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)发生在大约15%的临床确认的妊娠中，而只有30%的 受孕导致活产，但除了大染色体外，对PL的基因组预测因子知之甚少 像差。虽然很可能是非遗传原因和常见的变异构成了pls的一个组成部分，但我们 在此建议解开导致无生存能力的罕见和从头开始变异的突变谱。 我们克服了传统的PL基因组研究的障碍，即动力不足，分辨率低 技术和简化统计方法，通过建立胎儿基因组学联盟(FGC) 由21个国际站点组成。我们的团队包括领先的母婴医学、统计学 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)，并将外部数据集作为 前线战略，并对至少2,500名PL三人组进行分析。我们的队列将包括PL连续体， 包括至少2000例妊娠20-42周的死胎三胎和500例复发性妊娠丢失三胎 在任何孕龄至少有两次失血的夫妇。我们将结合、处理、分析和解释 病原变异并将临床相关结果返回给家庭，同时优先处理未解决病例的子集 合并复杂的胎儿畸形，用于长时间阅读WGS和从头组装(目标1)。然后我们将探索小说 PL的基因组预测因子和比较早期PL发育连续体的基因组结构 至晚发型发育障碍(AIM 2)。这些研究将应用新的分析方法来整合所有 基因组变异类别、突变率、相对风险估计和进化限制措施 基因组中的每一个基因都用来询问“不耐受组”。为了提高PL的发现能力，我们将利用 海量人口规模的数据集(&gt；200万个基因组)，以及正在进行的&gt；200,000个病例的聚合 发育障碍研究。这些队列是可以访问的，并且已经被我们的FGC小组分析过了， 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，全美，未诊断疾病网络，自闭症和神经发育障碍联盟 学习。目标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 识别与PL相关的新基因和基因座，并优先选择体内CRISPR致死性筛查的变异 在小鼠胚胎中(AIM 3)。因此，这一FGC提议将改变我们对基因组的理解 PL的预测因子。我们将仔细评估具有新兴技术的表型PL家庭，人口- 使用统一的生物信息学和统计学方法的规模数据集和发育障碍队列。我们的 分析将为当前家庭提供临床上有意义的结果，我们的功能建模将为 为未来的PL家庭解释与人类发展不相容的变异。