News · May 29, 2025
David Healy Award 2025 presented to Lindsay Guare
The David Healy Award for best Oral Presentation by an Early Career Scientist was presented to Lindsay Guare at the closing ceremony of the 16th World Congress on Endometriosis for her work on Expanding the Genetic Landscape of Endometriosis: Integrating Multi-omics with a Genome-wide Meta-analysis of Over 900,000 Genetically Diverse Women
L Guare1, J Das1, A Rajagopalan1, L Caruth1, S Namba2, Y Okada2, Y Shirai3, Y Yamamoto3, A Akerele4, J Jaworski4, D Velez-Edwards4, A Hill5, J Shortt5, N Elhadad6, G Jarvik7, L Kottyan8, Y Luo9, W Wei10, C Weng6, S Chapman11, Y Shi11, W Zhou11, A Mulford12, A Sanders12, B Brumpton13, E Moreno13, T Chen15, V Rovite14, Y Lin15,S Setia- Verma1
1University Of Pennsylvania, Philadelphia, United States
2The University of Tokyo, Japan
3Osaka University, Suita, Japan
4Vanderbilt University Medical Center, Nashville, United States
5Colorado Center for Precision Medicine, Aurora, United States
6Columbia University, New York City, United States
7University of Washington, Seattle, United States
8Cincinnati Children’s Hospital Medical Center, Cincinnati, United States
9Northwestern University, Evanston, United States
10Vanderbilt University, Nashville, United States
11The Broad Institute, Cambridge, United States
12Endeavor Health, Evanston, United States
13Norwegian University of Science and Technology, Trondheim, Norway
14Latvian Biomedical Research and Study Centre, Latvia
15National Health Research Institutes, Maoli County, Taiwan
Country: United States of America Introduction/Background
Endometriosis is a complex heritable disorder requiring comprehensive genomic investigation
across diverse populations. Previous studies have been somewhat limited by European-centric data. The Global Biobank Meta-Analysis Initiative (GBMI) enables large-scale genomic analysis across multiple genetic ancestry groups, complemented by computational multi-omic and single cell analyses to understand disease mechanisms.
Materials and Methods
We performed a Genome-Wide Association Study (GWAS) meta-analysis across 14 biobanks worldwide, with 31% non-European samples. Multiple endometriosis phenotype definitions were analyzed, including broad and surgically-confirmed cases. Post-GWAS analyses included ancestry-stratified heritability estimation and fine-mapping. We conducted Transcriptome- wide and Proteome-wide association analyses, followed by single-cell analyses of implicated genes. Integration of multi-omic data through Mergeomics analysis enabled comprehensive pathway enrichment assessment.
Results
The GWAS (N=928,413 : 44,125 cases) identified 45 significant loci using a broad phenotype definition, including seven previously-unreported signals and the first genome-wide significant locus (POLR2M) in African ancestry. Narrow phenotypes and surgically confirmed cases replicated known loci near CDC42 and SYNE1. Observed heritability was consistent (10-12%) across ancestral groups. Cross-ancestry fine-mapping revealed putative causal variants in 38 loci. Multi-omic imputed association analyses identified 11 significantly-associated gene transcripts (two previously unknown: DTD1 and CCDC88B), two intronic splicing events (within PGR and NSRP1), and one protein, RSPO3. In silico single-cell analyses prioritized 18 disease-relevant cell types including venous cells and macrophages. The results of these analyses specified key players in enriched molecular pathways involving immunopathogenesis, angiogenesis, Wnt signaling, and balance between proliferation, differentiation, and migration of endometrial cells as major hallmarks in genomics of endometriosis.
Conclusion
This diverse GWAS combined with transcriptomic, splice-omic, proteomic, and single-cell analyses revealed novel genetic associations and molecular mechanisms in endometriosis. The identification of ancestry-specific variants and pathway interactions provides multiple targets for developing precise therapeutic interventions across diverse populations.
Key words: Genome wide association analyses, multi-omic data integration, diverse populations