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

项目摘要 复发性流产（RPL）影响多达5%的夫妇，但近一半的病例仍然无法解释， 当前测试建议。在不明原因的RPL的情况下， 令患者和提供者感到沮丧，因为没有明确的解释或任何经证实的疗法来减轻 后续流产的风险。由于临床表现和随后的妊娠结局差异很大， 复杂的疾病最终需要精确的健康方法。虽然超过3000个人类基因 保守的，可能是必要的早期发展，显着很少知道他们的贡献，RPL 并且当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 来诠释这些必需基因的表型和基因型。该提案旨在定义遗传 通过大型RPL队列的临床和分子表型以及基因组测序确定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 通过全基因组测序确定妊娠丢失的基因组标记的综合方法， 将在目标1中进行特征化的RPL三重（母亲-父亲-妊娠丢失）。这些遗传学研究将 在目标2中与代谢组学、脂质组学和单细胞转录组学分析配对， 怀孕利用Aim 3中的创新机器学习策略，这种方法将大大提高 进一步了解无法解释的RPL的遗传基础。临床“不耐受组”数据库将 在目标4中构建，以促进基因型和相关基因型的全球合作和管理。 表型，使遗传学和组学数据和结果提供给公众以及其他资助 团队这个多学科团队包括RPL，遗传学，基因组学，产前诊断， 生物信息学和机器学习在斯坦福大学，加州大学旧金山分校和OHSU。结合起来，我们有一个庞大的队列， RPL患者将作为一个强大的招募来源，沿着与独特的英国 怀孕婴儿生物银行现有的三人组，以完成项目目标。预计拟议的研究将 通过在大的和良好的细胞中鉴定对RPL的基因组贡献， 表型队列和建立基于机器学习的改进的风险预测， 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 很难诊断并且几乎没有经过验证的治疗方法的夫妇。

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.