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.

抽象的 大约 15% 的临床认可妊娠发生妊娠丢失 (PL)，而只有 30% 的妊娠发生妊娠丢失 (PL)。 受孕会导致活产，但除了大染色体外，对 PL 的基因组预测因素知之甚少 像差。虽然非遗传病因和常见变异很可能是 PL 的组成部分，但我们 在此建议理清导致非生存能力的罕见变异和从头变异的突变谱。 我们克服了 PL 基因组研究的传统障碍，即功率不足、分辨率低 技术和还原统计方法，通过建立胎儿基因组学联盟 (FGC) 由 21 个国际站点组成。我们的团队包括母胎医学、统计方面的领先专业知识 遗传学、基因组学、技术和算法开发、结构变异和体内 CRISPR 建模。 我们将应用布罗德研究所的高通量基因组测序（WGS）和外部数据集作为 一线战略并对至少 2,500 个 PL 三人组进行分析。我们的队列将包括 PL 连续体， 包括至少 2,000 名妊娠 20-42 周的胎儿死亡三人组和 500 名复发性妊娠丢失三人组 在任何胎龄期间至少有过两次流产的夫妇。我们将结合、处理、分析和解释 致病变异并向家庭返回临床相关结果，同时优先考虑未解决病例的子集 具有复杂的胎儿异常，可进行长读全基因组测序和从头组装 (AIM 1)。然后我们将探索小说 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 迟发型发育障碍 (AIM 2)。这些研究将应用新颖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约束措施 基因组中的每个基因来询问“intolerome”。为了提高 PL 的发现能力，我们将利用 大规模人口规模数据集（>2M 基因组），以及正在进行的超过 200,000 个病例的聚合 发育障碍研究。这些队列是可以访问的，并且已经由我们的 FGC 小组进行了分析， 包括 Broad Institute 孟德尔遗传学中心、Gabriella Miller Kids First 测序中心、 gnomAD、全美、未确诊疾病网络以及自闭症和神经发育障碍联盟 研究。 AIM 2 将编码和非编码约束的计算模型集成到统计框架中 识别与 PL 相关的新基因和基因座，并优先考虑体内 CRISPR 致死率筛选的变体 在小鼠胚胎中（AIM 3）。因此，这项 FGC 提案有望改变我们对基因组的理解 PL 的预测因子。我们将利用新兴技术仔细评估表型 PL 家族， 使用统一的生物信息学和统计方法扩展数据集和发育障碍队列。我们的 分析将为当前家庭提供具有临床意义的结果，我们的功能模型将提供信息 对未来 PL 家庭的与人类发展不相容的变异的解释。