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

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $1,465,292

## Abstract

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.

## Key facts

- **NIH application ID:** 10225966
- **Project number:** 1R01HD105256-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Ruth B Lathi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,465,292
- **Award type:** 1
- **Project period:** 2021-05-15 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225966

## Citation

> US National Institutes of Health, RePORTER application 10225966, Trio Analysis of Recurrent Pregnancy Loss Integrated Bioinformatics Genomics Study (TRIOS) (1R01HD105256-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10225966. Licensed CC0.

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