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罕见变异的祖先中反复出现的突变。

Recurrent mutation in the ancestry of a rare variant.

机构信息

Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.

Department of Mathematics, Indiana University, Bloomington, IN 47405, USA.

出版信息

Genetics. 2023 Jul 6;224(3). doi: 10.1093/genetics/iyad049.

Abstract

Recurrent mutation produces multiple copies of the same allele which may be co-segregating in a population. Yet, most analyses of allele-frequency or site-frequency spectra assume that all observed copies of an allele trace back to a single mutation. We develop a sampling theory for the number of latent mutations in the ancestry of a rare variant, specifically a variant observed in relatively small count in a large sample. Our results follow from the statistical independence of low-count mutations, which we show to hold for the standard neutral coalescent or diffusion model of population genetics as well as for more general coalescent trees. For populations of constant size, these counts are distributed like the number of alleles in the Ewens sampling formula. We develop a Poisson sampling model for populations of varying size and illustrate it using new results for site-frequency spectra in an exponentially growing population. We apply our model to a large data set of human SNPs and use it to explain dramatic differences in site-frequency spectra across the range of mutation rates in the human genome.

摘要

反复突变会产生相同等位基因的多个副本,这些副本可能在群体中共同分离。然而,等位基因频率或位点频率谱的大多数分析都假设观察到的等位基因的所有副本都可以追溯到单个突变。我们为稀有变异(即在大样本中观察到的相对较少数量的变异)的祖先中的潜在突变数量开发了一种抽样理论。我们的结果来自于低计数突变的统计独立性,我们证明了这种独立性适用于群体遗传学的标准中性合并或扩散模型,以及更一般的合并树。对于大小不变的种群,这些计数的分布类似于Ewens 抽样公式中的等位基因数量。我们为大小不断变化的种群开发了泊松抽样模型,并使用在指数增长种群中的位点频率谱的新结果来说明该模型。我们将该模型应用于大量人类 SNP 数据集,并使用它来解释人类基因组中突变率范围内的位点频率谱的巨大差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a74f/10324944/28e370fee4e6/iyad049f1.jpg

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