Bayesian inference of phylogenetic networks from bi-allelic genetic markers.
Bayesian inference of phylogenetic networks from bi-allelic genetic markers.
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来自双行遗传标记的系统发育网络的贝叶斯推断。
DOI：
10.1371/journal.pcbi.1005932
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发表时间：
2018-01
影响因子：
4.3
通讯作者：
Nakhleh L
中科院分区：
文献类型：
--
作者：
Zhu J;Wen D;Yu Y;Meudt HM;Nakhleh L
系统发育网络是有根的、有向的、无环的图，模拟网状进化历史。最近，设计了统计方法，用于从基因树估计或多个未连锁基因座的序列比对推断此类网络。双等位基因标记，尤其是单核苷酸多态性 (SNP) 和扩增片段长度多态性 (AFLP)，提供了全基因组数据的强大来源。在最近的一篇论文中，引入了一种称为 SNAPP 的方法，用于根据未连锁的双等位标记对物种树进行统计推断。该方法假设的生成过程结合了双等位基因标记的进化模型以及多物种合并模型。该方法的一个新颖组成部分是多项式时间算法，用于通过整合给定标记的所有可能的基因树来精确计算固定物种树的可能性。在这里，我们报告了一种根据双等位标记对系统发育网络进行贝叶斯推断的方法。我们的方法通过整合所有可能的基因树，显着扩展了精确计算系统发育网络可能性的算法。与物种树的情况不同，该算法不再是系统发育网络的所有实例上的多项式时间。此外，该方法利用可逆跳跃 MCMC 技术对给定双等位标记数据的系统发育网络的后部进行采样。我们的方法在准确性和鲁棒性方面具有非常好的性能，正如我们在模拟数据以及多个新西兰植物属（车前草科）物种的数据集上所证明的那样。我们在公开的开源 PhyloNet 软件包中实现了该方法。基因组数据的可用性彻底改变了进化史和系统发育推断的研究。在大多数情况下，从基因组数据推断进化历史需要考虑到这样一个事实：不同的基因组区域可能具有彼此不同的进化历史，以及与基因组采样的物种不同的进化历史。在本文中，我们介绍了一种推断进化历史的方法，同时考虑了可能引起基因组差异的两个过程，即不完整的谱系排序和杂交。我们引入了一种新的算法，用于根据双等位基因标记计算系统发育网络的可能性，并将其用于贝叶斯推理方法。对合成数据集和经验数据集的分析表明，该方法在获得的估计方面具有非常好的性能。
Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate evolutionary histories. Recently, statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci. Bi-allelic markers, most notably single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs), provide a powerful source of genome-wide data. In a recent paper, a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers. The generative process assumed by the method combined both a model of evolution for the bi-allelic markers, as well as the multispecies coalescent. A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker. Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers. Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees. Unlike the case of species trees, the algorithm is no longer polynomial-time on all instances of phylogenetic networks. Furthermore, the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data. Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data, as well as a data set of multiple New Zealand species of the plant genus Ourisia (Plantaginaceae). We implemented the method in the publicly available, open-source PhyloNet software package. The availability of genomic data has revolutionized the study of evolutionary histories and phylogeny inference. Inferring evolutionary histories from genomic data requires, in most cases, accounting for the fact that different genomic regions could have evolutionary histories that differ from each other as well as from that of the species from which the genomes were sampled. In this paper, we introduce a method for inferring evolutionary histories while accounting for two processes that could give rise to such differences across the genomes, namely incomplete lineage sorting and hybridization. We introduce a novel algorithm for computing the likelihood of phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference method. Analyses of synthetic and empirical data sets show a very good performance of the method in terms of the estimates it obtains.
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影响因子：
56.9
作者：
Rieseberg, LH;Raymond, O;Lexer, C
通讯作者：
Lexer, C
影响因子：
4.9
作者：
Stevison, Laurie S.;Kohn, Michael H.
通讯作者：
Kohn, Michael H.
DOI：
10.1126/science.1258524
发表时间：
2015-01-02
期刊：
Science (New York, N.Y.)
影响因子：
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作者：
Fontaine MC;Pease JB;Steele A;Waterhouse RM;Neafsey DE;Sharakhov IV;Jiang X;Hall AB;Catteruccia F;Kakani E;Mitchell SN;Wu YC;Smith HA;Love RR;Lawniczak MK;Slotman MA;Emrich SJ;Hahn MW;Besansky NJ
通讯作者：
Besansky NJ
影响因子：
4.5
作者：
Solís-Lemus C;Ané C
通讯作者：
Ané C
DOI：
10.1038/nrg3936
发表时间：
2015-06
期刊：
Nature reviews. Genetics
影响因子：
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作者：
通讯作者：
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