Bayesian variable selection for genetic and genomic studies

用于遗传和基因组研究的贝叶斯变量选择

• 批准号: 2750748
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2750748
• 关键词: Bayesian; variable; selection; genetic; genomic

An important issue in high-dimensional regression problems is the accurate and efficient estimation of regression coefficients when, compared to the number of data points, a substantially larger number of potential predictors are present. Further complications arise with correlated predictors, leading to the breakdown of standard statistical models for inference; and the uncertain definition of the outcome variable, which is often a varying composition of several different observable traits. Examples of such problems arise in many scenarios in genomics- in determining expression patterns of genes that may be responsible for a type of cancer; and in determining which genetic mutations lead to higher risks for occurrence of a disease. This project involves developing broad and improved Bayesian methodologies for efficient inference in high-dimensional regression-type problems with complex outcomes, with a focus on genetic data applications. Further, we will extend this framework to a variety of latent class models, and investigate the operating characteristics and analytical properties of various priors in the context of high-dimensional variable selection problems.

高维回归问题中的一个重要问题是，与数据点的数量相比，存在大量潜在预测因子时，回归系数的准确和有效估计。相关的预测因素会导致进一步的复杂性，导致标准统计模型的崩溃;以及结果变量的不确定定义，这通常是几个不同的可观察特征的不同组成。这些问题的例子出现在基因组学的许多场景中-在确定可能导致某种癌症的基因的表达模式中;以及在确定哪些基因突变导致疾病发生的更高风险中。该项目涉及开发广泛且改进的贝叶斯方法，用于对具有复杂结果的高维回归类型问题进行有效推理，重点是遗传数据应用。此外，我们将这个框架扩展到各种潜在的类模型，并研究高维变量选择问题的背景下，各种先验的操作特性和分析特性。