黑腹果蝇的核苷酸多样性景观是由突变率变化形成的

Gustavo V Barroso, Julien Y Dutheil
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引用次数: 0

摘要

是什么塑造了核苷酸多样性沿基因组的分布?试图回答这个问题引发了关于中性随机过程和自然选择在分子进化中的作用的争论。然而,进化机制不是孤立的,缺乏同时考虑多种因素对多样性影响的综合模型;如果没有它们,混杂因素就会潜伏在估计中。在这里,我们提出了一种新的统计方法,联合推断家谱、重组率和突变率的基因组景观。在此过程中,我们的模型捕捉到了遗传漂变、连锁选择和局部突变率对基因组变异模式的影响。然后,我们形式化了这些微观进化机制如何相互作用的因果模型,并将其作为线性回归来估计它们对基因组多样性水平的个体贡献。我们的分析重申了黑腹果蝇的连锁选择特征,但我们估计突变景观是该物种全基因组多样性分布的主要驱动因素。此外,我们的模拟结果表明,在许多进化情景中,突变景观将是塑造多样性的关键因素,这主要取决于基因组窗口的大小。我们认为,将突变率变异纳入分子进化的零模型将导致种群基因组学中更现实的推论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The landscape of nucleotide diversity in Drosophila melanogaster is shaped by mutation rate variation
What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. We then formalize a causal model of how these micro-evolutionary mechanisms interact, and cast it as a linear regression to estimate their individual contributions to levels of diversity along the genome. Our analyses reclaim the well-established signature of linked selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the genome-wide distribution of diversity in this species. Furthermore, our simulation results suggest that in many evolutionary scenarios the mutation landscape will be a crucial factor shaping diversity, depending notably on the genomic window size. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more realistic inferences in population genomics.
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