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.

项目概要 复发性流产 (RPL) 影响高达 5% 的夫妇，但近一半的病例仍无法解释 目前的检测建议，在不明原因的 RPL 情况下，整倍体妊娠丢失尤其严重。 让患者和提供者感到沮丧，因为没有明确的解释或任何行之有效的疗法来缓解 由于临床表现和随后的妊娠结局差异很大，因此存在随后流产的风险。 复杂的疾病最终需要精确的健康方法，而 3000 多个人类基因都受到影响。 保守且可能对早期发育至关重要，但令人惊讶的是，人们对它们对 RPL 的贡献知之甚少 目前的遗传数据库基本上没有RPL条目，而且目前还没有数据库。 注释这些必需基因的表型和基因型。该提案旨在定义遗传。 通过对大型 RPL 队列进行临床和分子表型分析以及基因组测序来确定 RPL 的决定因素， 结合新颖的生物信息学和机器学习方法来推导预测风险算法。 通过全基因组测序来识别妊娠丢失的基因组标记的综合方法 目标 1 将进行特征化的 RPL 三重奏（母亲-父亲-怀孕失败）。这些遗传学工作将 在目标 2 中与代谢组学、脂质组学和单细胞转录组学分析前期和早期阶段配对 利用目标 3 中的创新机器学习策略，这种方法将显着提高怀孕率。 临床“intolerome”数据库将促进对不明原因 RPL 的遗传基础的理解。 在目标 4 中构建，以促进基因型和相关基因的全球合作和管理 表型，向公众以及其他资助机构提供遗传学和组学数据及结果 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领域加在一起，我们拥有大量的人才。 RPL 患者将作为强大的招募来源，并与独特的英国合作 现有三人组的怀孕婴儿生物库预计将实现项目目标。 通过在大型和良好的研究中确定基因组对 RPL 的贡献，产生重大的临床和研究影响 表型队列并基于机器学习结合临床、建立改进的风险预测 这项工作将为 RPL 的精准医学干预奠定基础。 难以诊断并且几乎没有经过验证的治疗方法的夫妇。