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 我们现在处于可用遗传和基因组数据集的医学和人口遗传学的关键点 有动力检测人类基因组上自然选择的多种特征，并调查其 下游对人类疾病和复杂特征的遗传结构的影响。表征这些 签名可以使我们能够通过 通过改进的变体改善了各种祖先的多基因风险评分和诊断 临床研究的优先分数。但是，仍然需要方法论发展和新分析 为了理解这些新的不同数据集。 在此提案中，我们将开发模型并应用旨在调查下游影响的方法 通过利用新型的大型遗传和基因组数据集来对人类疾病的自然选择。首先，我们会的 表征自然选择的遗传特征，塑造了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）汇总统计数据一百 特质和进化模拟的。确实，尽管许多作品最近都强调了负面的作用 选择人类疾病，我们仍然需要方法来分析低贫困疾病并调查 选择超出负面选择的作用。其次，我们将表征最近的遗传特征 选择通过利用各种祖先的不同基因调节和等位基因效应大小。 来自欧洲和亚洲祖先的单细胞RNA-seq和GWAS数据集。开发分析的方法 并解释最近发布的非欧洲基因组和遗传数据集的前提 最近的人类适应，以及为什么在祖先群体中，GWA的等位基因效应大小不同，这就是 提高多基因风险得分的基础可运输能力。第三，我们将表征遗传特征 通过利用数百万年的外显子和调节水平的基因选择的自然选择 测序来自240个哺乳动物和最新增强剂基因图的数据。包括基本对分辨率 对外显子和监管分数的约束数据集将允许我们提高我们对基因演变的了解和 功能，最终在诊断研究中解释了罕见的遗传变异。我们的方法和数据集 将公开可用，记录并适用于任何可遗传的特征，从而最大化其对 社区。