To identify loci for age at menarche, we performed a meta-analysis of 32 genome-wide association studies in 87,802 women of European descent, with replication in up to 14,731 women. In addition to the known loci at LIN28B (P=5.4×10−60) and 9q31.2 (P=2.2×10−33), we identified 30 novel menarche loci (all P<5×10−8) and found suggestive evidence for a further 10 loci (P<1.9×10−6). New loci included four previously associated with BMI (in/near FTO, SEC16B, TRA2B and TMEM18), three in/near other genes implicated in energy homeostasis (BSX, CRTC1, and MCHR2), and three in/near genes implicated in hormonal regulation (INHBA, PCSK2 and RXRG). Ingenuity and MAGENTA pathway analyses identified coenzyme A and fatty acid biosynthesis as biological processes related to menarche timing.

To identify novel loci for age at natural menopause, we performed a meta-analysis of 22 genome-wide association studies in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 new age at natural menopause loci (P < 5 × 10−8). The new loci included genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG, PRIM1) and immune function (IL11, NLRP11, BAT2). Gene-set enrichment pathway analyses using the full GWAS dataset identified exodeoxyribonuclease, NFκB signalling and mitochondrial dysfunction as biological processes related to timing of menopause.

We conducted a meta-analysis of genome-wide association data to detect genes influencing age at menarche in 17,510 women. The strongest signal was at 9q31.2 (P = 1.7 × 10 −9 ), where the nearest genes include TMEM38B, FKTN, FSD1L, TAL2 and ZNF462. The next best signal was near the LIN28B gene (rs7759938; P = 7.0 × 10 −9 ), which also influences adult height. We provide the first evidence for common genetic variants influencing female sexual maturation.Menarche is the start of menstruation and occurs at a mean age of approximately 13 years, normally about 2 years after the onset of puberty 1 . Twin and family studies suggest a significant genetic component to menarcheal age, with at least 50% heritability 2-4 . Linkage and candidate gene studies have not confirmed any loci that influence normal variation in age at menarche 4,5 . Genome-wide association (GWA) studies have been successful in identifying many variants associated with complex disease and quantitative traits and we therefore used this approach to identify genes involved in determining age at menarche. As earlier age at menarche is associated with shorter stature and obesity, the identified variants may not only clarify the genetic control of female sexual maturation but may also point to regulatory mechanisms involved in normal human growth and obesity.We carried out a meta-analysis of 17,510 females from eight different population-based cohorts: Age/Gene Environment Susceptibility-Reykjavik Study (AGES-Reykjavik), Atherosclerosis Risk in Communities (ARIC) Study, Framingham Heart Study (FHS), Amish HAPI Heart Study, InCHIANTI Study, Rotterdam Study I and II and TWINS UK Study (Supplementary Note online). Women of European descent, with self-reported age at menarche between 9 and 17 years (representing the 1st to 99th percentile, with mean age at menarche of 13.12 (s.d. 1.5) years), were included. Agreement between adult-recalled and prospectively collected age at menarche is reported to be good (κ statistic = 0.81) 6 . Each study conducted a GWA analysis using linear regression or linear mixed-effects models with an additive genetic model adjusting for birth year or birth cohort (FHS), with additional adjustments for population structure when appropriate. Approximately 2.55 million autosomal SNPs, imputed with reference to the HapMap CEU panel, passed quality control criteria. We then conducted a metaanalysis using a fixed-effects model based on inverse variance weighting. Full details of cohorts and methods are given in Supplementary Table 1 and Supplementary Methods online.Twenty-eight SNPs passed the conventional genome-wide significance threshold of P < 5 × 10 −8 and were at either 9q31.2 or 6q21 (Supplementary Table 2 and Supplementary Fig. 1 online). The 18 SNPs on chromosome 9 were in linkage disequilibrium (LD), with r 2 > 0.31, as were the 10 SNPs on chromosome 6, with r 2 > 0.96 ( Fig. 1 and Supplementary Table 2). To identify more than one signal that could account for the association findings, we carried out conditional analysis ...

Testosterone concentrations in men are associated with cardiovascular morbidity, osteoporosis, and mortality and are affected by age, smoking, and obesity. Because of serum testosterone's high heritability, we performed a meta-analysis of genome-wide association data in 8,938 men from seven cohorts and followed up the genome-wide significant findings in one in silico (n = 871) and two de novo replication cohorts (n = 4,620) to identify genetic loci significantly associated with serum testosterone concentration in men. All these loci were also associated with low serum testosterone concentration defined as <300 ng/dl. Two single-nucleotide polymorphisms at the sex hormone-binding globulin (SHBG) locus (17p13-p12) were identified as independently associated with serum testosterone concentration (rs12150660, p = 1.2×10−41 and rs6258, p = 2.3×10−22). Subjects with ≥3 risk alleles of these variants had 6.5-fold higher risk of having low serum testosterone than subjects with no risk allele. The rs5934505 polymorphism near FAM9B on the X chromosome was also associated with testosterone concentrations (p = 5.6×10−16). The rs6258 polymorphism in exon 4 of SHBG affected SHBG's affinity for binding testosterone and the measured free testosterone fraction (p<0.01). Genetic variants in the SHBG locus and on the X chromosome are associated with a substantial variation in testosterone concentrations and increased risk of low testosterone. rs6258 is the first reported SHBG polymorphism, which affects testosterone binding to SHBG and the free testosterone fraction and could therefore influence the calculation of free testosterone using law-of-mass-action equation.

Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.