Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA.
Bioinformatics. 2020 Jul 1;36(Suppl_1):i326-i334. doi: 10.1093/bioinformatics/btaa465.
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.
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.
The program GTmix is available for download at: https://github.com/yufengwudcs/GTmix.
Supplementary data are available at Bioinformatics online.
群体混合是群体遗传学中的一个重要课题。从群体遗传数据中推断具有混合的所谓混合网络模型的群体人口历史是遗传学中的一个既定问题。现有的混合网络推断方法适用于单一遗传多态性。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）]。
在本文中，我们开发了一种新的混合网络推断方法，称为 GTmix。与现有方法不同，GTmix 适用于可以从群体单体型推断出的局部基因系统发生。局部基因系统发生代表了抽样单体型的进化历史，并包含 LD 信息。GTmix 根据著名的多物种合并（MSC）模型，基于推断出的局部系统发生，对混合网络进行基于合并的最大似然推断。GTmix 利用各种技术来加速 MSC 模型上的似然计算和最优网络搜索。我们的模拟表明，GTmix 可以比现有方法推断出更准确的混合网络，即使现有方法获得了更大的数据量。GTmix 的效率相当高，可以分析当前感兴趣的群体遗传数据集。
程序 GTmix 可在以下网址下载：https://github.com/yufengwudcs/GTmix。
补充数据可在 Bioinformatics 在线获取。
