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: 10674983
• 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/10674983
• 关键词: Alleles; Asian ancestry; Base Pairing; Chromosome Mapping; Clinical Research; Communities; Complex; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Disparate; Enhancers; European ancestry; Evolution; Exons; Gene Expression Regulation; Genes; Genetic; Genomics; Goals; Heritability; Human; Human Genome; Knowledge; Low Prevalence; Mammals; 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.

摘要 我们现在正处于医学和群体遗传学的关键点， 有能力检测人类基因组上自然选择的各种特征，并研究它们的 对人类疾病和复杂性状的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 临床研究中的优先级评分。然而，仍然需要发展方法和进行新的分析 to make sense感of these new新distanct不同datasets数据集. 在本提案中，我们将开发模型和应用方法，旨在调查 通过利用新的大型遗传和基因组数据集进行人类疾病的自然选择。一是 描述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）对100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作用， 在人类疾病的选择上，我们仍然需要方法来分析低流行疾病和调查 超越负选择的选择。其次，我们将描述最近的遗传特征， 选择导致不同的基因调控和等位基因效应大小在不同的祖先群体， 来自欧洲和亚洲祖先的单细胞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，以及为什么来自GWAS的等位基因效应大小在不同的祖先群体中不同， 提高多基因风险评分可移植性的基础。第三，我们将描述基因特征 在数百万年的进化过程中， 来自240种哺乳动物的测序数据和最近的增强子基因图谱。包括碱基对分辨率 外显子和调控分数的约束数据集将允许提高我们对基因进化的认识， 功能，并最终在诊断研究中解释罕见的遗传变异。我们的方法和数据集 将公开提供，深入记录，并适用于任何遗传特征，最大限度地发挥其影响， 社会各界