Gene regulatory activity associated with polycystic ovary syndrome revealed DENND1A-dependent testosterone production

Laavanya Sankaranarayanan, Kelly J. Brewer, Shauna Morrow, Graham D. Johnson, Alejandro Barrera, Revathy Venukuttan, Ryan Sisk, Andrea Dunaif () and Timothy E. Reddy ()
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Laavanya Sankaranarayanan: Duke University
Kelly J. Brewer: Icahn School of Medicine at Mount Sinai
Shauna Morrow: Duke University Medical School
Graham D. Johnson: Duke University Medical School
Alejandro Barrera: Duke University Medical School
Revathy Venukuttan: Duke University Medical School
Ryan Sisk: Northwestern University Feinberg School of Medicine
Andrea Dunaif: Icahn School of Medicine at Mount Sinai
Timothy E. Reddy: Duke University

Nature Communications, 2025, vol. 16, issue 1, 1-19

Abstract: Abstract Polycystic ovary syndrome (PCOS) is among the most common disorders affecting up to 15% of the menstruating population globally. It is the leading cause of anovulatory infertility and a major risk factor for type 2 diabetes. Elevated testosterone levels are a core endophenotype. Despite that prevalence, the underlying causes remain unknown. PCOS genome-wide association studies (GWAS) have reproducibly mapped a number of susceptibility loci, including one encompassing a gene regulating androgen biosynthesis, DENND1A. Identifying the causal variants within these loci will provide fundamental insight into the precise biological pathways that are disrupted in PCOS. Here, we report the discovery of gene regulatory mechanisms that help explain genetic association with PCOS in the GATA4, FSHB and DENND1A loci using a combination of high throughput reporter assays, CRISPR-based epigenome editing, and genetic association analysis from PCOS case and control populations. In addition, we find that increasing endogenous DENND1A expression causes elevated testosterone levels in an adrenal cell model, specifically by perturbing candidate regulatory elements. These results further highlight the potential for combining genetic variant analyses with experimental approaches to fine map genetic associations with disease risk.

Date: 2025
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DOI: 10.1038/s41467-025-62884-7

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