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The role of positive selection in molecular evolution: Alternative models for within-locus selective effects.

机译:正选择在分子进化中的作用:位点内选择效应的替代模型。

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摘要

A key question in population genetics is the extent to which positive selection drives molecular evolution. According to the selectionist viewpoint, evolution at the molecular level occurs by natural selection acting on DNA sequence mutations, with selectively favorable mutations more likely to eventually reach fixation in a species. On the other hand, the neutral theory of evolution postulates that random genetic drift, not selection, is the major driving force behind evolution at the molecular level.;Here, we address this question within a Poisson random field framework, based on aligned DNA sequence data from two closely related species. We investigate heavy-tailed distributions for within-locus selection coefficients, specifically a double-exponential and a Student's t distribution. Using Markov chain Monte Carlo methods on a set of coding sequences from 91 autosomal genes in Drosophila melanogaster and Drosophila simulons, we estimate that while few (∼ 10%) new mutations are beneficial, many 40%) of observed polymorphic sites and most (∼ 95%) fixations are due to positive selection. While these results are necessarily model-dependent, they are in accord with previous estimates based on a normal random effects model.;As a second aim, we develop programs for forward simulation of polymorphism and divergence data under varying levels of selection and recombination to investigate the performance of the Poisson random field model when the assumption of independence between polymorphic sites is not met. We find slightly diminished performance of the model under tight linkage and examine the reasons why this may be so.
机译:群体遗传学中的一个关键问题是,积极选择在多大程度上推动了分子进化。根据选择主义者的观点,分子水平上的进化是通过自然选择作用于DNA序列突变而发生的,选择性有利的突变更可能最终在一个物种中固定。另一方面,进化的中性理论假设随机遗传漂移而不是选择是分子水平上进化背后的主要驱动力;在这里,我们基于对齐的DNA序列在Poisson随机场框架内解决这个问题来自两个密切相关物种的数据。我们调查就位选择系数的重尾分布,特别是双指数分布和学生t分布。在果蝇和果蝇模拟物中的91个常染色体基因的编码序列集上使用马尔可夫链蒙特卡罗方法,我们估计虽然很少(〜10%)新突变是有益的,但许多(40%)的观察到的多态性位点和大多数(〜 95%)的注视归因于积极的选择。虽然这些结果必须与模型相关,但它们与基于正常随机效应模型的先前估计相符。作为第二个目标,我们开发了用于在不同选择和重组水平下对多态性和散度数据进行正向仿真的程序,以研究当不满足多态位点之间的独立性假设时,泊松随机场模型的性能。我们发现紧密联系下模型的性能略有下降,并研究了可能如此的原因。

著录项

  • 作者

    Abel, Haley Jane.;

  • 作者单位

    Washington University in St. Louis.;

  • 授予单位 Washington University in St. Louis.;
  • 学科 Biology Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 51 p.
  • 总页数 51
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;统计学;
  • 关键词

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