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利用突变选择模型推断长期有效种群大小。

Inferring Long-Term Effective Population Size with Mutation-Selection Models.

机构信息

Laboratoire de Biométrie et Biologie Évolutive UMR 5558, Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France.

École Normale Supérieure de Lyon, Université de Lyon, Université Lyon 1, Lyon, France.

出版信息

Mol Biol Evol. 2021 Sep 27;38(10):4573-4587. doi: 10.1093/molbev/msab160.


DOI:10.1093/molbev/msab160
PMID:34191010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8476147/
Abstract

Mutation-selection phylogenetic codon models are grounded on population genetics first principles and represent a principled approach for investigating the intricate interplay between mutation, selection, and drift. In their current form, mutation-selection codon models are entirely characterized by the collection of site-specific amino-acid fitness profiles. However, thus far, they have relied on the assumption of a constant genetic drift, translating into a unique effective population size (Ne) across the phylogeny, clearly an unrealistic assumption. This assumption can be alleviated by introducing variation in Ne between lineages. In addition to Ne, the mutation rate (μ) is susceptible to vary between lineages, and both should covary with life-history traits (LHTs). This suggests that the model should more globally account for the joint evolutionary process followed by all of these lineage-specific variables (Ne, μ, and LHTs). In this direction, we introduce an extended mutation-selection model jointly reconstructing in a Bayesian Monte Carlo framework the fitness landscape across sites and long-term trends in Ne, μ, and LHTs along the phylogeny, from an alignment of DNA coding sequences and a matrix of observed LHTs in extant species. The model was tested against simulated data and applied to empirical data in mammals, isopods, and primates. The reconstructed history of Ne in these groups appears to correlate with LHTs or ecological variables in a way that suggests that the reconstruction is reasonable, at least in its global trends. On the other hand, the range of variation in Ne inferred across species is surprisingly narrow. This last point suggests that some of the assumptions of the model, in particular concerning the assumed absence of epistatic interactions between sites, are potentially problematic.

摘要

突变-选择系统发育密码子模型基于群体遗传学的基本原理,代表了一种用于研究突变、选择和漂变之间错综复杂相互作用的原则性方法。在其当前形式中,突变-选择密码子模型完全由特定于位点的氨基酸适应度分布的集合来描述。然而,到目前为止,它们一直依赖于遗传漂变恒定的假设,这意味着整个系统发育过程中存在一个独特的有效种群大小 (Ne),显然这是一个不现实的假设。通过在谱系中引入 Ne 之间的变异,可以缓解这种假设。除了 Ne 之外,突变率 (μ) 也容易在谱系之间发生变化,并且两者都应该与生活史特征 (LHT) 相关。这表明该模型应该更全面地考虑所有这些谱系特定变量 (Ne、μ 和 LHT) 的联合进化过程。为此,我们引入了一个扩展的突变-选择模型,该模型通过贝叶斯蒙特卡罗框架共同重建了跨位点的适应度景观,以及 Ne、μ 和 LHT 沿着系统发育的长期趋势,使用 DNA 编码序列的比对和现存物种中观察到的 LHT 矩阵。该模型在模拟数据和哺乳动物、等足目动物和灵长类动物的实际数据上进行了测试。这些群体中重建的 Ne 历史似乎与 LHT 或生态变量相关,这表明重建至少在其总体趋势上是合理的。另一方面,跨物种推断的 Ne 变化范围令人惊讶地狭窄。最后这一点表明,该模型的一些假设,特别是关于位点之间不存在上位相互作用的假设,可能存在问题。

相似文献

[1]
Inferring Long-Term Effective Population Size with Mutation-Selection Models.

Mol Biol Evol. 2021-9-27

[2]
Reconstructing the History of Variation in Effective Population Size along Phylogenies.

Genome Biol Evol. 2021-8-3

[3]
Detecting consistent patterns of directional adaptation using differential selection codon models.

BMC Evol Biol. 2017-6-23

[4]
Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.

Mol Biol Evol. 2017-1

[5]
Reconstructing the phylogenetic history of long-term effective population size and life-history traits using patterns of amino acid replacement in mitochondrial genomes of mammals and birds.

Genome Biol Evol. 2013

[6]
Phylogenetic patterns of GC-biased gene conversion in placental mammals and the evolutionary dynamics of recombination landscapes.

Mol Biol Evol. 2012-10-18

[7]
A population genetics-phylogenetics approach to inferring natural selection in coding sequences.

PLoS Genet. 2011-12-1

[8]
Joint reconstruction of divergence times and life-history evolution in placental mammals using a phylogenetic covariance model.

Evolution. 2012-1-23

[9]
The erratic mitochondrial clock: variations of mutation rate, not population size, affect mtDNA diversity across birds and mammals.

BMC Evol Biol. 2009-3-10

[10]
Shifting Balance on a Static Mutation-Selection Landscape: A Novel Scenario of Positive Selection.

Mol Biol Evol. 2017-2-1

引用本文的文献

[1]
Effective population size does not explain long-term variation in genome size and transposable element content in animals.

Elife. 2025-7-18

[2]
Estimating the proportion of beneficial mutations that are not adaptive in mammals.

PLoS Genet. 2024-12-26

[3]
From teeth to pad: tooth loss and development of keratinous structures in sirenians.

Proc Biol Sci. 2023-11-29

[4]
Beginner's Guide on the Use of PAML to Detect Positive Selection.

Mol Biol Evol. 2023-4-4

[5]
Genes and sites under adaptation at the phylogenetic scale also exhibit adaptation at the population-genetic scale.

Proc Natl Acad Sci U S A. 2023-3-14

[6]
Jump-Chain Simulation of Markov Substitution Processes Over Phylogenies.

J Mol Evol. 2022-8

本文引用的文献

[1]
Reconstructing the History of Variation in Effective Population Size along Phylogenies.

Genome Biol Evol. 2021-8-3

[2]
How Much Does Vary Among Species?

Genetics. 2020-10

[3]
OrthoMaM v10: Scaling-Up Orthologous Coding Sequence and Exon Alignments with More than One Hundred Mammalian Genomes.

Mol Biol Evol. 2019-4-1

[4]
GC-biased gene conversion conceals the prediction of the nearly neutral theory in avian genomes.

Genome Biol. 2019-1-7

[5]
Life History Traits Impact the Nuclear Rate of Substitution but Not the Mitochondrial Rate in Isopods.

Mol Biol Evol. 2018-12-1

[6]
Protein evolution depends on multiple distinct population size parameters.

BMC Evol Biol. 2018-2-8

[7]
Sequence entropy of folding and the absolute rate of amino acid substitutions.

Nat Ecol Evol. 2017-10-23

[8]
TimeTree: A Resource for Timelines, Timetrees, and Divergence Times.

Mol Biol Evol. 2017-7-1

[9]
State aggregation for fast likelihood computations in molecular evolution.

Bioinformatics. 2017-2-1

[10]
Shifting Balance on a Static Mutation-Selection Landscape: A Novel Scenario of Positive Selection.

Mol Biol Evol. 2017-2-1

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