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非中性特征的祖先状态重建的准确性。

Accuracy of ancestral state reconstruction for non-neutral traits.

机构信息

School of Natural Sciences, University of Tasmania, Private Bag 55, Hobart, Tas, 7001, Australia.

School of Health Sciences, University of Tasmania, Private Bag 121, Hobart, Tas, 7001, Australia.

出版信息

Sci Rep. 2020 May 6;10(1):7644. doi: 10.1038/s41598-020-64647-4.

DOI:10.1038/s41598-020-64647-4
PMID:32376845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7203120/
Abstract

The assumptions underpinning ancestral state reconstruction are violated in many evolutionary systems, especially for traits under directional selection. However, the accuracy of ancestral state reconstruction for non-neutral traits is poorly understood. To investigate the accuracy of ancestral state reconstruction methods, trees and binary characters were simulated under the BiSSE (Binary State Speciation and Extinction) model using a wide range of character-state-dependent rates of speciation, extinction and character-state transition. We used maximum parsimony (MP), BiSSE and two-state Markov (Mk2) models to reconstruct ancestral states. Under each method, error rates increased with node depth, true number of state transitions, and rates of state transition and extinction; exceeding 30% for the deepest 10% of nodes and highest rates of extinction and character-state transition. Where rates of character-state transition were asymmetrical, error rates were greater when the rate away from the ancestral state was largest. Preferential extinction of species with the ancestral character state also led to higher error rates. BiSSE outperformed Mk2 in all scenarios where either speciation or extinction was state dependent and outperformed MP under most conditions. MP outperformed Mk2 in most scenarios except when the rates of character-state transition and/or extinction were highly asymmetrical and the ancestral state was unfavoured.

摘要

祖先状态重建所基于的假设在许多进化系统中都被违反了,特别是对于那些受到定向选择的特征。然而,对于非中性特征的祖先状态重建的准确性还了解甚少。为了研究祖先状态重建方法的准确性,我们使用广泛的特征状态相关的物种形成、灭绝和特征状态转换速率,在 BiSSE(二态物种形成和灭绝)模型下模拟了树和二元字符。我们使用最大简约法(MP)、BiSSE 和二态马尔可夫模型(Mk2)来重建祖先状态。在每种方法下,错误率随着节点深度、真实状态转换次数、状态转换和灭绝速率的增加而增加;在最深的 10%的节点和最高的灭绝和特征状态转换速率下,错误率超过了 30%。当特征状态转换速率不对称时,远离祖先状态的速率越大,错误率就越大。具有祖先特征的物种优先灭绝也会导致更高的错误率。BiSSE 在所有依赖于状态的物种形成或灭绝的情况下都优于 Mk2,并且在大多数情况下优于 MP。MP 在大多数情况下都优于 Mk2,除了在特征状态转换和/或灭绝速率非常不对称且祖先状态不利的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/10bd281db342/41598_2020_64647_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/1c2cdf8d00cc/41598_2020_64647_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/509ab3ae396a/41598_2020_64647_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/aea6097cc1fe/41598_2020_64647_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/7aa4dc3137c2/41598_2020_64647_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/88a2fd881fdd/41598_2020_64647_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/10bd281db342/41598_2020_64647_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/1c2cdf8d00cc/41598_2020_64647_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/509ab3ae396a/41598_2020_64647_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/aea6097cc1fe/41598_2020_64647_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/7aa4dc3137c2/41598_2020_64647_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/88a2fd881fdd/41598_2020_64647_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b659/7203120/10bd281db342/41598_2020_64647_Fig6_HTML.jpg

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