization and incomplete lineage sorting. In this paper, we consider hybridization and coalescence in a unified framework and propose a new test that can detect whether there are any hybrid species in a set of species of arbitrary size. Based on this global test of hybridization, one can decide whether a tree or network analysis is appropriate for a given data set.

从基因组尺度数据对杂交血统进行全球测试。
基于多物种聚合的方法已被广泛应用于利用基因组尺度的 DNA 序列数据进行系统发生树估计,以了解采样物种之间的潜在进化关系。杂交等进化过程会通过两个不同物种之间的杂交产生新物种,因此有必要推断物种网络而不是物种树。物种树是严格分叉的,因此无法包含杂交事件,而杂交需要一个度数为三的内部节点。因此,在获得 DNA 序列数据集的情况下,决定是进行物种树分析还是物种网络分析至关重要。虽然已经提出了很多杂交检测方法,但很少有技术能在考虑数据生成机制的同时,在全局范围内进行杂交检测,从而实现杂交和不完全世系分类。在本文中,我们在一个统一的框架中考虑了杂交和凝聚,并提出了一种新的检验方法,可以检测任意大小的物种集合中是否存在杂交物种。基于这种杂交的全局检验,我们可以决定树状分析还是网络分析是否适合给定的数据集。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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