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 基因组技术的进步和大规模不同生物和健康集合的组装 数据的发展速度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，以提供对生物过程的新理解， 并提供更好的人类疾病预测，以实现个性化医疗的愿景。广大 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， 我们的统计分析方法和软件的发展包括四个主要领域：1）多变量 解读遗传贡献的方法; 2）遗传变异的统计学精细作图; 3）因果中介 方法：4）多基因风险评分（PRS）预测疾病。虽然这些领域可能看起来很广泛， 由于这些领域各不相同，因此迫切需要在这些领域建立更加综合的方法。比如说， 因为分子多效性是普遍存在的，所以多变量分析对于确定共有的遗传因子是必不可少的 通过多种性状的共同生物学机制起作用，并且当使用PRS预测疾病时， 当使用多变量相关性状时，复杂性状通常被更好地预测。而且，所使用的方法 用于统计学精细映射，包括注释的使用，与创建PRS以预测疾病相关。我们 团队，包括统计遗传学家，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，并将利用我们广泛的资源和合作， 已经发展。我们的新方法将应用于广泛的疾病，最终目标是更好地 了解疾病的病因，改善对不同种族的疾病预测， 差距。方便用户的软件将向科学界开放。我们将采取 快速发展的技术优势，来自多个领域的生物和计算见解，以及 不断发展的公共卫生和临床未满足的需求为我们的科学提供信息。