Evolution of genetic disease risks over time and space

项目总结 这项研究项目弥合了进化遗传学和遗传学之间的鸿沟。 流行病学。在过去的几年里，大量的全基因组关联 研究已经确定了数千个疾病易感基因。然而，广大的 这些研究大多集中在具有欧洲血统的个人身上。许多 尚未发现疾病部位，以及目前已发现的疾病关联 已知的存在确定偏差。一项重大的公共卫生挑战是 准确预测其他人群的遗传病风险。以前的工作有 揭示了遗传性疾病的风险在人类历史上一直在演变， 疾病风险与不同的基因祖先相关。这项拟议的研究 PLAN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，以及它 将有助于将精准医学推广到拥有不同祖先的个人。通过 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过去是如何演变的。 将使用数学模型和计算机模拟来生成可测试 关于哪种基因结构对健康贡献最大的预测 不平等。该项目包括开发一种新的祖先绘画方法。 这将改善对以下个人遗传性疾病风险的预测 基因组包含不同祖先的混合。该项目还涉及到生成 改进的遗传风险评分，可纠正确定性偏差和进化 历史，同时结合分子细胞生物学的细节。的有效性。 这一方法将使用来自多个群体的表型数据进行评估。一个 这个项目的主要目标是了解疾病风险是如何变化的 这项工作将有助于将基因组医学的好处扩展到 拥有不同祖先的个体。

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.