Characterizing genetic signatures of natural selection to understand human diseases

摘要 我们现在正处于医学和人口遗传学的关键时刻，现有的遗传和基因组数据 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 对人类疾病和复杂特征的遗传结构的下游影响。描述这些特性 签名可以使我们通过以下方式提高对疾病及其预防的了解 通过改进变异，提高不同血统群体的多基因风险得分和诊断 临床研究中的优先排序分数。然而，仍然需要方法学的发展和新的分析。 来理解这些新的完全不同的数据集。 在这项提案中，我们将开发模型和应用方法，旨在研究空气污染的下游影响 通过利用新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 描述自然选择塑造人类复杂特征的遗传结构的遗传特征， 通过利用多基因方法，全基因组关联研究(GWAS)汇总了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行为 在人类疾病的选择上，我们仍然需要方法来分析低发疾病和调查 选择超越了消极选择的行为。第二，我们将描述最近的基因特征 选择导致不同祖先群体之间不同的基因调控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。发展分析方法 并对最近公布的非欧洲基因组和基因数据集进行解读，有了了解的前提 最近的人类适应，以及为什么来自GWA的等位基因效应大小在不同的祖先群体中不同，这是 提高多基因风险评分可传递性的基础。第三，我们将描述基因签名的特征 经过数百万年的进化，在外显子和调控水平上的基因自然选择 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允许提高我们对基因进化和 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 将公开可用、详细记录，并适用于任何可遗传特征，最大限度地影响 社区。

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.