A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.
A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.
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一种深度学习的方法，用于从祖先迁移图中推断选择性扫描。
DOI：
10.1093/molbev/msab332
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发表时间：
2022-01-07
影响因子：
10.7
通讯作者：
Siepel A
中科院分区：
文献类型：
--
作者：
Hejase HA;Mo Z;Campagna L;Siepel A
从基因组数据中检测选择信号是群体遗传学的中心问题。将祖先重组图（ARG）中的丰富信息与强大且可扩展的深度学习框架相结合，我们开发了一种检测和量化正选择的新颖方法：使用祖先重组图（SIA）进行选择推理。 SIA 建立在长短期记忆 (LSTM) 架构（一种特定类型的循环神经网络 (RNN)）的基础上，可以通过训练来明确推断全范围的选择系数，以及等位基因频率轨迹和选择开始时间。我们在欧洲人口统计模型下的模拟上对 SIA 进行了广泛的基准测试，发现它的性能与一些最佳可用方法一样好甚至更好，包括最先进的机器学习和基于 ARG 的方法。此外，我们使用 SIA 来估计与感兴趣的人类表型相关的几个位点的选择系数。 SIA 在 MC1R 和 ABCC11 位点检测到欧洲 (CEU) 人群特有的新选择信号。此外，它还概括了 LCT 基因座和几个色素沉着相关基因的选择信号。最后，我们重新分析了最近辐射的孢子属南卡布奇诺食籽类群的多态性数据，以量化选择的强度，并提高了我们以前的方法检测部分软扫描的能力。总体而言，SIA 使用深度学习来利用 ARG，从而为选择性扫描如何塑造基因组多样性提供了新的见解。
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.
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