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，但在美国和美国，病毒仍然是对人类健康的主要威胁 国家和国际上都是如此。最近和持续爆发的SARS-CoV-2、埃博拉、寨卡病毒、拉沙热和 基孔肯雅热以及艾滋病毒等持续流行强调了了解病毒的必要性 流行期间的进化和病毒与宿主的相互作用。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和环境或宿主因素之间相互作用的有力工具。然而， 当前的系统发育模型通常太过于fl，无法现实地模拟这些关系，以及那些 即使对于中等大小的数据集，DO在计算上也是难以处理的。该项目旨在开发新的 统计模型既具有足够的fl灵活性，可以对复杂的生物关系进行建模，又可以扩展到大型 病毒和宿主特征的数据集。fi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力(例如，病毒载量、宿主CD4T细胞计数、复制能力)。 目前的统计方法通常会产生有偏见的遗传力估计，并且难以处理大数据 布景。这个项目试图扩展最先进的推理技术来模拟病毒表型的遗传性。 类型(支持无偏和有效的fi推理)，并应用这些新方法来更好地估计 HIV-1病毒载量的遗传度。第二个目标是发展研究复杂性的统计方法， 高维病毒表型，如感染严重程度，不能用单一衡量标准来衡量- 门槛。由于其内在的复杂性，这些表型很难量化，这与严格的努力相混淆。 比如，识别异常致命的病毒分支。而系统发育因子分析使身份fi阳离子和 量子fi阳离子的高维表型，它的伸缩性很差，以大型数据集。我们提出了新的推论 解决这些可伸缩性问题并允许进行以前难以处理的分析的技术。我们计划申请 这些新方法用于研究埃博拉和拉沙热的毒力模式，并识别异常毒力 病毒株。此外，这些方法非常适合于识别病毒与病毒之间的上位性相互作用。 我们计划探索HIV、寨卡病毒和基孔肯雅病毒之间的这些相互作用 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。fi 来自病毒序列数据的感染。为了适应这些模型所需的fl灵活性，我们 开发高度概化的新推理策略(即它们不依赖于严格的假设 在现有模型中)，并且在计算上是fi有效的。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 将使用上面提到的埃博拉和拉沙热数据来评估这些方法。

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.