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子

• 批准号: 10393656
• 负责人: Ira M Hall
• 金额: $ 150.96万
• 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 2026-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10393656
• 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; 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：复发性流产 (RPL) 发生在临床认可的妊娠中大约 5% 损失。 RPL 的病因尚不明确：在排除已知病因后，大约一半 患有 RPL 的女性仍然没有明确的病因。事实上，RPL 是经常性的，这一事实表明， 遗传成分，然而，目前对基因组的贡献的了解非常有限 RPL。先前的研究通常在设计上存在缺陷，受到样本量小、临床不完整的限制 表型分析和/或仅招募单身人士。在这份提案中，我们提出了招募 1000 名员工的计划。 具有严格表型的 RPL 三人组，包括来自美国和其他地区的多元化和代表性不足的背景 应用我们实验室开发的全基因组测序和复杂的变异检测和解释方法来识别 RPL 的致病性和可能致病性变异。然后我们将进行全面的综合数据 分析以确定无法解释的 RPL 的遗传基础并绘制染色体的基因和区域图 这是人类发育和成功怀孕所绝对需要的。我们的变体解释 管道包括绘制可能的致病性非编码和结构变异的尖端方法，很少 在任何妊娠丢失研究中进行评估。我们还将进行一项 RNA-seq 试点研究来评估该方法的实用性 妊娠丢失环境中基因发现的方法。我们将首先寻找隐性致病变异， 包括复合杂合性，然后测试从头嵌合、线粒体突变的模型， 监管非编码变异和总体突变负担。从这些综合分析中，我们期望 发现基因和染色体区域中的许多变异，这些变异无法容忍功能变异， 我们将其定义为人类不耐受组。我们将在之前的研究基础上通过以下方式绘制不耐受组图： 结合所有临床研究的可用数据来确定不明原因妊娠的遗传病因 损失，包括本提案和我们之前的工作中生成的数据，ii）基于网络的优先级方法 对人类发育和妊娠很重要的变异基因，iii) 小鼠（KOMP、DMDD/MGI）和细胞系 敲除研究 iv) 罕见和常见疾病测序研究，包括孟德尔基因组中心 (CMG)、常见疾病基因组学中心 (CCDG) 和儿科心脏基因组学联盟 (PCGC)， iv) 新兴人类泛基因组研究 HPP，以及 v) 人口规模生物样本库项目，例如英国生物样本库 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研究来证实这些预测 RPL 第一次损失、兄弟姐妹和祖父母的分析。分享耶鲁大学早期未发表的数据 我们在这些项目中的领导地位使 CMG 和 HPP 成为我们迄今为止的重要优势 迄今为止规模最大、最全面的 RPL 研究。我们的发现将朝着 全面绘制人类不耐受组图谱的目标并将进一步扩展和完善探索性 研究人类发育所必需的基因和染色体区域的空间。