Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach
Inference of population admixture network from local gene genealogies: a coalescent-based maximum likelihood approach
复制标题
DOI：
10.1093/bioinformatics/btaa465
复制
发表时间：
2020-07-01
期刊：
影响因子：
5.8
通讯作者：
Wu, Yufeng
中科院分区：
文献类型：
--
作者：
Wu, Yufeng
动机：种群杂合是种群遗传学中的一个重要课题。从种群遗传数据出发，在所谓的混合网络模型下，利用混合推断种群人口历史是遗传学中一个既定的问题。现有的混合网络推理方法适用于单基因多态性。虽然这些方法通常非常快，但它们没有充分利用群体遗传数据中包含的信息[例如连锁不平衡（LD）]。结果：本文提出了一种新的外加剂网络推理方法GTmix。与现有的方法不同，GTmix适用于可以从群体单倍型推断出的本地基因谱系。局部基因谱系代表了样本单倍型的进化史，并包含LD信息。GTmix基于众所周知的多物种聚结（MSC）模型，对混合网络进行基于聚结的最大似然推断，并推断出局部谱系。GTmix利用多种技术加快了MSC模型的似然计算和最优网络搜索速度。我们的模拟表明，GTmix可以用更小的数据推断出比现有方法更准确的外加剂网络，即使这些现有方法给出了更大的数据。GTmix的效率相当高，可以分析当前感兴趣的群体遗传数据集。
Motivation: Population admixture is an important subject in population genetics. Inferring population demographic history with admixture under the so-called admixture network model from population genetic data is an established problem in genetics. Existing admixture network inference approaches work with single genetic polymorphisms. While these methods are usually very fast, they do not fully utilize the information [e.g. linkage disequilibrium (LD)] contained in population genetic data.Results: In this article, we develop a new admixture network inference method called GTmix. Different from existing methods, GTmix works with local gene genealogies that can be inferred from population haplotypes. Local gene genealogies represent the evolutionary history of sampled haplotypes and contain the LD information. GTmix performs coalescent-based maximum likelihood inference of admixture networks with inferred local genealogies based on the well-known multispecies coalescent (MSC) model. GTmix utilizes various techniques to speed up the likelihood computation on the MSC model and the optimal network search. Our simulations show that GTmix can infer more accurate admixture networks with much smaller data than existing methods, even when these existing methods are given much larger data. GTmix is reasonably efficient and can analyze population genetic datasets of current interests.