Characterizing genetic signatures of natural selection to understand human diseases

表征自然选择的遗传特征以了解人类疾病

• 批准号: 10510415
• 负责人: Steven Gazal
• 金额: $ 40.79万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10510415
• 关键词: Alleles; Asian ancestry; Base Pairing; Clinical Research; Communities; Complex; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Enhancers; European; Evolution; Exons; Gene Expression Regulation; Genes; Genetic; Genomics; Goals; Heritability; Human; Human Genome; Knowledge; Low Prevalence; Mammals; Maps; Medical Genetics; Methodology; Methods; Modeling; Natural Selections; Population Genetics; Prevention; Recording of previous events; Resolution; Shapes; Variant; Work; genetic architecture; genetic signature; genetic variant; genome wide association study; genomic data; human disease; improved; novel; polygenic risk score; simulation; single cell sequencing; single-cell RNA sequencing; statistics; trait

Abstract We are now at a pivotal point of medical and population genetics where available genetic and genomic datasets are powered to detect diverse signatures of natural selection on the human genome, and to investigate their downstream effects on the genetic architecture of human diseases and complex traits. Characterizing these signatures could enable us to improve our understanding of diseases, as well as their prevention, through improved polygenic risk scores across diverse ancestry groups, and diagnosis, through improved variant prioritization scores in clinical studies. However, methodological development and new analyses are still required to make sense of these new disparate datasets. In this proposal, we will develop models and apply methods aiming at investigate the downstream effects of natural selection on human diseases by leveraging novel large genetic and genomic datasets. First, we will characterize the genetic signatures of natural selection shaping the genetic architecture of human complex traits, by leveraging polygenic methods, genome-wide association studies (GWAS) summary statistics for a hundred of traits, and evolutionary simulations. Indeed, while many works have recently highlighted the action of negative selection on human diseases, we still need methods to analyze low-prevalence diseases and to investigate selection beyond the action of negative selection. Second, we will characterize the genetic signatures of recent selection leading to different gene regulation and allele effect sizes across diverse ancestry groups by leveraging single-cell RNA-seq and GWAS datasets from European and Asian ancestries. Developing methods to analyze and interpret the recently released non-European genomic and genetic datasets has the premise to understand recent human adaptation, and why allele effect sizes from GWAS differ across ancestry groups, which is fundamental to improve polygenic risk scores transportability. Third, we will characterize the genetic signatures of natural selection on genes at the exon and regulatory levels over millions of years of evolution by leveraging sequencing data from 240 mammals and recent enhancer-gene maps. Including the base pair resolution of constraint datasets to exon and regulatory scores will allow to improve our knowledge of gene evolution and function, and ultimately the interpretation of rare genetic variants in diagnostic studies. Our methods and datasets will be publicly available, deeply documented, and applicable to any heritable traits, maximizing their impact to the community.

抽象的 我们现在正处于医学和群体遗传学的关键时刻，可用的遗传和基因组数据集 有能力检测人类基因组上自然选择的不同特征，并研究它们的 对人类疾病和复杂性状遗传结构的下游影响。表征这些 签名可以使我们通过以下方式提高对疾病及其预防的认识： 通过改进变异，改善不同血统群体的多基因风险评分和诊断 临床研究中的优先分数。然而，仍需要方法论的发展和新的分析 理解这些新的不同数据集。 在本提案中，我们将开发模型并应用旨在研究下游影响的方法 利用新颖的大型遗传和基因组数据集对人类疾病进行自然选择。首先，我们将 表征自然选择的遗传特征，塑造人类复杂性状的遗传结构， 通过利用多基因方法，全基因组关联研究（GWAS）汇总统计了一百个 特征和进化模拟。事实上，虽然最近许多作品都强调了消极的行为 对于人类疾病的选择，我们仍然需要分析低流行疾病和调查的方法 选择超越负选择的作用。其次，我们将描述最近的遗传特征 通过利用选择，导致不同祖先群体中不同的基因调控和等位基因效应大小 来自欧洲和亚洲血统的单细胞 RNA-seq 和 GWAS 数据集。开发分析方法 并解释最近发布的非欧洲基因组和遗传数据集的前提是了解 最近的人类适应，以及为什么 GWAS 的等位基因效应大小因祖先群体而异，这是 提高多基因风险评分可运输性的基础。第三，我们将表征遗传特征 通过利用数百万年的进化，在外显子和调控水平上对基因进行自然选择 来自 240 种哺乳动物的测序数据和最新的增强子基因图谱。包括碱基对分辨率 外显子和调控分数的约束数据集将有助于提高我们对基因进化和 功能，并最终解释诊断研究中罕见的遗传变异。我们的方法和数据集 将公开、深入记录并适用于任何遗传特征，最大限度地发挥其影响 社区。