Secondly , given the genetic overlap between traits , we observed that some signals were shared between sex hormone traits but appeared to have much stronger effects in one versus others . To help derive additional genetic risk scores that reflected this , we took all genome - wide significant signals within each sex but across traits , and performed ward - based hierarchical clustering 35 on individual variant Z scores . We used the observed clusters from these analyses to produce additional genetic instruments ( Supplementary Table 16 ): 8 . A ' male SHBG cluster ' ( N = 362 ) formed from SNPs with dominant effects on SHBG in men . Each SNP in this genetic instrument is weighted by its effect from the BMI - unadjusted SHBG analysis . 9 . A ' male testosterone cluster ' ( N = 122 ) formed from SNPs with dominant effects on both total and bioavailable testosterone in men . Each SNP in this genetic instrument is weighted by its effect on total testosterone . 10 . A ' male estradiol cluster ' ( N = 14 ) formed from SNPs with dominant effects on estradiol in men . 11 . A ' female SHBG cluster ' ( N = 373 ) formed from SNPs with dominant effects on SHBG in women . Each SNP in this genetic instrument is weighted by its effect from the BMI - unadjusted SHBG analysis . 12 . A ' female testosterone cluster ' ( N = 241 ) formed from SNPs with dominant effects on both total and bioavailable testosterone in women . Each SNP in this genetic instrument is weighted by its effect on total testosterone . Gene prioritization . We used the SMR software package 36 to systematically map associated genetic variants to genes via expression effects ( eQTLs ) . For all analyses we included expression data from liver , in addition to skeletal muscle in men and adrenal gland in women . All expression data were generated by the GTEx consortium ( v7 ) , made available from the SMR website resource section ( https:// cnsgenomics.com/software/smr/#DataResource ) . Only genes passing multiple test correction and exhibiting no statistically significant evidence of coincidental eQTL overlap ( assessed by the SMR HEIDI metric ) were considered . The same data were additionally used to perform global tissue enrichment using linkage disequilibrium score regression applied to specifically expressed genes ( LDSC - SEG ) 37 .