Evolution of genetic disease risks over time and space

遗传病风险随时间和空间的演变

• 批准号: 10220999
• 负责人: Joseph L. Lachance
• 金额: $ 37.61万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-05 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10220999
• 关键词: Cellular biology; Computer Simulation; DNA; Development; Disease; Disease susceptibility; Effectiveness; European; Genetic; Genetic Diseases; Genetic Risk; Genome; Genomic medicine; Goals; Health; Hereditary Disease; Human; Individual; Mathematical Model Simulation; Modernization; Molecular; Population; Public Health; Recording of previous events; Research; Research Project Grants; Research Project Summaries; Susceptibility Gene; Time; Work; bioinformatics tool; disorder risk; genetic architecture; genetic epidemiology; genetic evolution; genome wide association study; health inequalities; improved; mathematical model; novel; phenotypic data; precision medicine; tool development

Project summary This research project bridges the gap between evolutionary genetics and genetic epidemiology. In the past few years, a large number of genome-wide association studies have identified thousands of disease susceptibility loci. However, the vast majority of these studies have focused on individuals with European ancestry. Many disease loci have yet to be discovered, and disease-associations that are presently known suffer from ascertainment bias. A major public health challenge is to accurately predict hereditary disease risks in other populations. Previous work has revealed that hereditary disease risks have evolved over human history and that disease risks are correlated with different genetic ancestries. This proposed research plan combines multiple approaches: analysis of ancient and modern genomes, mathematical modeling, and bioinformatics tool development. It examines the evolutionary causes of differences in genetic disease risks across populations, and it will help extend precision medicine to individuals who have diverse ancestries. By applying genomic medicine approaches to ancient DNA, this project will be result in a better understanding of how health and disease have evolved in the recent past. Mathematical models and computer simulations will be used to generate testable predictions about what sorts of genetic architectures contribute the most to health inequities. This project includes development of a novel ancestry painting approach that will improve predictions of hereditary disease risks for individuals whose genomes contain a mix of different ancestries. This project also involves generating improved genetic risk scores that correct for ascertainment bias and evolutionary history while incorporating details of molecular cell biology. The effectiveness of this approach will be assessed using phenotypic data from multiple populations. A major goal of this project is to understand how disease risks have come to vary across the globe, and this work will help extend the benefits of genomic medicine to individuals who have diverse ancestries.

项目概要 该研究项目弥合了进化遗传学和遗传学之间的差距 流行病学。过去几年，大量的全基因组关联 研究已确定了数千个疾病易感位点。然而，广阔的 这些研究大部分集中在具有欧洲血统的个体。许多 尚未发现疾病位点，目前尚未发现疾病关联 已知存在确定偏差。一项重大的公共卫生挑战是 准确预测其他人群的遗传性疾病风险。之前的作品有 揭示遗传性疾病风险在人类历史中不断演变，并且 疾病风险与不同的遗传血统相关。这项拟议的研究 计划结合了多种方法：分析古代和现代基因组， 数学建模和生物信息学工具开发。它检查 不同人群遗传疾病风险差异的进化原因，以及 将有助于将精准医疗扩展到具有不同血统的个体。经过 将基因组医学方法应用于古代 DNA，该项目将导致 更好地了解健康和疾病在最近的演变过程。 数学模型和计算机模拟将用于生成可测试的 预测哪种遗传结构对健康贡献最大 不平等。该项目包括开发一种新颖的祖先绘画方法 这将改善对患有以下疾病的个体的遗传性疾病风险的预测 基因组包含不同祖先的混合。该项目还涉及发电 改善遗传风险评分，纠正确定偏差和进化 历史，同时融入分子细胞生物学的细节。的有效性 将使用来自多个人群的表型数据来评估这种方法。一个 该项目的主要目标是了解疾病风险如何变化 在全球范围内，这项工作将有助于将基因组医学的好处扩展到 具有不同血统的个人。