Generative adversarial networks for demographic inferences of nonmodel species from genomic data

根据基因组数据对非模型物种进行人口统计推断的生成对抗网络

• 批准号: NE/X009637/1
• 负责人: Matteo Fumagalli
• 金额: $ 9.77万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX009637%2F1
• 关键词: Generative; adversarial; networks; demographic; inferences

Understanding the temporal and geographic movement of populations is vital to address key questions in evolutionary and conservation biology. Whilst the generation of high-throughput genomic data enabled the inference of population genomic parameters at unprecedented rate, large-scale datasets also prompted the development of novel computational techniques. In recent years, the predictive power provided by machine learning algorithms, in particular deep learning, has led to breakthrough discoveries in many disciplines. Nevertheless, the application of deep learning in evolutionary genomics is still in its infancy. Deep learning algorithms exhibits several advantages over commonly-used inferential approaches in population genomics, as they can handle large data sets with minimal compression and are theoretically universal approximators of arbitrarily complex models.The intrinsic statistical uncertainty associated with genomic sequencing data, the lack of natural training data sets, and the computational resources needed have hampered the exploitation of these powerful techniques to generate novel findings in evolutionary biology. These challenges are particularly prominent in the study of nonmodel species, where prior knowledge of key parameters is typically missing.A promising strategy to partly overcome such barriers is given by the recent application of Generative Adversarial Networks (GANs), a branch of deep learning methods, which have been successfully applied to generate artificial genomes and estimate cryptic evolutionary parameters. GANs consist of two deep neural networks which are trained together and, at the end, the algorithm generates simulations that are indistinguishable from real examples (as in the case of "Deepfake" methods in Artificial Intelligence). Thus, the final simulator provides estimates of model parameters.In this project, we aim to to pilot the design, implementation, and deployment of a novel GAN architetcure for population genomic data. As an illustration, we will focus on the inference on demographic parameters, , including temporal changes in population size and migration rate, describing the recent evolution of Anopheles mosquito populations among three villages in Burkina Faso. As the first objective, we will adapt a recently proposed GAN architecture for population genomic data to incorporate multiple populations with unequal sizes. As the second objective, we will train the algorithm by integrating simulations with extensive genomic data from Anopheles mosquito populations. We will include a significant technological advance by integrating a model selection step to discriminate among competing evolutionary scenarios.By estimating the migration rate of mosquito populations among villages, we will be able to assist predictions on the spread of resistance mutations and support molecular surveillance and intervention strategies at local scale. In fact, it is still unclear to what extent resistant mutations can spread across the entire continent as different studies have led to contrasting findings on the extent of migration between Anopheles populations. Upon completion of this pilot study, we will be able to scale the deep learning algorithm to all available mosquito populations from sub-Saharan Africa and infer gene flow at the continental scale.Additionally, the novel deep learning framework will be applicable to all mutations potentially associated with resistance or other notable phenotypes. It can be further extended to model complex modes of adaptation (e.g. via introgression or polygenic adaptation) and to other species of importance.

了解种群的时间和地理移动对于解决进化和保护生物学中的关键问题至关重要。虽然高通量基因组数据的产生使得能够以前所未有的速度推断群体基因组参数，但大规模数据集也促进了新型计算技术的发展。近年来，机器学习算法（特别是深度学习）提供的预测能力在许多学科中带来了突破性的发现。然而，深度学习在进化基因组学中的应用仍处于起步阶段。与群体基因组学中常用的推理方法相比，深度学习算法具有几个优势，因为它们可以以最小的压缩处理大型数据集，并且在理论上是任意复杂模型的通用近似器。所需的计算资源阻碍了利用这些强大的技术在进化生物学中产生新的发现。这些挑战在非模型物种的研究中尤为突出，其中关键参数的先验知识通常是缺失的。最近应用生成对抗网络（GANs）提供了一种有希望的策略，可以部分克服这些障碍，GANs是深度学习方法的一个分支，已成功应用于生成人工基因组和估计隐藏的进化参数。GAN由两个一起训练的深度神经网络组成，最后，该算法生成与真实的示例无法区分的模拟（如人工智能中的“Deepfake”方法）。因此，最终的模拟器提供了模型参数的估计。在这个项目中，我们的目标是试点设计，实施和部署一个新的GAN架构的人口基因组数据。作为一个例子，我们将集中在人口参数的推断，包括人口规模和迁移率的时间变化，描述了在布基纳法索的三个村庄之间的按蚊种群的最近演变。作为第一个目标，我们将调整最近提出的GAN架构，用于人口基因组数据，以整合大小不等的多个人口。作为第二个目标，我们将通过将模拟与按蚊种群的广泛基因组数据相结合来训练算法。我们将通过整合模型选择步骤来区分竞争的进化情景，从而实现重大的技术进步。通过估计村庄之间蚊子种群的迁移率，我们将能够帮助预测耐药突变的传播，并支持局部规模的分子监测和干预策略。事实上，目前还不清楚耐药突变在多大程度上可以在整个大陆传播，因为不同的研究导致了对按蚊种群之间迁移程度的对比发现。在完成这项试点研究后，我们将能够将深度学习算法扩展到撒哈拉以南非洲的所有可用蚊子种群，并推断大陆规模的基因流。此外，新的深度学习框架将适用于所有可能与耐药性或其他显著表型相关的突变。它可以进一步扩展到模拟复杂的适应模式（例如通过渐渗或多基因适应）和其他重要物种。