Computational methods for pandemic-scale genomic epidemiology

对传染性病原体基因组序列的系统发育分析可以揭示关于它们的进化和传播史的基本信息。以新冠肺炎大流行为例，这些分析和数据在流行病学中发挥着关键作用，对于跟踪和重建传染病在社区内和国家之间的传播至关重要；了解传播的动力学；估计控制措施的有效性；预测流行病学动态；以及监测病原体的演变，如发现新的SARS-CoV-2变异和令人担忧的变种所显示的那样。随着基因组测序技术的不断改进和广泛采用，基因组流行病学将成为关键的医疗资产。因此，基因组流行病学数据分析方法的改进不仅有助于我们应对目前的传染病流行，还将加强我们对未来大流行的准备。然而，目前对基因组流行病学数据的调查主要基于不符合其需要的计算方法，而是为进化生物学研究而开发的，在进化生物学研究中，通常考虑的基因组很少，差异很大。因此，对大型基因组序列数据集进行最理想的分析，例如新冠肺炎大流行期间出现的数据，目前是不可行的。在这个项目中，我们通过开发为大流行规模的基因组流行病学量身定做的计算方法来解决这一限制。这些方法将使对大型基因组流行病学数据集的准确实时分析成为可能。这些目标属于MRC的几个优先事项，如“全球健康”、“感染和免疫”、“抗菌素耐药性”和“生物医学和健康数据科学”。我们的具体目标是：1)开发基因组流行病学的算法。我们将开发新的算法来分析基因组序列数据。我们将利用基因组流行病学中的序列通常非常密切地相关，因此彼此非常相似的事实，来设计为该领域量身定做的算法和数学方法。根据我们过去的经验，我们预计这些方法的效率将比传统方法高数千倍：允许分析数百万而不是数千个基因组序列。2)提高真实性和准确性。高度可变的突变率和重复的序列错误虽然很常见，但在当前的基因组流行病学分析中会造成错误和不确定性。为了在不影响效率的情况下提高方法的准确性，我们将开发定制的基因组进化数学模型，考虑到基因组流行病学数据的这些复杂性。3)为更广泛的实施铺平道路。我们将开发一个高效的开放源码软件库，以便将我们的新方法轻松地整合到其他高影响力的遗传数据分析软件包中。这将允许我们的方法得到最广泛的应用，因为用户将能够采用这些方法进行各种分析，例如估计传播历史或及时识别疾病的变种。4)实现大流行规模的贝叶斯系统发育学。贝叶斯系统发育学是基因组流行病学中最先进的应用的核心，例如系统地理学(研究病原体在边界内和边界之间的传播)和系统动力学(研究病原体患病率随时间变化的研究)。我们将把我们的方法集成到广泛使用的贝叶斯系统发育包Beast中，以便能够分析数百万基因组的数据集。

Phylogenetic analyses of genome sequences from infectious pathogens can reveal essential information regarding their evolution and transmission history. As the COVID-19 pandemic exemplified, these analyses and data play a crucial role in epidemiology and are essential to track and reconstruct the spread of infectious disease within communities and between countries; to understand the dynamics of transmission; to estimate the efficacy of containment measures; to predict epidemiological dynamics; and to monitor pathogen evolution as showcased by the identification of new SARS-CoV-2 mutations and variants of concern.With ongoing improvement and widespread adoption of genome sequencing technologies, genomic epidemiology will become a key medical asset. Improvements to genomic epidemiological data analysis methods therefore will not only help us tackle ongoing infectious disease epidemics, but will also enhance our preparedness towards future pandemics.However, current investigations of genomic epidemiological data are predominantly based on computational methods that are not tailored to their needs, but rather were developed for evolutionary biology studies where typically few, highly diverged genomes are considered. Most desirable analyses of large genome sequence data sets, such as those that emerged during the COVID-19 pandemic, are thus currently unfeasible.In this project we address this limitation by developing computational methods tailored for pandemic-scale genomic epidemiology. These methods will enable accurate real-time analyses of large genomic epidemiological data sets. These objectives fall within several priorities of the MRC, such as "Global health", "Infections and immunity", "Antimicrobial resistance", and "Biomedical and health data science". Our specific aims are to:1) Develop algorithms for genomic epidemiology. We will develop new algorithms for analysing genome sequence data. We will exploit the fact that sequences in genomic epidemiology are typically very closely related, and thus very similar to each other, to devise algorithms and mathematical approaches tailored for this field. Based on our past experience, we expect these approaches to be thousands of times more efficient than traditional methods: allowing the analysis of millions rather than thousands of genome sequences.2) Increase realism and accuracy. Highly variable mutation rates and recurrent sequence errors, while common, cause errors and uncertainty in current genomic epidemiological analyses. To increase the accuracy of our methods without affecting their efficiency, we will develop bespoke mathematical models of genome evolution that take into account these complexities of genomic epidemiological data.3) Pave the way to wider implementation. We will develop an efficient open-source software library to easily integrate our new methods within other highly impactful software packages for the analysis of genetic data. This will allow the broadest application of our methods, as users will be able to adopt them for a variety of analyses, such the estimation of transmission histories or the timely identification of variants of concern.4) Enable pandemic-scale Bayesian phylogenetics. Bayesian phylogenetics is at the core of most advanced applications in genomic epidemiology, such as phylogeography (the study of the spread of pathogens within and between borders) and phylodynamics (the study of pathogen prevalence changes through time). We will integrate our methods within the widely used Bayesian phylogenetic package BEAST to allow the analysis of data sets of millions of genomes.