Efficient Statistical and Computational Methods for Genetics and Dynamical Models

遗传学和动力学模型的高效统计和计算方法

• 批准号: RGPIN-2019-06131
• 负责人: Wang, Liangliang
• 金额: $ 1.82万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765052
• 关键词: Efficient; Statistical; Computational; Methods; Genetics

My NSERC research program focuses on developing efficient statistical and computational methodologies for problems in genetics and dynamical models arising from various disciplines such as epidemiology and pharmacokinetics. My first research theme aims to tackle challenging problems in fields related to computational and statistical genetics/genomics. I aim to develop scalable statistical inference methodologies for phylogenetics, where trees are used to describe the evolutionary relationship among biological sequences. The development of these methods will allow inference for more complex statistical models in the contexts related to phylogenetics, such as the reconstruction of tumor trees from single-cell sequencing data, phylogenetic networks which model gene flow, and phylodynamics that involves both the phylogenetic tree and epidemiological models. I will develop various complex evolutionary models and efficient Bayesian model selection methods. The proposed research will allow evolutionary biologists and cancer researchers to conduct statistical inference more efficiently and accurately when facing large modern microbial and cancer cell sequencing datasets. I will also develop efficient Bayesian inference for imaging genetics, which involves large-scale neuroimaging data and high-dimensional genetic data. Motivated by data from the Alzheimer's Disease (AD) Neuroimaging Initiative, I will investigate the influence of genetic variation on brain structure and AD status using various statistical models and computational methods such as Bayesian regression, clustering, network modeling, Markov chain Monte Carlo and variational Bayes. I will develop functional principal component analysis methods for dimension reduction and estimating state-space models for longitudinal studies in the context of imaging genetics. This research will help to develop personalized medicine for treating AD. My second research theme focuses on statistical inference for dynamical models expressed in the form of differential equations (DEs). They are to understand complex dynamical systems in areas such as neuroscience and physics. DE parameters usually have scientific interpretations, but their values are often unknown. In addition, the available data are often noisy and partially observed. My goal is to model real-world applications using DEs and to develop novel methodologies to provide accurate and robust parameter estimates while keeping computational costs low. I will focus on the inference of high-dimensional ordinary differential equations and complex stochastic differential equations.  My research will enable more efficient and accurate statistical inference for large-scale data. Not only will the proposed research support the training of highly qualified personnel, but it will also result in publicly available software packages. The proposed methods are transferable to many other fields of natural science and engineering where similar models are used.

我的NSERC研究计划专注于为遗传学和动力学模型中的问题开发有效的统计和计算方法，这些问题来自不同的学科，如流行病学和药代动力学。我的第一个研究主题旨在解决与计算和统计遗传学/基因组学相关领域的挑战性问题。我的目标是为系统发育开发可扩展的统计推断方法，其中树被用来描述生物序列之间的进化关系。这些方法的发展将允许在与系统发育相关的背景下推断更复杂的统计模型，例如从单细胞测序数据重建肿瘤树、模拟基因流的系统发生网络以及涉及系统发生树和流行病学模型的系统动力学。我将开发各种复杂的进化模型和高效的贝叶斯模型选择方法。这项拟议的研究将使进化生物学家和癌症研究人员在面对大型现代微生物和癌细胞测序数据集时，能够更有效和准确地进行统计推断。我还将为成像遗传学开发高效的贝叶斯推理，这涉及到大规模的神经成像数据和高维遗传数据。受阿尔茨海默病(AD)神经成像计划数据的启发，我将使用各种统计模型和计算方法，如贝叶斯回归、聚类、网络建模、马尔可夫链蒙特卡罗和变分贝叶斯，研究遗传变异对大脑结构和AD状态的影响。我将在成像遗传学的背景下，为纵向研究开发用于降维和估计状态空间模型的函数主成分分析方法。本研究将有助于开发治疗AD的个体化药物。我的第二个研究主题集中在以微分方程(DES)形式表示的动力学模型的统计推断上。他们要了解神经科学和物理学等领域的复杂动力系统。DE参数通常有科学的解释，但它们的值往往是未知的。此外，现有的数据往往是有噪声的和部分观察到的。我的目标是使用DES对真实世界的应用程序进行建模，并开发新的方法来提供准确和可靠的参数估计，同时保持较低的计算成本。我将重点研究高维常微分方程组和复杂随机微分方程的推断。我的研究将使大规模数据的统计推断更加高效和准确。拟议的研究不仅将支持对高素质人员的培训，而且还将产生可公开使用的软件包。所提出的方法可推广到其他许多使用类似模型的自然科学和工程领域。