Trio Analysis of Recurrent Pregnancy Loss Integrated Bioinformatics Genomics Study (TRIOS)

复发性流产综合生物信息学基因组学研究 (TRIOS) 的三重奏分析

• 批准号: 10612433
• 负责人: Ruth B Lathi
• 金额: $ 125.88万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612433
• 关键词: Affect; Anatomy; Bioinformatics; Biological; Catalogs; Circulation; Clinical; Collaborations; Communities; Complex; Conceptions; Couples; Data; Data Set; Databases; Development; Diagnosis; Disease; Essential Genes; Etiology; Fathers; Foundations; Frustration; Funding; Future; Genes; Genetic; Genetic Databases; Genetic Determinism; Genetic study; Genomics; Genotype; Goals; Hematology; Heritability; Human; Immune; Individual; Intervention; Machine Learning; Molecular; Molecular Profiling; Mothers; Multiomic Data; Online Mendelian Inheritance In Man; Parents; Pathway interactions; Patients; Phenotype; Precision Health; Pregnancy; Pregnancy Outcome; Pregnancy loss; Prenatal Diagnosis; Provider; Recommendation; Recurrence; Research; Risk; Seminal; Source; Spontaneous abortion; Structural Congenital Anomalies; Systems Biology; Testing; Work; adverse pregnancy outcome; biobank; clinical phenotype; cohort; data repository; early pregnancy; epidemiology study; exome sequencing; experience; gene conservation; gene regulatory network; genetic variant; genome sequencing; genome wide association study; genomic biomarker; genomic locus; improved; innovation; insight; learning strategy; lipidomics; metabolomics; molecular marker; molecular phenotype; multidisciplinary; multiple omics; novel; patient oriented; phenotypic data; precision medicine; predictive modeling; prenatal; protein protein interaction; recruit; reproductive; reproductive system disorder; risk mitigation; risk prediction; risk prediction model; risk variant; single-cell RNA sequencing; transcriptomics; treatment center; whole genome

PROJECT SUMMARY Recurrent pregnancy loss (RPL) affects up to 5% of couples, yet nearly half of cases remain unexplained by current testing recommendations. Euploid pregnancy loss, in the setting of unexplained RPL, is particularly frustrating for patients and providers because there is no clear explanation or any proven therapies to mitigate risk of subsequent miscarriages. As clinical presentation and subsequent pregnancy outcomes vary widely, this complex disorder will ultimately require a precision health approach. While more than 3000 human genes are conserved and likely essential for early development, remarkably little is known about their contribution to RPL and current genetic databases are essentially devoid of RPL entries. Moreover, there is currently no database that annotates phenotypes and genotypes of these essential genes. This proposal aims to define genetic determinants of RPL through clinical and molecular phenotyping and genomic sequencing of a large RPL cohort, combined with novel bioinformatics and machine learning approaches to derive predictive risk algorithms. A comprehensive approach to identify genomic markers of pregnancy loss by whole genome sequencing of well- characterized RPL trios (mother-father-pregnancy loss) will be undertaken in Aim 1. These genetics efforts will be paired in Aim 2 with metabolomic, lipidomic and single cell transcriptomic profiling preconception and in early pregnancy. Leveraged with innovative machine learning strategies in Aim 3, this approach will significantly advance understanding of the genetic underpinnings of unexplained RPL. A clinical ‘intolerome’ database will be constructed in Aim 4 to facilitate worldwide collaboration and curation of genotypes and associated phenotypes, making the genetics and omics data and results available to the public as well as other funded teams. This multidisciplinary team includes leaders in RPL, genetics, genomics, prenatal diagnosis, bioinformatics and machine learning at Stanford, UCSF and OHSU. Combined we have a substantial cohort of RPL patients that will serve as a robust recruitment source, along with a collaboration with the unique UK Pregnancy Baby BioBank of existing trios to accomplish project goals. The proposed study is anticipated to have significant clinical and research impact by identifying the genomic contribution to RPL in a large and well phenotyped cohort and building improved risk predictions based on machine learning incorporating clinical, genetic, and molecular data. This work will lay the foundation for precision medicine-based interventions for RPL couples who are difficult to diagnose and have few proven treatments.

项目总结 反复妊娠丢失(RPL)影响高达5%的夫妇，但近一半的病例仍无法解释 当前的测试建议。整倍体妊娠丢失，在原因不明的RPL的背景下，尤其是 令患者和提供者沮丧，因为没有明确的解释或任何有效的治疗方法来缓解 有随后流产的风险。由于临床表现和随后的妊娠结局差异很大，这 复杂的疾病最终将需要一种精确的健康方法。而超过3000个人类基因是 它们是保守的，可能对早期发育至关重要，人们对它们对RPL的贡献知之甚少 目前的基因数据库基本上没有RPL条目。此外，目前还没有数据库 它注释了这些重要基因的表型和基因类型。这项提案旨在定义基因 通过大量RPL队列的临床和分子表型和基因组测序确定RPL的决定因素， 结合新的生物信息学和机器学习方法，得出预测风险算法。一个 用全基因组测序方法鉴定妊娠丢失的基因组标记 特色化的RPL三人组(母亲-父亲-怀孕丢失)将在目标1中进行。这些遗传学努力将 在目标2中与代谢组、脂组和单细胞转录组分析配对前概念和早期 怀孕了。在Aim 3中利用创新的机器学习策略，这种方法将显著 促进对无法解释的RPL的遗传基础的理解。临床上的“不耐受组”数据库将 将在目标4中建立，以促进全球范围内的合作和对基因类型和相关 表型，向公众以及其他基金提供遗传学和组学数据和结果 团队。这个多学科团队包括RPL、遗传学、基因组学、产前诊断、 斯坦福大学、加州大学旧金山分校和俄亥俄州立大学的生物信息学和机器学习。加起来，我们有一个相当大的队列 RPL患者将作为一个强大的招募来源，以及与独特的英国 孕婴生物库现有三人组完成项目目标。拟议的研究预计将包括 通过在大量和良好的研究中确定基因组对RPL的贡献，对临床和研究产生重大影响 表型队列，并基于机器学习建立改进的风险预测，结合临床、 基因和分子数据。这项工作将为RPL的精准医学干预奠定基础 难以诊断且几乎没有得到证实的治疗方法的夫妇。