Full Bayesian Comparative Phylogeography from Genomic Data.
Full Bayesian Comparative Phylogeography from Genomic Data.
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来自基因组数据的全贝叶斯比较植物地理学。
DOI：
10.1093/sysbio/syy063
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发表时间：
2019-05-01
影响因子：
6.5
通讯作者：
Oaks JR
中科院分区：
文献类型：
--
作者：
Oaks JR
理解生物多样性的一个挑战是解释导致多个共同分布的谱系共同物种形成的群落规模过程。这样的过程预测了非独立的、暂时聚集的跨分类群的分化。近似似然贝叶斯计算（ABC）方法从比较遗传数据中推断出这种模式，对先前的假设非常敏感，并且经常偏向于估计共享差异。我们引入了一个全似然贝叶斯方法，生态，它充分利用了基因组数据中的信息。通过对基因树进行解析积分，我们能够直接从基因组数据中计算种群历史的似然，并通过马尔可夫链蒙特卡罗算法有效地对模型平均后验进行采样。通过模拟，我们发现新方法比现有的近似似然方法更准确和精确地估计跨种群对的发散事件的数量和时间。我们的全贝叶斯方法所需的计算时间也比现有的ABC方法少几个数量级。我们发现，尽管假设了非连锁的字符（例如，非连锁的单核苷酸多态性），如果为了保留整个连锁位点的恒定字符而违反了这一假设，则新方法表现更好。事实上，保留不变的特征使得新方法能够在面对字符获取偏差时以高后验概率稳健地估计正确的分歧事件数量，而字符获取偏差通常困扰着从减少表示的基因组文库中聚集的位点。我们将我们的方法应用于来自菲律宾的四对岛屿种群的壁虎蜥蜴的基因组数据，这些种群预计不会共分化。尽管所有四对都是最近才开始发散的，但我们的方法强烈支持它们是独立发散的，并且这些结果对于非常不同的先前假设是稳健的。
A challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian approach, ecoevolity, which takes full advantage of information in genomic data. By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estimating the number and timing of divergence events across pairs of populations than existing approximate-likelihood methods. Our full Bayesian approach also requires several orders of magnitude less computational time than existing ABC approaches. We find that despite assuming unlinked characters (e.g., unlinked single-nucleotide polymorphisms), the new method performs better if this assumption is violated in order to retain the constant characters of whole linked loci. In fact, retaining constant characters allows the new method to robustly estimate the correct number of divergence events with high posterior probability in the face of character-acquisition biases, which commonly plague loci assembled from reduced-representation genomic libraries. We apply our method to genomic data from four pairs of insular populations of Gekko lizards from the Philippines that are not expected to have co-diverged. Despite all four pairs diverging very recently, our method strongly supports that they diverged independently, and these results are robust to very disparate prior assumptions.
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影响因子：
2.7
作者：
Harvey MG;Judy CD;Seeholzer GF;Maley JM;Graves GR;Brumfield RT
通讯作者：
Brumfield RT
影响因子：
3.3
作者：
Oaks, Jamie R.;Linkem, Charles W.;Sukumaran, Jeet
通讯作者：
Sukumaran, Jeet
影响因子：
6.5
作者：
Huelsenbeck, JP;Rannala, B
通讯作者：
Rannala, B
影响因子：
2.4
作者：
Neal, RM
通讯作者：
Neal, RM
影响因子：
3.3
作者：
Hickerson, Michael J.;Stone, Graham N.;Takebayashi, Naoki
通讯作者：
Takebayashi, Naoki