# Large scale genome sequencing and integrative analyses to define genomic predictors of recurrent pregnancy loss

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $1,509,604

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10393656
- **Project number:** 5R01HD105267-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Ira M Hall
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,509,604
- **Award type:** 5
- **Project period:** 2021-04-15 → 2026-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10393656

## Citation

> US National Institutes of Health, RePORTER application 10393656, Large scale genome sequencing and integrative analyses to define genomic predictors of recurrent pregnancy loss (5R01HD105267-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10393656. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*