Quantitative Methods for Genetic Epidemiology

遗传流行病学的定量方法

• 批准号: 10396017
• 负责人: Daniel J. Schaid
• 金额: $ 39.75万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10396017
• 关键词: Area; Award; Biological; Biological Process; Chromosome Mapping; Clinical; Clinical Investigator; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Development; Disease; Epidemiologist; Ethnic group; Etiology; Genetic; Genomics; Goals; Heart Diseases; Human; Knowledge; Malignant Neoplasms; Mediation; Methods; Molecular; Multivariate Analysis; Public Health; Reduce health disparities; Research; Research Personnel; Resources; Science; Software Tools; Source; Statistical Methods; Technology; Vaccination; Vision; analytical method; disorder risk; epidemiology study; experience; genetic epidemiology; genetic variant; genomic data; health data; human disease; improved; insight; novel; personalized medicine; phenotypic data; pleiotropism; polygenic risk score; response; trait; user friendly software

Project Summary The advancements of genomic technologies and assemblies of large disparate sets of biological and health data have outpaced the ability to integrate these different sources of information. Powerful statistical methods and software are needed to fill this gap in order to provide novel understandings of biological processes, as well as provide better predictions of human diseases to achieve the vision of personalized medicine. The broad goals of this project are to advance genetic epidemiology studies of human traits and diseases by expanding our development of statistical analytic methods and software encompassing four main areas: 1) multivariate methods to decipher genetic contributions; 2) statistical fine-mapping of genetic variants; 3) causal mediation methods; 4) polygenic risk scores (PRS) for predicting disease. Although these areas might appear broad and disparate, there is pressing need to build more integrative methods across these domains. For example, because molecular pleiotropy is pervasive, multivariate analysis is essential to identify shared genetic factors acting through common biological mechanisms of multiple traits, and when using PRS to predict disease, complex traits are often better predicted when multivariate correlated traits are used. And, the methods used for statistical fine-mapping, including use of annotation, are relevant for creating PRS to predict disease. Our team, involving statistical geneticists, computational biologists, genetic epidemiologist and clinical investigators, has decades of experience and will capitalize on the extensive resources and collaborations we have developed. Our novel methods will be applied to a broad range of diseases, with ultimate aims to better understand disease etiology and improved disease prediction across different ethnic groups to reduce health disparities. User-friendly software will be distributed with open access to the scientific community. We will take advantage of rapidly evolving technologies, biologic and computational insights from multiple fields, and evolving public health and clinical unmet needs to inform our science.

项目摘要 大量不同生物学和健康集合的基因组技术和集会的进步 数据超过了整合这些不同信息来源的能力。强大的统计方法 需要软件来填补这一空白，以便提供对生物过程的新了解，例如 还可以更好地预测人类疾病以实现个性化医学的愿景。宽阔 该项目的目标是通过扩大人类特征和疾病的遗传流行病学研究 我们开发统计分析方法和包含四个主要领域的软件：1）多变量 破译遗传贡献的方法； 2）遗传变异的统计精细图； 3）因果关系 方法; 4）用于预测疾病的多基因风险评分（PR）。尽管这些领域可能看起来很广泛，并且 不同的是，迫切需要在这些域中构建更多的集成方法。例如， 由于分子多效性无处不在，因此多元分析对于鉴定共享遗传因素至关重要 通过多种特征的常见生物学机制作用，以及使用PR来预测疾病时， 当使用多元相关性状时，通常可以更好地预测复杂的性状。而且，使用的方法 对于统计精细映射，包括使用注释，与创建PRS预测疾病有关。我们的 团队，涉及统计遗传学家，计算生物学家，遗传流行病学家和临床 调查人员拥有数十年的经验，并将利用我们的广泛资源和合作我们 已经开发了。我们的新方法将应用于广泛的疾病，并具有最终的目标来改善 了解疾病的病因和改善不同种族的疾病预测以减少健康 差异。用户友好的软件将通过开放访问科学界分发。我们会接受的 来自多个领域的快速发展技术，生物学和计算见解的优势，以及 不断发展的公共卫生和未满足的临床需要为我们的科学提供信息。