The Genomic Architecture of Pregnancy Loss

流产的基因组结构

• 批准号: 10705318
• 负责人: MICHAEL E TALKOWSKI
• 金额: $ 83.9万
• 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/10705318
• 关键词: Architecture; Bayesian Modeling; Bioinformatics; CRISPR screen; CRISPR/Cas technology; Child; Chromosome abnormality; Classification Scheme; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Comparative Genomic Analysis; Complex; Computer Models; Conceptions; Couples; Data; Data Aggregation; Data Set; Development; Disease; Embryo; Emerging Technologies; Engineering; Enhancers; Etiology; Family; Funding; Future; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genome; Genome engineering; Genomic Segment; Genomics; Gestational Age; Goals; Heterozygote; Human; Human Development; Human Genome; Individual; 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; Tail; Techniques; Technology; Untranslated RNA; Validation; Variant; Work; aggregation database; algorithm development; analytical method; autism spectrum disorder; case control; clinical database; clinically relevant; cloud based; cohort; data integration; de novo mutation; developmental disease; dosage; early pregnancy loss; fetal; fetal loss; gene discovery; genetic architecture; genome editing; genome sequencing; genomic data; 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 妊娠丧失（PL）发生在大约15％的临床认可的妊娠中，只有30％ 概念导致活产，但对PL超出大染色体的基因组预测指标知之甚少 畸变。尽管非遗传病因和普通变体很可能是PL的组成部分，但我们 这里的提案要解散罕见和从头差异的突变频谱，从而导致不可变性。 我们克服了PL基因组研究的传统障碍，即功率不足，低分辨率 通过建立胎儿基因组学联盟（FGC），技术和降低统计方法 由21个国际遗址编译。我们的团队包括领先的母校医学专业知识，统计 遗传学，基因组学，技术和算法开发，结构变化以及体内CRISPR建模。 我们将在Broad Institute和外部数据集中应用高通量基因组测序（WGS） 前线策略并执行至少2500个PL三重奏的分析。我们的队列将包括PL Continuum， 在20-42周的妊娠中包括至少2,000个胎儿死亡三重奏和500个复发性怀孕三重复 任何胎龄至少有两次损失的夫妻。我们将结合，处理，分析和解释 致病性变异和临床相关结果与家族相关，同时优先考虑未解决的病例的子集 具有长阅读WGS和从头组装的复杂胎儿异常（AIM 1）。然后我们将探索小说 PL的基因组预测指标，并比较早期PL的发育连续体的基因组结构 以后发病发育障碍（AIM 2）。这些研究将采用新颖的分析方法来整合所有 基因组变异，突变率，相对风险估计和进化约束的度量的类别 基因组中的每个基因都询问“静态组”。为了提高PL的发现能力，我们将利用 大量的人口尺度数据集（> 2M基因组），以及持续的200,000例汇总 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组进行了分析， 包括孟德尔遗传学研究所中心，加布里埃拉·米勒（Gabriella Miller）儿童首次测序中心， gnomad，我们所有人，未诊断的疾病网络以及自闭症和神经发育障碍联盟 研究。 AIM 2将将编码和非编码约束的计算模型集成到统计框架中 识别新型基因和与PL相关的局部基因，并优先考虑用于体内CRISPR杀伤性筛查的变体 在小鼠胚胎中（AIM 3）。因此，该FGC建议被毒死以改变我们对基因组的理解 PL的预测因子。我们将通过新兴技术，人群 - 使用统一的生物信息学和统计方法，比例数据集和发育障碍队列。我们的 分析将为当前家庭带来临床意义的结果，我们的功能建模将告知 解释与未来PL家庭的人类发展不相容的变异。

项目成果

High-Resolution and Noninvasive Fetal Exome Screening.

高分辨率和无创胎儿外显子组筛查。

• DOI: 10.1056/nejmc2216144
• 发表时间: 2023
• 期刊: The New England journal of medicine
• 作者: Brand,Harrison;Whelan,ChristopherW;Duyzend,Michael;Lemanski,John;Salani,Monica;Hao,StephanieP;Wong,Isaac;Valkanas,Elise;Cusick,Caroline;Genetti,Casie;Dobson,Lori;Studwell,Courtney;Gianforcaro,Kathleen;Wilkins-Haug,Louise;Guse
• 通讯作者: Guse