Large scale genome sequencing and integrative analyses to define genomic predictors of recurrent pregnancy loss

大规模基因组测序和综合分析以确定复发性流产的基因组预测因素

• 批准号: 10226657
• 负责人: Ira M Hall
• 金额: $ 152.42万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10226657
• 关键词: Bioinformatics; Biometry; Cell Line; Chromosomes; Clinic; Clinical; Clinical Research; Code; Collaborations; Collection; Conceptions; Counseling; Couples; Data; Data Analyses; Data Science; Data Set; Data Sources; Detection; Development; Diagnosis; Disease; Etiology; Evaluation; Fathers; Fetal Development; Funding; Future; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genetic study; Genome; Genomic approach; Genomics; Goals; Heterozygote; Human; Human Development; Human Genetics; Knock-out; Lead; Leadership; Machine Learning; Maps; Mendelian disorder; Methods; Minority; Mitochondria; Modeling; Mosaicism; Mothers; Mouse Cell Line; Mus; Mutation; National Institute of Child Health and Human Development; Nature; Network-based; Pathogenicity; Pediatric Cardiac Genomics Consortium; Phenotype; Pilot Projects; Population; Pregnancy; Pregnancy loss; Recurrence; Reproductive Health; Reproductive Medicine; Research; Sample Size; Sampling; Short Tandem Repeat; Siblings; Site; Structure; Sum; Testing; Untranslated RNA; Variant; Woman; Work; base; biobank; bioinformatics tool; clinical phenotype; clinical research site; cohort; computerized data processing; design; exome sequencing; experience; gene discovery; genetic architecture; genetic variant; genome sequencing; genome-wide; genomic predictors; genomic variation; grandparent; human pangenome; improved; innovation; multidisciplinary; multimodality; novel; recruit; tool; transcriptome sequencing; variant detection; whole genome

SUMMARY: Recurrent pregnancy loss (RPL) occurs in approximately 5% of clinically recognized pregnancy losses. The etiology of RPL is not well characterized: after excluding the known etiologies, approximately half of women with RPL still have no identifiable cause. The fact that RPL is, in fact, recurrent suggests a strong genetic component, however there is currently a very limited understanding of the genomic contributions to RPL. Previous studies are typically deficient in their design, limited by small sample size, incomplete clinical phenotyping and/or the recruitment of singletons only. In this proposal, we put forward our plan to recruit 1000 rigorously-phenotyped RPL trios including from diverse and underrepresented backgrounds across the US and to apply WGS and sophisticated variant detection and interpretation methods developed by our labs to identify pathogenic and likely pathogenic variants for RPL. We will then perform comprehensive integrative data analyses to define the genetic basis of unexplained RPL and map the genes and regions of the chromosome that are absolutely required for human development and a successful pregnancy. Our variant interpretation pipeline includes cutting edge approaches to map likely pathogenic noncoding and structural variants rarely assessed in any pregnancy loss study. We will also perform a pilot RNA-seq study to assess the utility of this approach for gene discovery in the pregnancy loss setting. We will first look for recessive pathogenic variation, including compound heterozygosity and then test for models for de novo mosaicism, mitochondrial mutations, regulatory noncoding variation and overall mutational burden. From these combined analyses, we expect to uncover many variants in genes and regions of the chromosome that are intolerable to functional variation, which we define as the human intolerome. We will build on our previous studies to map the intolerome by combining i) available data from all clinical studies to define the genetic etiology of unexplained pregnancy loss, including data generated in this proposal and in our prior work, ii) network-based approaches to prioritize variants genes important for human development and pregnancy, iii) mouse (KOMP, DMDD/MGI) and cell line knockout studies iv) rare and common disease sequencing studies including Centers for Mendelian Genomics (CMG), Center for Common Disease Genomics (CCDG) and Pediatric Cardiac Genomics Consortium (PCGC), iv) emerging human pangenome studies HPP, and v) population-scale biobank projects such as UK BioBank and All of Us. We will then confirm these predictions via collaborator-led functional studies and retrospective analyses of RPL first losses, siblings and grandparents. The sharing of early, unpublished data from the Yale CMG and HPP enabled by our leadership in these projects is a significant strength of what will be by far the largest and most comprehensive study of RPL performed to date. Our findings will take great strides towards the goal of comprehensively mapping the human intolerome and will further expand and refine the exploratory space in which to investigate the genes and chromosomal regions essential for human development.

摘要：大约5％的临床认可妊娠发生了复发性妊娠丧失（RPL） 损失。 RPL的病因没有很好地表征：排除已知病因后，大约一半 有RPL的妇女仍然没有可识别的原因。实际上，RPL经常发生的事实表明了一个强大的 但是，遗传成分目前对对基因组贡献的理解非常有限 rpl。先前的研究通常缺乏设计，受样本量较小，临床不完整的限制 仅表型和/或招募单例。在此提案中，我们提出了招募1000的计划 严格的型RPL三重奏包括来自美国各地的不同和代表性不足的背景 应用WGS和我们实验室开发的复杂变体检测和解释方法来识别 RPL的致病性和可能的​​致病变异。然后，我们将执行全面的集成数据 分析以定义无法解释的RPL的遗传基础，并绘制染色体的基因和区域 这是人类发展和成功怀孕所必需的。我们的变体解释 管道包括尖端方法，以绘制可能的致病性非编码和结构变体很少 在任何怀孕丧失研究中进行评估。我们还将进行一项试点RNA-seq研究，以评估此功能 在妊娠损失设置中发现基因发现的方法。我们将首先寻找隐性致病性变异， 包括复合杂合性，然后测试新的镶嵌性模型，线粒体突变， 监管非编码变化和整体突变负担。从这些结合分析中，我们希望 发现染色体的基因和区域中的许多变体因功能变异而无法忍受， 我们将其定义为人类的静态组。我们将以我们以前的研究为基础，以绘制磁通膜 结合i）所有临床研究的可用数据来定义无法解释的妊娠的遗传病因 损失，包括本提案中生成的数据以及我们先前的工作，ii）基于网络的方法来优先 对人类发育和妊娠重要的变异基因，iii）小鼠（KOMP，DMDD/MGI）和细胞系 敲除研究IV）罕见和常见的疾病测序研究，包括孟德尔基因组学中心 （CMG），普通疾病基因组学中心（CCDG）和儿科心脏基因组学联盟（PCGC）， iv）新兴的人类pangenome研究HPP和v）人口规模的生物库项目，例如英国生物库 和我们所有人。然后，我们将通过合作者主导的功能研究和回顾性确认这些预测 分析RPL第一损失，兄弟姐妹和祖父母。共享耶鲁大学早期未发表的数据 CMG和HPP由我们在这些项目中的领导才能实现 迄今为止，对RPL的最大，最全面的研究。我们的发现将大步向前 全面绘制人类静态组并将进一步扩展和完善探索的目的 研究基因和染色体区域对人类发育必不可少的空间。