Laura J Rasmussen-Torvik

PubMed2026

Genetically-predicted placental gene expression links to uterine fibroids and endometriosis.

INTRODUCTION: Mother-to-child disease transmission begins in utero, with the placenta playing a critical role in pregnancy and offspring health. Uterine leiomyomata (fibroids, UFs) and endometriosis (ENDO) are common gynecologic diseases that have substantial overlaps in symptomology and risk factors, however drivers of disease risk remain unclear. The objective of this study was to investigate shared placental genetic associations across ENDO and UFs. METHODS: Genome-wide association study (GWAS) summary statistics were utilized from a published study of UFs (PMID: 40050615) and meta-analyzed for ENDO (24,092 cases and 548,255 controls). To improve our statistical power, we applied Multi-Trait Analysis of GWAS to the ENDO and UF GWAS. We estimated genetically predicted gene expression using S-PrediXcan across 49 tissues using GTEx v7 and a placental tissue expression model. RESULTS: We identified 54 and 14 genes where predicted expression in the placenta was significantly associated with UFs and ENDO, respectively. Twenty-one of these genes were shared between UFs and ENDO. Significant gene associations in placenta tissue were compared to the other 48 GTEx v7 tissue types to identify placenta specific associations. There were 40 and 13 significant gene-tissue associations specific to the placenta across UFs and ENDO, respectively. Eight of the placenta-specific genes were shared across UFs and ENDO. The strongest shared placenta-specific associations included PRKCI and HRH1. CONCLUSIONS: Our findings demonstrate a shared genetic relationship between UFs and ENDO in the placenta. The placenta specific associations suggest that dysregulation of early developmental pathways may contribute to a shared genetic origin of these diseases.

Female↗

PubMed2025

Covering medical care costs for participants in the eMERGE Network: Challenges for equity and implementation.

PURPOSE: To investigate the complexities of covering study-recommended medical care costs for individuals (in order to prevent lack of adherence due to financial reasons), which have received little attention. METHODS: We explored the deliberations, decisions, and challenges faced by the Electronic Medical Records and Genomics (eMERGE) Network during the implementation of a genomic research project recommending clinical care based on high-risk results defined largely by polygenic risk scores. Two surveys were disseminated to eMERGE sites: to identify preferences about payment for specific care recommendations (survey 1) and to understand the operational processes of covering medical care costs (survey 2). RESULTS: Paying for a subset of care recommendations for the funded study duration was identified as the most feasible approach for covering medical care costs for participants who received high-risk genomic results. Each eMERGE site, by necessity, used diverse approaches to pay for medical care costs. CONCLUSION: eMERGE researchers balanced competing concerns about bias, equity, study design, regulatory compliance, and cost in designing a unified approach to cover some of the recommended medical care costs in the study. Many implementation challenges were encountered. Findings can inform researchers and regulatory bodies about the implications and complications of covering medical care costs in translational research studies focused on prevention.

Humans↗