Project 2 - Deciphering the Molecular Drivers of Common Forms of Human Infertility Using Integrative Genomic, Cellular, and Phenomic Approaches

项目摘要 不孕不育是一种常见的疾病，超过十分之一的夫妇试图怀孕，而 在全球范围内，不孕不育的管理超过180亿美元。我们对这一问题的了解存在很大差距 不孕不育的潜在机制和风险因素，这些差距阻碍了有针对性的发展 针对不孕不育的根本原因的治疗。项目2将通过进行人类基因工程来填补这些空白 以前所未有的规模研究不孕不育和相关的生殖特征。通过与 世界各地的大型生物库，我们将访问1800多万人的数据以识别基因变异 与女性和男性不孕不育的风险以及与一些与生育有关的 特征，包括性激素浓度、青春期开始、脑下垂体和嗅球大小(目标1)。 然后我们将使用最先进的计算方法来精确定位特定的变异、基因、途径， 以及影响这些条件和特征的细胞类型(目标2)。这些方法包括精细映射、 路径分析，以及跨研究的遗传关联数据的交集，它们将由数据推动 来自项目2，目标1，以及关于与在 项目1，核心产生的基因表达的数据，以及关于基因的广泛的公开数据 链接模式、基因表达、蛋白质-蛋白质相互作用和其他实验结果。到时候我们会的 综合所有这些发现，了解不孕不育和目标1中研究的生殖特征是如何相互关联的 其他，项目1中罕见的不孕症，以及一般生殖和非生殖健康 (目标3)。整个物候组的关联研究将分析大量的表型以揭示新的表型 对于所研究的遗传变异的关联，对多效性的研究将确定 这些疾病和性状的遗传原因重叠，将使用孟德尔随机化技术 来推断因果关系。通过利用大规模人类遗传学的力量，这个项目将导致 全面了解生育和不孕不育的生物学，确定生育治疗的目标，以及 揭示生殖健康影响整体健康的途径。

PROJECT ABSTRACT Infertility is a common condition that affects over one-tenth of couples attempting to conceive, and the costs of managing infertility exceed 18 billion US dollars worldwide. There are significant gaps in our knowledge of the underlying mechanisms and risk factors for infertility, and these gaps impede the development of targeted treatments that address the root causes of infertility. Project 2 will fill these gaps by conducting human genetic studies of unprecedented scale to study infertility, and related reproductive traits. Through collaborations with large biobanks worldwide, we will access data from over 18 million individuals to identify genetic variants associated with risk for female and male infertility as well as those associated with a number of fertility-related traits, including sex-hormone concentrations, pubertal onset, and pituitary gland and olfactory bulb size (Aim 1). We will then use state-of-the-art computational approaches to pinpoint the specific variants, genes, pathways, and cell types that influence these conditions and traits (Aim 2). These approaches include fine-mapping, pathway analyses, and intersection of genetic association data across studies, and they will be fueled by data from Project 2, Aim 1, as well as data on genetic variants associated with rare infertility conditions identified in Project 1, data on gene expression generated by the Core, as well as extensive publicly available data on genetic linkage patterns, gene expression, protein-protein interactions, and other experimental findings. We will then integrate all these findings to understand how infertility and the reproductive traits studied in Aim 1 relate to each other, with the rare infertility conditions in Project 1, and with general reproductive and non-reproductive health (Aim 3). Phenome-wide association studies will analyze a large number of phenotypes to reveal novel phenotypic associations for the genetic variants studied, investigations of pleiotropy will determine the extent to which the genetic causes of these conditions and traits overlap, and the technique of Mendelian randomization will be used to infer causal relationships. By leveraging the power of large-scale human genetics, this project will lead to a comprehensive understanding of the biology of fertility and infertility, identify targets for fertility treatments, and reveal the pathways by which reproductive health influences overall health.