Back

Understanding and Predicting Polycystic Ovary Syndrome through Shared Genetics with Testosterone, SHBG, and Chronic Inflammation

Petersen, L. K.; Brixi, G.; Li, J.; Hu, J.; Wang, Z.; Han, X.; Meir, A. Y.; Tyrmi, J.; Mahalingaiah, S.; Piltonen, T.; Liang, L.

2023-10-18 genetic and genomic medicine
10.1101/2023.10.17.23297115 medRxiv
Show abstract

Polycystic ovary syndrome (PCOS) is a common hormonal disorder that affects one out of eight women and has high metabolic and psychological comorbidities. PCOS is thought to be associated with obesity, hormonal dysregulation, and systemic low-grade inflammation, but the underlying mechanisms remain unclear. Here we study the genetic relationship between PCOS and obesity, testosterone, sex hormone binding globulin (SHBG), and a wide-range of inflammatory markers. First, we created a large meta-analysis of PCOS (7,747 PCOS cases and 498,227 controls) and identified four novel genetic loci associated with PCOS. These novel loci have been previously associated with gene expression in multiple PCOS-relevant tissues including the thyroid and ovary. We then further incorporated GWASs for obesity (n=681,275), SHBG (n=190,366), testosterone (n=176,687), and 138 inflammatory biomarkers (average n=30,000). Using Mendelian randomization methods, we replicated genetic causal relationships from obesity and SHBG to PCOS. We identified significant genetic correlations between PCOS and eleven inflammatory biomarkers, including novel and strong correlations with death receptor 5 (LDSC rg = 0.54, FDR = 0.043), among others. Although no statistically significant causal relationship was observed between inflammatory markers and PCOS, 31 inflammatory biomarkers showed significant causal effects on SHBG or testosterone, supporting a potentially etiological role of chronic inflammation in influencing sex hormone levels. Finally, we show that combining the polygenic risk scores of PCOS and PCOS-related traits improves genetic prediction of PCOS cases in the UK Biobank and MGB Biobank, as compared to using only the risk score of PCOS. Together, these results support the theory that immune responses are altered in PCOS patients and that chronic inflammation may play a role in testosterone dysregulation.

Matching journals

The top 10 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.