Scalable Inference in Statistical Models of Viral Evolution and Human Health

病毒进化和人类健康统计模型中的可扩展推理

基本信息

批准号：
10394133
负责人：
Gabriel William Hassler
金额：
$ 2.58万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2022-11-06
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10394133
关键词：
Address Area Biological CD4 Positive T Lymphocytes COVID-19 outbreak Cell Count Characteristics Chikungunya virus Clinical Clinical Data Complex Dangerousness Data Data Set Diffusion Dimensions Disease Outcome Ebola Ebola Hemorrhagic Fever Environmental Risk Factor Epidemic Evolution Factor Analysis Future Genetic Determinism Genome Genotype Gold HIV HIV-1 Health Heritability Human Immune response Immune system Individual Infection Integration Host Factors International Knowledge Lassa Fever Maps Measurable Measurement Measures Methods Modeling Mutation Outcome Patients Pattern Performance Phenotype Phylogenetic Analysis Phylogeny Process Proxy Public Health Research Personnel Residual state Severities Source Statistical Bias Statistical Methods Statistical Models Symptoms Techniques Trees Tweens United States Variant Viral Viral Genome Viral Load result Virulence Virulent Virus Virus Diseases Work ZIKA Zika Virus base chikungunya clinical predictors clinically relevant design flexibility fundamental research genetic information high dimensionality interest large datasets multidimensional data outcome prediction phenotypic data predict clinical outcome prediction algorithm predictive modeling tool trait viral genomics virus genetics virus host interaction

项目摘要

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.

项目概要/摘要尽管全球公共卫生取得了进步，病毒仍然是对美国和美国人类健康的主要威胁国家和国际上近期和持续爆发的 SARS-CoV-2、埃博拉病毒、寨卡病毒、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的必要性流行病期间的进化和病毒与宿主的相互作用提供了病毒进化的系统发育统计模型。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。当前的系统发育模型往往过于僵化，无法真实地模拟这些关系，而那些即使对于中等大小的数据集，计算也很困难。该项目旨在开发新的数据集。统计模型既足够灵活，可以模拟复杂的生物关系，又可以扩展到大规模第一个目标是开发更有效和更少偏差的统计方法。用于估计病毒表型的遗传力（例如病毒载量、宿主 CD4 T 细胞计数、复制能力）。当前的统计实践通常会产生有偏差的遗传力估计，并且难以处理大数据该项目旨在扩展最先进的推理技术来模拟病毒表型的遗传力。类型（实现无偏且有效的推理）并应用这些新方法来更好地估计 HIV-1 病毒载量的遗传性的第二个目标是开发用于研究复杂的统计方法。高维病毒表型，例如感染严重程度，无法通过单一测量来捕获这些表型由于其固有的复杂性而难以量化，从而阻碍了严格的努力。例如，识别异常毒力的病毒进化枝，而系统发育因子分析可以识别和识别病毒。高维表型的量化，它很难扩展到大数据集，我们提出了新的推论。我们计划应用解决这些可扩展性问题并允许以前棘手的分析的技术。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的毒力此外，这些方法非常适合识别病毒 mu-之间的上位相互作用。我们计划探索 HIV、寨卡病毒和基孔肯雅热中的这些相互作用第三个目标是开发专门用于预测病毒结果的新统计模型。为了适应这些模型所需的必要灵活性，我们开发高度通用的新推理策略（即它们不依赖于严格的假设）在现有模型中）和强大的计算效率将使研究人员或 Fortress 使用病毒序列预测临床相关结果，这有助于为治疗提供信息。将使用上述埃博拉和拉沙热数据评估这些方法。