Scalable Inference in Statistical Models of Viral Evolution and Human Health

项目摘要/摘要 尽管全球公共卫生取得了进展，但病毒仍然是对人类健康的主要威胁， 国家和国际。最近和持续爆发的SARS-CoV-2、埃博拉、寨卡、拉沙热和 基孔肯雅热以及艾滋病毒等持续流行病强调了了解病毒 流行病期间的病毒进化和病毒-宿主相互作用。病毒进化的系统发育统计模型提供了 这是研究病毒遗传学与环境或宿主因素之间相互作用的有力工具。然而，在这方面， 目前的系统发育模型往往过于灵活，无法真实地模拟这些关系， Do对于甚至中等大小的数据集在计算上是难以处理的。该项目旨在开发新的 这些统计模型既足够灵活，可以模拟复杂的生物关系，又可以扩展到大型 病毒和宿主特征的数据集。第一个目标是开发更有效和更少偏见的统计方法 用于估计病毒表型的遗传性（例如病毒载量、宿主CD 4 T细胞计数、复制能力）。 当前的统计实践通常会产生有偏见的遗传力估计，并且对于大数据来说很棘手 集.该项目旨在扩展最先进的推理技术，以模拟病毒表型的遗传性， 类型（实现无偏和有效的推理），并应用这些新方法来更好地估计 HIV-1病毒载量的遗传性。第二个目标是开发研究复杂的， 高维度病毒表型，如感染严重程度，无法通过单一措施捕获- 我是说。这些表型由于其固有的复杂性而难以量化，混淆了严格的努力 比如说，识别异常致命的病毒分支。虽然系统发育因子分析可以识别和 由于高维表型的量化，它对大数据集的扩展性很差。我们提出了新的推论 技术，解决这些可扩展性问题，并允许以前棘手的分析。我们计划申请 这些新方法可以研究埃博拉和拉沙热的毒力模式， 病毒株此外，这些方法非常适合于鉴定病毒mu-之间的上位相互作用。 我们计划在HIV、Zika和Chikungunya中探索这些相互作用， 病毒第三个目标是开发新的统计模型，专门用于预测病毒感染的结果。 从病毒序列数据中获取感染信息。为了适应这些模型所要求的必要的可扩展性，我们 开发新的推理策略，既高度概括（即他们不依赖于严格的假设 在现有的模型中）和计算效率。强大的预测性能将使研究人员或 临床医生使用病毒序列预测临床相关结果，这可能有助于指导治疗。我们 将使用上述埃博拉和拉沙热数据评估这些方法。

Project Summary / Abstract Despite global public health advances, viruses remain a major threat to human health both in the United States and internationally. Recent and continuing outbreaks of SARS-CoV-2, Ebola, Zika, Lassa fever, and Chikungunya, as well as persistent epidemics such as HIV have emphasized the need to understand viral evolution and virus-host interactions during epidemics. Phylogenetic statistical models of viral evolution offer a powerful tool for studying the interplay between viral genetics and environmental or host factors. However, current phylogenetic models are often too inflexible to realistically model these relationships, and those that do are computationally intractable for even moderately sized data sets. This project aims to develop new statistical models that are both flexible enough to model complex biological relationships and scalable to large data sets of viral and host traits. The first aim is to develop more efficient and less biased statistical methods for estimating the heritability of viral phenotypes (e.g. viral load, host CD4 T-cell count, replicative capacity). Current statistical practices typically produced biased heritability estimates and are intractable for large data sets. This project seeks to extend state-of-the-art inference techniques to model the heritability of viral pheno- types (enabling both unbiased and efficient inference) and to apply these new methods to better estimate the heritability of viral load in HIV-1. The second aim seeks to develop statistical methods for studying complex, high-dimensional viral phenotypes such as infection severity which cannot be captured with a single measure- ment. These phenotypes are difficult to quantify due to their inherent complexity, confounding rigorous efforts at, say, identifying unusually virulent viral clades. While phylogenetic factor analysis enables identification and quantification of high-dimensional phenotypes, it scales poorly to large data sets. We propose new inference techniques that address these scalability problems and allow previously intractable analyses. We plan to apply these new methods to study patterns of virulence in Ebola and Lassa fever and to identify unusually virulent viral strains. Additionally, these methods are well suited to identifying epistatic interactions between viral mu- tations and phenotypes of interest, and we plan to explore these interactions in HIV, Zika, and Chikungunya viruses. The third aim is to develop new statistical models specifically designed to predict outcomes of viral infections from viral sequence data. To accommodate the necessary flexibility required by these models, we develop new inference strategies that are both highly generalizable (i.e. they do not rely on strict assumptions in existing models) and computationally efficient. Strong predictive performance would enable researchers or clinicians to predict clinically relevant outcomes using viral sequences, which could help inform treatment. We will evaluate these methods using the Ebola and Lassa fever data from mentioned above.