Suppr超能文献

评估研究性染色体重组停止时间的系统发育方法。

Assessment of phylogenetic approaches to study the timing of recombination cessation on sex chromosomes.

机构信息

Department of Biology, Lund University, Lund, Sweden.

出版信息

J Evol Biol. 2022 Dec;35(12):1721-1733. doi: 10.1111/jeb.14068. Epub 2022 Jul 27.

Abstract

The evolution of sex chromosomes is hypothesized to be punctuated by consecutive recombination cessation events, forming "evolutionary strata" that ceased to recombine at different time points. The demarcation of evolutionary strata is often assessed by estimates of the timing of recombination cessation (t ) along the sex chromosomes, commonly inferred from the level of synonymous divergence or with species phylogenies at gametologous (X-Y or Z-W) sequence data. However, drift and selection affect sequences unpredictably and introduce uncertainty when inferring t . Here, we assess two alternative phylogenetic approaches to estimate t ; (i) the expected likelihood weight (ELW) approach that finds the most likely topology among a set of hypothetical topologies and (ii) the BEAST approach that estimates t with specified calibration priors on a reference species topology. By using Z and W gametologs of an old and a young evolutionary stratum on the neo-sex chromosome of Sylvioidea songbirds, we show that the ELW and BEAST approaches yield similar t estimates, and that both outperform two frequently applied approaches utilizing synonymous substitution rates (dS) and maximum likelihood (ML) trees, respectively. Moreover, we demonstrate that both ELW and BEAST provide more precise t estimates when sequences of multiple species are included in the analyses.

摘要

性染色体的进化被假设为由连续的重组停止事件所打断,形成了“进化层”,这些进化层在不同的时间点停止重组。进化层的划分通常通过估计性染色体上重组停止的时间(t)来评估,通常从同义分歧水平或通过配子同源序列(X-Y 或 Z-W)数据的物种系统发育来推断。然而,漂移和选择不可预测地影响序列,并在推断 t 时引入不确定性。在这里,我们评估了两种替代的系统发育方法来估计 t:(i)期望似然权重(ELW)方法,该方法在一组假设拓扑结构中找到最可能的拓扑结构;(ii)BEAST 方法,该方法在参考物种拓扑结构上指定校准先验估计 t。通过使用新旧进化层的 Sylvioidea 雀形目鸟类的neo-性染色体的 Z 和 W 配子同源物,我们表明 ELW 和 BEAST 方法产生相似的 t 估计值,并且这两种方法都优于分别利用同义替代率(dS)和最大似然(ML)树的两种常用方法。此外,我们证明了当将多个物种的序列纳入分析时,ELW 和 BEAST 都提供了更精确的 t 估计值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b10/10086819/3d0b0486a8e6/JEB-35-1721-g003.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验