Core B - Technical Services

项目摘要 生殖医学中心(CRM)代表了一个多学科的项目，以探索基因 不孕不育的架构。这里描述的CRM基因组学核心(GC)设想提供集中式和 所有组分的基因组变异、统计关联和功能扰动的催化资源 这一最重要的客户关系管理计划。这个基因组学中心将通过提供专业知识来特别催化发现 在数据和方法方面，将被用来更深入地了解中国罕见和常见的不孕症 项目1和项目2。GC还将作为数据、分析、 以及整个程序的功能建模。在过去的几年里，我们制定了一份概要， 用于研究突变的计算工具、统计学方法和功能基因组学方法 人类疾病的变异谱。我们的方法结合了对短变量(SNV、INDELS)的联合分析 和结构变体(SVS)，包括规范平衡SVS和拷贝数变体(CNV)，以及 种类繁多的复杂SVs，数量惊人地丰富，并与一系列人类疾病有关。 这些研究需要统一生成、处理和严格分析基因组数据集的方法 用来进行联想研究。在GC中，我们将发现并注释变化，解释与 来自我们相关研究的超过1,000,000个基因组的种群规模数据集，并执行可扩展的 基因工程在与罕见和常见形式的癌症相关的基因中产生一系列等位基因扰动 利用人诱导多能干细胞(HiPSC)建立GnRH不孕症模型。总体而言，我们将支持 通过完成与提供数据集、方法和功能资源相关的三个目标来实现客户关系管理。 目标1将从外显子组、基因组和长期阅读三个方面开发全面的基因组资源 对客户关系管理队列进行排序和统一数据处理。目标2将执行整合的稀有变体 通过联合分析CRM队列和人口规模来关联和解释这些数据集 在我们的基因组聚合数据库(GnomAD)项目和复杂疾病联盟中生成的数据集 学习。然后，目标3将对GnRH神经元中的不孕基因进行可扩展的CRISPR扰动 通过设计功能丧失突变和选择不育基因的等位基因序列来建立模型。 基因组学核心中的转录图谱将识别与这些干扰相关的签名 不孕症基因，并将寻求这些信号在少数不孕症相关途径上的收敛。 所有CRISPR设计的模型都将分发给相关项目进行进一步的功能分析，以及所有数据 模型将公开提供给社区分发。基因组学中的这些目标 因此，客户关系管理中心将提供数据集、基因发现和CRISPR设计的等基因模型 在客户关系管理和更广泛的研究社区内促进对不孕症的新见解。

PROJECT ABSTRACT The Center for Reproductive Medicine (CRM) represents a multi-disciplinary program to explore the genetic architecture of infertility. The CRM Genomics Core (GC) described herein envisages providing a centralized and catalytic resource for genomic variation, statistical association, and functional perturbations for all components of this overarching CRM program. This genomics hub will specifically catalyze discoveries by providing expertise in the data and methods that will be leveraged for deeper insights into rare and common forms of infertility in Projects 1 and 2, respectively. The GC will also serve as the focal point for cross-fertilization of data, analyses, and functional modeling across the program. Over the last several years, we have developed a compendium of computational tools, statistical approaches, and functional genomics methods to interrogate the mutational spectrum of variation in human diseases. Our methods incorporate joint analyses of short variants (SNVs, indels) and structural variants (SVs), including canonical balanced SVs and copy number variants (CNVs), as well as a diverse catalog of complex SVs that are surprisingly abundant and associated with an array of human disease. These studies have required methods to uniformly generate, process, and rigorously analyze genomics datasets for association studies. In the GC, we will discover and annotate variation, interpret association against population-scale datasets in excess of 1,000,000 genomes from our related studies, and perform scalable engineering to generate an allelic series of perturbations in genes associated with rare and common forms of infertility using human induced pluripotent stem cell (hiPSC) derived GnRH models. Overall, we will support the CRM by completing three objectives related to providing datasets, methods, and functional resources. Objective 1 will develop a comprehensive genomics resource from exome, genome and long-read sequencing, and uniform data processing of the CRM cohorts. Objective 2 will perform integrated rare variant association and interpretation of these datasets by jointly analyzing CRM cohorts with population-scale datasets generated in our genome aggregation database (gnomAD) project and complex disease consortia studies. Objective 3 will then perform scalable CRISPR perturbation of infertility genes in GnRH neuronal models by engineering loss-of-function mutations and an allelic series for select infertility genes. Transcriptional profiling in the genomics core will identify signatures associated with perturbation of these infertility genes, and will seek convergence of these signatures on a small number of infertility relevant pathways. All CRISPR-engineered models will be distributed relevant projects for further functional assays, and all data and models will be made openly available for distribution to the community. These objectives in the genomics hub of the CRM will thus provide datasets, gene discoveries, and CRISPR-engineered isogenic models to facilitate new insights into infertility within the CRM and the broader research community.