Novel population-genetic methods for localizing targets of natural selection in diverse human genomes

用于在不同人类基因组中定位自然选择目标的新群体遗传学方法

• 批准号: 10538648
• 负责人: Sohini Ramachandran
• 金额: $ 37.45万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10538648
• 关键词: Bayesian Analysis; Biological; Computing Methodologies; Data Set; Disease; Evolution; Genes; Genetic; Genetic Variation; Genome Scan; Genomics; Goals; Health; Human; Human Genetics; Human Genome; Individual; Investments; Knowledge; Maps; Methods; Mission; Mutation; Natural Selections; Outcomes Research; Pathway interactions; Pattern; Phenotype; Play; Population; Population Dynamics; Population Genetics; Process; Public Health; Recording of previous events; Research; Role; Sampling; Series; Shapes; Slide; Statistical Methods; United States National Institutes of Health; biobank; candidate identification; cost; genetic architecture; genomic data; human population genetics; insight; multi-ethnic; novel; programs; theories; trait; whole genome

PROJECT SUMMARY. The PI's research program in population genetics focuses on the coalescent-based inference of population his- tories from whole genomes, and the determination of genetic basis of adaptation and disease at multiple biolog- ical scales—from mutations to genes to gene subnetworks. In the genomic era, computational and statistical methods are essential for identifying candidate adaptive and disease-associated mutations in humans, in whom mapping via linkage studies is challenging and costly. State-of-the-art approaches that scan genome-wide for signatures of selection or association with phenotype state are routinely applied to samples from one homoge- neous ancestry, rely on arbitrary thresholds for interpreting results, and produce results at genomic scales that can be difficult to connect to biological mechanism (for example, analyzing linkage blocks or sliding genomic windows). Thus, despite the enormous investments made by the NIH and biobanks around the world to generate large-scale genomic datasets from diverse individuals, methods for analyzing such datasets are lagging behind. This application describes a series of projects motivated by answering three fundamental questions in human population genetics: (1) what role has balancing selection played in human adaptation? (2) to what extent has adaptive evolution versus non-adaptive processes shaped human genomes? (3) to what extent do the genetic architectures of human traits vary by ancestry? The overall strategy for future research plans draws on the PI's expertise in coalescent theory, Bayesian inference, population genetics, and statistical genetics to produce new frameworks for analyzing patterns in and evolutionary processes underlying multiethnic genomic datasets. The outcomes of the research described in this MIRA application will give new insight into the interaction between selection and dynamic population histories in generating human genetic diversity, while determining the different modes of selection shaping human phenotypes and diseases.

项目摘要。 PI 的群体遗传学研究计划重点关注群体历史的基于合并的推断。 来自全基因组的理论，以及在多个生物学上确定适应和疾病的遗传基础 物理尺度——从突变到基因再到基因子网络在基因组时代，计算和统计。 方法对于识别人类候选适应性和疾病相关突变至关重要，其中 通过连锁研究进行绘图具有挑战性且成本高昂，是扫描全基因组的最先进方法。 选择或与表型状态关联的特征通常应用于来自一个同源的样品 新的祖先，依赖任意阈值来解释结果，并在基因组规模上产生结果 可能很难连接到生物机制（例如，分析连锁块或滑动基因组） 因此，尽管美国国立卫生研究院（NIH）和世界各地的生物银行投入了大量资金来产生。 来自不同个体的大规模基因组数据集，分析此类数据集的方法是滞后的。 该应用程序描述了一系列旨在回答人类三个基本问题的项目 群体遗传学：（1）平衡选择在人类适应中发挥了什么作用？（2）在多大程度上发挥了作用？ 适应性进化与非适应性过程在多大程度上塑造了人类基因组？ 人类特征的架构因祖先而异？未来研究计划的总体策略借鉴了 PI 的经验 合并理论、贝叶斯推理、群体遗传学和统计遗传学方面的专业知识，以产生新的 用于分析多种族基因组数据集的模式和进化过程的框架。 MIRA 应用程序中描述的研究成果将为了解两者之间的相互作用提供新的见解 选择和动态种群历史产生人类遗传多样性，同时确定不同的 塑造人类表型和疾病的选择模式。