In order to generate fully functional and high-quality organelles, a precise coordination between the nuclear and mitochondrial genomes is imperative to ensure the production of protein products in the correct stoichiometry. With advancing age, mitochondria are often reported to diminish in volume and function in muscle, establishing a correlation between the progressive decline in skeletal mitochondrial function and aging, wherein physical strength diminishes disproportionately to muscle mass loss 11,12. Another limitation of our study was that underlying mechanisms explaining the results have not been assessed. Therefore, the combination of the 5 SNPs identified in this study could somehow favor the hypertrophy of fast-twitch muscle fibers and strength performance. Plots show effect on ln(odds) of Type 2 diabetes in women (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). Plots show effect on ln(odds) of PCOS (y axes) of the following sex hormone genetic instruments in women (x axes; effect size in units). Dot plots representing the change in the odds of the following cancers per unit higher sex hormone in males or females, as appropriate. Finally, replication of the genetic scores was attempted with measurements of total testosterone (5,334 men and 3,804 women) and of SHBG (5,694 men and 5,476 women) from the EPIC Norfolk study34. Genotyping chip, age at baseline and 10 genetically derived principal components were included as covariates in all models, in addition to specific covariates used for individual traits detailed in Supplementary Table S1. We performed power analyses on the Cox models using the powerEpiCont.default from powerSurvEpi package in R. Cox proportional hazards models were used for estimating hazard ratios (HRs) and 95% CIs, with age as the time scale and 10 first principal components of ancestry and genotyping batch as covariates. The summary statistics for the 44 traits were downloaded directly from the source repositories and analyzed locally, for the original sources please see references in Supplementary Data 11. To evaluate the PGS prediction accuracy in the YFS, we calculated the R2 for each trait using using linear regression with z-score normalized PGS as predictor, age and 10 PCs as covariates and z-score normalized T trait as outcome. We selected 44 traits with publicly available GWAS summary statistics, identical to (e.g., T2D, breast and prostate cancers) or closely reflecting the studied disease phenotypes (heel bone mineral density (HBMD), mood swings) from FinnGen, adding anthropometric traits to the analyses (Supplementary Data 11). This was the only endpoint for which we detected evidence for sexual antagonism. Case and control numbers for each endpoint are available from Supplementary Data 9. A Cross-sex PGS associations with hypothyroidism, statin use, T2D and stroke. The figure includes traits from the endocrine, metabolic, circulatory and sex-specific categories shown in Fig. In FinnGen, there was only modest case and genetic overlap between PCOS, hirsutism and PMB cases, potentially reflecting underdiagnosis of PCOS in the dataset65. B Shows hazard ratios per one SD increase in PGS for 20 traits from endocrine, metabolic, circulatory and sex-specific categories. The associations involved endocrine, metabolic and sex-specific disorders, highlighting in particular female-specific endpoints (Fig. 2 and and Supplementary Data 7). In short, these analyses allow for estimating the consequences of having a genetic predisposition to higher or lower T levels (Supplementary Fig. 6).
