Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models
Quantifying GC-Biased Gene Conversion in Great Ape Genomes Using Polymorphism-Aware Models
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DOI：
10.1534/genetics.119.302074
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发表时间：
2019-08-01
期刊：
影响因子：
3.3
通讯作者：
Kosiol, Carolin
中科院分区：
文献类型：
--
作者：
Borges, Rui;Szollosi, Gergely J.;Kosiol, Carolin
随着多个体种群规模数据的获得，需要更复杂的建模策略来量化核苷酸使用的全基因组模式和相关的进化机制。最近，人们提出了多元中立型Moran模型。然而，它不足以解释等位基因在类人猿中的分布。在这里，我们提出了一个包含等位基因选择的新模型。我们的理论结果构成了一个新的贝叶斯框架的基础，该框架从人口数据中估计突变率和选择系数。我们将新的框架应用于一个大型类人猿数据集，在那里我们发现了与全基因组GC偏向基因转换(GBGC)相匹配的等位基因选择模式。特别是，我们发现大猩猩有不同强度的等位基因选择模式--我们将这一特征与大猩猩不同的人口统计学相关联。我们还证明，AT/GC切换效应降低了替换的可能性，促进了类人猿基因组碱基组成的更多多态。我们进一步评估了GC偏差在分子分析中的影响，发现当GBGC没有被正确解释时，突变率和遗传距离是在偏差下估计的。我们的结果有助于讨论GBGC的进化速度和模式，同时强调了在种群遗传学和系统发育中需要GBGC感知模型。
As multi-individual population-scale data become available, more complex modeling strategies are needed to quantify genome-wide patterns of nucleotide usage and associated mechanisms of evolution. Recently, the multivariate neutral Moran model was proposed. However, it was shown insufficient to explain the distribution of alleles in great apes. Here, we propose a new model that includes allelic selection. Our theoretical results constitute the basis of a new Bayesian framework to estimate mutation rates and selection coefficients from population data. We apply the new framework to a great ape dataset, where we found patterns of allelic selection that match those of genome-wide GC-biased gene conversion (gBGC). In particular, we show that great apes have patterns of allelic selection that vary in intensity-a feature that we correlated with great apes' distinct demographies. We also demonstrate that the AT/GC toggling effect decreases the probability of a substitution, promoting more polymorphisms in the base composition of great ape genomes. We further assess the impact of GC-bias in molecular analysis, and find that mutation rates and genetic distances are estimated under bias when gBGC is not properly accounted for. Our results contribute to the discussion on the tempo and mode of gBGC evolution, while stressing the need for gBGC-aware models in population genetics and phylogenetics.