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.

项目摘要 /摘要尽管全球公共卫生进展，但在统一的人类健康仍然是对人类健康的主要威胁国家和国际。 SARS-COV-2，埃博拉，Zika，Lassa Fever和 chikungunya以及诸如艾滋病毒之类的持续情节强调了了解病毒的必要性流行期间的进化和病毒宿主相互作用。病毒进化的系统发育统计模型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，当前的系统发育模型通常太不受欢迎，无法现实地模拟这些关系，而那些关系的关系对于即使是适度大小的数据集，在计算上也很棘手。该项目旨在开发新的统计模型既具有足够的功能，足以模拟复杂的生物学关系，并且可扩展到大型病毒和宿主特征的数据集。第一个目的是开发更多有效且较少有偏见的统计方法用于估计病毒表型的遗传力（例如病毒载荷，宿主CD4 T细胞计数，复制能力）。当前的统计实践通常会产生偏见的遗传力估计，并且对大数据很棘手套。该项目旨在扩展最新的推理技术，以模拟病毒式现象的遗传力类型（实现公正和有效的推论），并应用这些新方法以更好地估计 HIV-1中病毒负荷的遗传力。第二个目的旨在开发研究复杂的统计方法，高维病毒表型，例如感染严重程度，无法用单一测度捕获 - 精神。这些表型由于其继承的复杂性而难以量化，这使严格的努力感到困惑例如，确定异常有毒的病毒进化枝。系统发育因素分析可以识别和高维表型的量化，它缩放到大数据集。我们提出了新的推论解决这些可伸缩性问题并允许以前棘手的分析的技术。我们计划申请这些新方法研究埃博拉病毒和LASSA热中的病毒模式，并确定异常强度病毒株。此外，这些方法非常适合鉴定病毒Mu-之间的上皮相互作用感兴趣的表型和表型，我们计划探索艾滋病毒，寨卡病毒和基孔肯雅的这些相互作用病毒。第三个目的是开发特定于预测病毒结果的新的统计模型病毒序列数据感染。为了适应这些模型所需的必要灵活性，我们制定新的推理策略，这些策略都可以推广（即它们不依赖严格的假设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，这可以帮助治疗。我们将使用上述埃博拉病毒和LASSA发烧数据评估这些方法。