Back

Sex-specific dissection of adiposity genetics reveals distinct pathways to endometrial cancer risk

Bouttle, K.; Glubb, D. M.; Thorp, J.; Ingold, N.; O'Mara, T. A.

2026-03-31 genetic and genomic medicine
10.64898/2026.03.29.26349665 medRxiv
Show abstract

Excess adiposity accounts for up to 60% of endometrial cancer cases, yet the mechanisms linking adiposity to carcinogenesis and the relevance of sex-specific adiposity genetics to disease risk have been largely unexplored. Using genomic structural equation modelling of six adiposity genome-wide association studies (GWAS), we perform the first sex-stratified adiposity common factor GWAS model, combining data from 2 million people. We identified sex differences in adiposity genetic effects and identified a fourfold larger female-specific causal genetic component relative to males. Female adiposity genetics converged on hormone-responsive and oncogenic pathways directly implicated in endometrial carcinogenesis, a specificity confirmed by stronger female adiposity genetic effects on endometrial cancer but not other hormone-related cancers. Cross-trait analysis identified 26 loci jointly associated with female adiposity and endometrial cancer, including 16 previously unreported loci. GWAS-by-subtraction revealed that only 14.1% of the genetic variance in endometrial cancer is shared with adiposity, with the remainder reflecting adiposity-independent mechanisms captured by established endometrial cancer loci. The adiposity-mediated component converged on insulin-leptin adipocyte signalling and on imprinted and pluripotency-associated developmental pathways, linked by shared nodes such as PTPN11 and PPARG. These findings recast the obesity-endometrial cancer relationship from an epidemiological observation into a mechanistically partitioned genetic programme, and underscores the importance of sex-stratified approaches to resolving how adiposity genetics contributes to disease susceptibility.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.