Ancestral character estimation under the threshold model from quantitative genetics

Evolutionary biology is a study of life's history on Earth. In researching this history, biologists are often interested in attempting to reconstruct phenotypes for the long extinct ancestors of living species. Various methods have been developed to do this on a phylogeny from the data for extant taxa. In the present article, I introduce a new approach for ancestral character estimation for discretely valued traits. This approach is based on the threshold model from evolutionary quantitative genetics. Under the threshold model, the value exhibited by an individual or species for a discrete character is determined by an underlying, unobserved continuous trait called “liability.” In this new method for ancestral state reconstruction, I use Bayesian Markov chain Monte Carlo (MCMC) to sample the liabilities of ancestral and tip species, and the relative positions of two or more thresholds, from their joint posterior probability distribution. Using data simulated under the model, I find that...

进化生物学（Evolutionary biology）是研究地球生命历史的学科。在研究这一历史时，生物学家常致力于重建现存物种已灭绝远祖的表型（phenotype）。科学家已开发出多种方法，利用现存类群（taxon）的数据在系统发育树（phylogeny）上实现这一目标。本文提出一种针对离散值性状的祖先特征估计（ancestral character estimation）新方法。该方法基于进化数量遗传学（evolutionary quantitative genetics）中的阈值模型（threshold model）。在阈值模型下，个体或物种表现出的离散特征值由一个潜在的、未观测到的连续性状决定，该性状被称为“liability”。在这种祖先状态重建（ancestral state reconstruction）新方法中，我采用贝叶斯马尔可夫链蒙特卡洛（Bayesian Markov chain Monte Carlo，MCMC）方法，从祖先物种与终端物种的liability值以及两个或多个阈值的相对位置的联合后验概率分布中进行抽样。通过模型下的模拟数据，我发现……