The Genomic Architecture of Pregnancy Loss

流产的基因组结构

• 批准号: 10226655
• 负责人: MICHAEL E TALKOWSKI
• 金额: $ 57.7万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10226655
• 关键词: Architecture; Bayesian Modeling; Bioinformatics; CRISPR screen; CRISPR/Cas technology; Chromosome abnormality; Classification Scheme; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Comparative Genomic Analysis; Complex; Computer Models; Conceptions; Couples; Data; Data Set; Databases; Development; Disease; Embryo; Emerging Technologies; Engineering; Enhancers; Family; Fetal Structures; Funding; Future; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genome; Genome engineering; Genomic Segment; Genomics; Gestational Age; Goals; Human; Human Development; Human Genome; Individual; Institutes; Institution; Intellectual functioning disability; International; Joints; Life; Live Birth; Manuals; Maternal-fetal medicine; Measures; Mediating; Medical Genetics; Mendelian disorder; Methods; Modeling; Molecular; Mus; Mutation; Neurodevelopmental Disorder; Pathogenicity; Penetrance; Phenotype; Point Mutation; Population; Pregnancy; Pregnancy loss; Process; Property; Proteins; Recurrence; Relative Risks; Resolution; Risk Estimate; Sampling; Site; Source; Statistical Models; Structural defect; Structure; Tail; Techniques; Technology; Untranslated RNA; Validation; Variant; Weight; Work; algorithm development; analytical method; autism spectrum disorder; base; case control; clinical database; clinically relevant; cloud based; cohort; de novo mutation; developmental disease; dosage; early pregnancy loss; fetal; fetal loss; gene discovery; genetic architecture; genetic variant; genome editing; genome sequencing; genomic data; genomic locus; genomic predictors; genomic variation; improved; in vivo; in vivo Model; innovation; mouse genome; non-genetic; novel; pleiotropism; promoter; prospective; screening; stillbirth; technology development; transmission process

ABSTRACT Pregnancy loss (PL) occurs in approximately 15% of clinically recognized pregnancies and only 30% of conceptions result in a live birth, yet little is known about genomic predictors of PL beyond large chromosomal aberrations. While it is likely that non-genetic etiologies and common variants underlie a component of PLs, we propose here to disentangle the mutational spectrum of rare and de novo variation contributing to non-viability. We overcome traditional barriers to genomic studies of PL, namely insufficient power, low-resolution technologies, and reductive statistical approaches, by establishing a Fetal Genomics Consortium (FGC) comprised of 21 international sites. Our team includes leading expertise in maternal-fetal medicine, statistical genetics, genomics, technology and algorithm development, structural variation, and in vivo CRISPR modeling. We will apply high-throughput genome sequencing (WGS) at the Broad Institute and external datasets as a frontline strategy and perform analyses of at least 2,500 PL trios. Our cohort will include the PL continuum, including at least 2,000 fetal demise trios from 20-42 weeks gestation and 500 recurrent pregnancy loss trios in couples with at least two previous losses at any gestational age. We will combine, process, analyze, and interpret pathogenic variation and return clinically relevant results to families, while prioritizing a subset of unsolved cases with complex fetal anomalies for long-read WGS and de novo assembly (AIM 1). We will then explore novel genomic predictors of PL and compare the genomic architectures of the developmental continuum from early PL to later onset developmental disorders (AIM 2). These studies will apply novel analytic methods to integrate all classes of genomic variation, mutation rates, relative risk estimates and measures of evolutionary constraint for each gene in the genome to interrogate the ‘intolerome’. To improve discovery power in PL, we will leverage massive population-scale datasets (>2M genomes), and the aggregation of >200,000 cases from ongoing developmental disorder studies. These cohorts are accessible and already being analyzed by our FGC groups, including the Broad Institute Center for Mendelian Genetics, Gabriella Miller Kids First sequencing center, gnomAD, All of US, the Undiagnosed Disease Network, and autism and neurodevelopmental disorder consortia studies. AIM 2 will integrate computational models of coding and noncoding constraint into a statistical framework to identify novel genes and loci associated with PL, and prioritize variants for in vivo CRISPR lethality screening in mouse embryos (AIM 3). This FGC proposal is thus poised to transform our understanding of the genomic predictors of PL. We will evaluate meticulously phenotyped PL families with emerging technologies, population- scale datasets and developmental disorder cohorts using uniform bioinformatic and statistical approaches. Our analyses will deliver clinically meaningful results to current families, and our functional modeling will inform interpretation of variation incompatible with human development for future PL families.

摘要 妊娠丢失（PL）发生在约15%的临床公认的妊娠和只有30%的 怀孕导致活产，但除了大染色体外，对PL的基因组预测因子知之甚少 畸变虽然非遗传病因学和常见变异可能是PL的一个组成部分，但我们 在这里，我建议解开罕见的突变谱和从头变异导致的非生存能力。 我们克服了传统的PL基因组研究的障碍，即能量不足，分辨率低， 通过建立胎儿基因组学联盟（FGC）， 由21个国际网站组成。我们的团队包括领先的专业知识，母胎医学，统计 遗传学、基因组学、技术和算法开发、结构变异和体内CRISPR建模。 我们将在布罗德研究所应用高通量基因组测序（WGS）和外部数据集作为一个 前线战略和执行至少2,500 PL三人组的分析。我们的队列将包括PL连续体， 包括至少2,000例妊娠20-42周的胎儿死亡三人组和500例复发性妊娠丢失三人组， 在任何胎龄至少有两次流产史的夫妇。我们将联合收割机，处理，分析和解释 病原变异，并将临床相关结果返回给家庭，同时优先考虑未解决病例的子集 具有复杂的胎儿异常，用于长读WGS和从头组装（AIM 1）。我们将探索新的 PL的基因组预测因子，并比较早期PL发育连续体的基因组结构 晚发性发育障碍（AIM 2）。这些研究将采用新的分析方法， 基因组变异的类别，突变率，相对风险估计和进化约束的措施， 基因组中的每一个基因来询问“不耐受体”。为了提高PL中的发现能力，我们将利用 大规模人群规模数据集（> 200万个基因组），以及来自正在进行的 发育障碍研究。这些队列是可以访问的，已经被我们的FGC小组分析， 包括布罗德研究所孟德尔遗传学中心，加布里埃拉米勒儿童第一测序中心， gnomAD、All of US、未诊断疾病网络以及自闭症和神经发育障碍联盟 问题研究AIM 2将编码和非编码约束的计算模型整合到一个统计框架中 鉴定与PL相关的新基因和基因座，并优先考虑用于体内CRISPR致死性筛选的变体 小鼠胚胎（AIM 3）。因此，FGC的提议有望改变我们对基因组的理解。 预测PL。我们将仔细评估PL家族的表型与新兴技术，人口- 使用统一的生物信息学和统计学方法来扩展数据集和发育障碍队列。我们 分析将为当前的家庭提供有临床意义的结果，我们的功能模型将告知 解释与未来PL家族的人类发育不相容的变异。