Signature-Informed Selection Detection: A Novel Method for Multi-Locus Temporal Population Genetic Model with Recombination
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基于路径签名的选择检测：含重组的多位点时间种群遗传模型
- 研究问题
- 多个相互连锁的受选择位点同时存在时，如何利用等位基因频率的时间轨迹推断选择系数，并处理重组带来的复杂性？
- 方法与思路
- 作者构造广义贝叶斯评分规则后验，以路径签名核衡量时间轨迹，通过模拟无偏估计评分并用伪边际MCMC采样。
- 材料陈述的贡献
- 摘要称方法在若干多位点模拟中显示推断效能，并扩展至频率依赖选择及初始频率联合推断，提供两个真实数据示例。
- 阅读关注点
- 核对签名核与评分后验的定义、无偏模拟估计及采样收敛条件，并检查不同遗传模型下的基准比较和计算成本。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未核实；摘要未说明后验校准、模拟配置和数值优势，应用范围属于种群遗传学。
原文摘要
In population genetics, there is often interest in inferring selection coefficients. This task becomes more challenging if multiple linked selected loci are considered simultaneously. For such a situation, we propose a novel generalized Bayesian framework where we compute a scoring rule posterior for the selection coefficients in multi-locus temporal population genetics models. As we consider trajectories of allele frequencies over time as our data, we choose to use a signature kernel scoring rule - a kernel scoring rule defined for high-dimensional time-series data using iterated path integrals of a path (called signatures). We can compute an unbiased estimate of the signature kernel score using model simulations. This enables us to sample asymptotically from the signature kernel scoring rule posterior of the selection coefficients using pseudo-marginal MCMC-type algorithms. Through a simulation study, we were able to show the inferential efficacy of our method compared to existing benchmark methods for two and three selected locus scenarios under the standard Wright-Fisher model with recombination and selection. We also consider a negative frequency-dependent selection model for one and two locus scenarios, and also joint inference of selection coefficients and initial haplotype frequencies under the standard Wright-Fisher model. Finally, we illustrate the application of our inferential method for two real-life dataset. More specifically, we consider a data set on Yeast, as well as data from an Evolve and Resequence (E\&R) experiment on {\em Drosophila simulans}.