Article Text

Original article
Genome-wide association study of sex hormones, gonadotropins and sex hormone–binding protein in Chinese men
  1. Zhuo Chen1,
  2. Sha Tao1,
  3. Yong Gao2,3,
  4. Ju Zhang4,
  5. Yanling Hu2,5,
  6. Linjian Mo2,3,
  7. Seong-Tae Kim1,
  8. Xiaobo Yang2,6,
  9. Aihua Tan2,
  10. Haiying Zhang2,6,
  11. Xue Qin2,7,
  12. Li Li5,
  13. Yongming Wu2,3,
  14. Shijun Zhang2,
  15. S Lilly Zheng1,
  16. Jianfeng Xu1,2,8,9,
  17. Zengnan Mo2,3,
  18. Jielin Sun1
  1. 1Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  2. 2Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
  3. 3Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
  4. 4State Key Laboratory of Medicinal Chemical Biology and Department of Biochemistry and Molecular Biology, College of Life Sciences, Nankai University, Tianjin, China
  5. 5Medical Scientific Research Center, Guangxi Medical University, Nanning, Guangxi, China
  6. 6Department of Occupational Health and Environmental Health, School of Public Health, Guangxi Medical University, Nanning, Guangxi, China
  7. 7Department of Clinical Laboratory, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
  8. 8Fudan-VARI Center for Genetic Epidemiology, School of Life Sciences, Fudan University, Shanghai, China
  9. 9Fudan University Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
  1. Correspondence to Dr Zengnan Mo, Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, China; zengnanmo{at}


Background Sex hormones and gonadotropins exert a wide variety of effects in physiological and pathological processes. Accumulated evidence shows a strong heritable component of circulating concentrations of these hormones. Recently, several genome-wide association studies (GWASs) conducted in Caucasians have identified multiple loci that influence serum levels of sex hormones. However, the genetic determinants remain unknown in Chinese populations. In this study, we aimed to identify genetic variants associated with major sex hormones, gonadotropins, including testosterone, oestradiol, follicle-stimulating hormone (FSH), luteinising hormone (LH) and sex hormone binding globulin (SHBG) in a Chinese population.

Methods A two-stage GWAS was conducted in a total of 3495 healthy Chinese men (1999 subjects in the GWAS discovery stage and 1496 in the confirmation stage).

Results We identified a novel genetic region at 15q21.2 (rs2414095 in CYP19A1), which was significantly associated with oestradiol and FSH in the Chinese population at a genome-wide significant level (p=6.54×10−31 and 1.59×10−16, respectively). Another single nucleotide polymorphism in CYP19A1 gene was significantly associated with oestradiol level (rs2445762, p=7.75×10−28). In addition, we confirmed the previous GWAS-identified locus at 17p13.1 for testosterone (rs2075230, p=1.13×10−8) and SHBG level (rs2075230, p=4.75×10−19) in the Chinese population.

Conclusions This study is the first GWAS investigation of genetic determinants of FSH and LH. The identification of novel susceptibility loci may provide more biological implications for the synthesis and metabolism of these hormones. More importantly, the confirmation of the genetic loci for testosterone and SHBG suggests common genetic components shared among different ethnicities.

  • GWAS
  • sex hormone
  • FSH
  • estradiol
  • CYP19A1

Statistics from


Sex hormones are mostly synthesised from cholesterol in the gonads (testes and ovaries). A small amount of sex hormone is synthesised in the adrenal glands or is conversed from other sex steroids in peripheral tissues such as liver, fat or skin.1 Testosterone is the principal sex hormone in males while oestradiol is the predominant hormone in females. Sex hormones are responsible for primary sex characteristics development and secondary sex characteristics. In addition, they play an important role in a variety of physiological and pathological processes, including carcinogenesis,2 ,3 osteoporosis and fracture,4 ,5 cardiovascular diseases,6 metabolic syndrome,7 diabetes mellitus8 and depression.9

Testosterone and oestradiol synthesis and secretion are commonly regulated by the hypothalamic pituitary gonads (HPG) axis. Follicle-stimulating hormone (FSH) and luteinising hormone (LH), which are often referred to as gonadotropins, are key components of the HPG axis. Although named after their function in females, FSH and LH are glycopeptides secreted by the pituitary gland in both females and males. In females, FSH and LH act primarily to activate the ovaries to produce oestrogen and to regulate the menstrual and ovarian cycles.10 In males, FSH stimulates sustentacular cells to release androgen-binding protein, which promotes testosterone binding. LH binds to the interstitial cells, which in turn, secrete testosterone.11 Sex hormone binding globulin (SHBG) is a glycoprotein that binds to testosterone and oestradiol with high affinity and specificity. The circulating testosterone and oestradiol bind mostly to SHBG, leaving only 1%–3% bioactive-free form.12 ,13 Moreover, SHBG has been found to play an important role in hormone-sensitive cancers, such as prostate cancer and breast cancer.14 ,15

Concentrations of sex hormones have been shown to be heritable. Twin studies reported that up to 60% of normal variation in sex hormones and SHBG within a population may be due to genetic variation.16 ,17 Recently, multiple genetic variants, which influence the concentrations of sex hormones, have been identified through genome-wide association studies (GWAS). To date, five GWAS on serum levels of testosterone, oestradiol and SHBG have been conducted in Caucasian populations.18–22 For testosterone, a GWAS by Ohlsson et al20 identified three single nucleotide polymorphisms (SNPs) in 8938 men, including two independent genetic loci nearby or within SHBG gene region at 17p13 (p=1.2×10−41 for rs12150660, p=2.3×10−22 for rs6258) and one locus in the family with sequence similarity 9, member B gene (FAM9B) at Xp22 (=5.6×10−16). The two genomic regions (17p13 and Xp22) were further confirmed in 1244 men in the Reduction by Dutasteride of Prostate Cancer Events (REDUCE) trial (p=1.26×10−12 for rs727428 in SHBG at 17p13 and p=1.61×10−8 for rs5934505 in FAM9B at Xp22).18 In addition to 17p13 and Xp22, a novel susceptibility loci in the jumonji domain containing 1C gene (JMJD1C) at 10q21 for testosterone (p=1.12×10−8 for rs10822184) was also identified in the REDUCE study.18 However, none of these SNPs identified in the men above was associated with testosterone-circulating level in postmenopausal women.19 For oestradiol, only one GWAS study has been conducted to date. The study by Prescott et al19 was conducted in ∼1600 postmenopausal women and did not detect any association at a genome-wide significant level (p=5×10−8). For SHBG, 12 genomic regions have been identified, including 1p13.3, 2p16.3, 2p23.3, 4q13.2, 7q21.3, 8q21.13, 10q21.3, 12p12.1, 15q26.2, 17p13.1, 17q21.32 and Xq2.3.19–22

Recently, a review by Vandenput and Ohlsson provided an overview of the genomic loci identified in GWAS for sex steroid and SHBG levels.23 Although the identified loci provided insight into sex hormone genetics, they could only explain a relatively small proportion of the total variations of serum sex hormone levels among individuals with European ancestry.23 It remains uncertain whether the findings in Caucasians could be applied to other populations. In addition, no GWAS on FSH and LH has been performed yet. More importantly, to the best of our knowledge, no GWAS has been conducted to identify genetic loci determining the serum level of sex hormones in Asian populations. To answer the above questions, we conducted a GWAS among 3495 men from the Fangchenggang Area Male Health and Examination Survey (FAMHES)24 to identify genetic loci influencing serum levels of testosterone, oestradiol, FSH, LH and SHBG in a Chinese population.

Materials and methods

Study population

Subjects included in the GWAS were selected from FAMHES.24 FAMHES reviewed 4364 men that had routine physical examinations at the Medical Centre of Fangchenggang First People's Hospital, Fangchenggang, Guangxi Province, from September 2009 to December 2009; 4303 of the men consented to participate in FAMHES and donated blood samples. In addition, each of them received professional assistance in completing a structured questionnaire, which elicited information on demographic features, use of sex steroid hormones, medical history, physical activity, dietary habits and family history of cancer. Among them, those who met any of the following criteria were excluded from further study: (1) age <20 or age >69; (2) not Chinese Han ethnicity; (3) currently diagnosed with diabetes mellitus, coronary heart disease, stroke, hyperthyroidism, rheumatoid arthritis and cancer; (4) taking any kind of medication; (5) with impaired hepatic function (alanine transaminase >2.0 times upper limit of normal); or (6) with impaired renal function (serum creatinine >178 µmol/L). Participants that presented with signs of high-fasting blood glucose levels at the time of initial assessment were included in the study. In the end, 1999 men were eligible for the GWAS study. Baseline information for all FAMHES participants is presented in online supplementary table S3.

The replication samples in stage 2 were independently recruited from the stage 1 samples using the same recruitment strategy and inclusion or exclusion criteria except ethnicity. The combined set consisted of 1496 men aged 20–69 years, with 996 participants of Han ethnicity and 500 of Zhuang ethnicity. These participants were recruited in conjunction with health examinations that were performed at three collaborating hospitals from September 2009 to September 2010 in Guangxi, China.

Written informed consent was obtained from each participant, and the study was approved by the local Ethics Committee.

Clinical trait measurements

We collected blood samples after participants fasted overnight. Samples were collected between 8:00 AM and 10:00 AM to minimise the effects of diurnal rhythm. Serums were prepared from clotted blood and stored at −80°C until use. Serum testosterone, oestradiol, FSH, LH and SHBG concentrations were quantified by electrochemiluminescence immunoassay on the COBAS 6000 system E601 (Elecsys module) immunoassay analyzer (Roche Diagnostics, GmbH, Mannheim, Germany) with the same batch of reagents. For quality control (QC), Elecsys reciControl Universal 1 and 2, that is, low and high concentrations, were run as single determinations with each batch of analyses. For the initial association study in stage 1, the interassay coefficients of variation of the assays were 3.6% for testosterone, 3.4% for oestradiol, 3.6% for FSH, 4.3% for LH and 4.4% for SHBG. The interassay coefficients of variation for the replication study in stage 2 were 3.4% for testosterone, 3.8% for oestradiol, 2.0% for FSH, 2.6% for LH and 2.1% for SHBG. All assays and QC were performed according to the manufacturer's instructions and standard QC protocols. There were five subjects with one or more values of hormones beyond the detection range, including four subjects with oestradiol measurements below the lower detection limit (5 pg/mL) and one subject with SHBG measurements exceeding the upper detection limit (200 nmol/L). The values of these subjects were treated as lower limit or upper limit of measurements in statistical analysis.

Genotyping, imputation and QC

Two different platforms were used for SNP genotyping. The Illumina Omini1 platform was used for the genome-wide assay of samples in stage 1, and the Sequenom iPLEX (Sequenom, Inc, San Diego, California, USA) platform was used for genotyping in stage 2. PCR and extension primers were designed using MassARRAY Assay Design V.3.1 software (Sequenom, Inc). Genotyping procedures were performed according to the manufacturer's iPLEX Application Guide (Sequenom, Inc). All genotyping reactions were performed in 384-well plates. Each plate included a duplicate for three or four subjects selected at random, as well as six to nine negative controls in which water was substituted for DNA. The average concordance rate was 99.8%.

QC procedures were first applied to 1999 individuals that were genotyped using the Illumina Omni platform. All individuals passed the QC with an average overall call rate of 95% and were used in the final statistical analyses. A total of 967 030 SNPs were genotyped. Among them, SNPs that did not meet the following criteria were excluded from statistical analysis: p<0.001 for the Hardy–Weinberg Equilibrium test (N=11 651), minor allele frequency (MAF) <0.01 (N=207 684) and genotype call rate <95% (N=50 039). After QC, 709 211 SNPs were used for the final genome-wide association analysis. The 1000 Genomes Project phase 1 data were then used as reference haplotype map to infer the genotypes of SNPs that were not directly genotyped within 15q21.2 and 17p13.1 using IMPUTE2 software.25 A posterior probability of >0.90 was applied to call imputed genotypes. The same QC procedures were applied to imputed SNPs as to genotyped SNPs.

Statistical analysis

The associations between clinical traits and SNPs were evaluated using a linear regression model assuming additive effects of the alleles (0, 1 and 2). Oestradiol, FSH, SHBG and body mass index (BMI) were log-transformed to approximate a normal distribution. Testosterone, LH and age were normally distributed, and therefore, were not transformed in the analysis. Age and log-BMI were adjusted as covariates in the regression model. The PLINK software package was used to perform statistical analysis.26 Population stratification was estimated by a principal component approach, as implemented by EIGENSTRAT software.27 The top two eigenvectors were adjusted as covariates as well. For testosterone, SNPs that achieved genome-wide significance were further investigated for association, after adjustment for smoking as an additional covariate, in subjects of stage 1 and stage 2. In addition, Pearson correlation coefficients were estimated between hormones.

For regions with multiple SNPs that were significant at p<10−5, a multivariate linear regression analysis was applied to test the independence of the respective SNPs. Only the SNPs (except rs11078697) that remained significant (p<10−5) in the multivariate analysis were selected, thus yielding one SNP per region to be followed in the second stage. The combined analysis of two-stage data was performed using a linear regression model, adjusting for the covariates and stage information.

In addition, we conducted multivariate linear regression analysis in 1999 subjects of stage 1 for SNPs that associated with hormones in Caucasians in previous studies, adjusting for the top SNP identified in the Chinese population. Age, log-BMI and the top 2 eigenvectors were also adjusted as covariates in the regression model.


Demographic characteristics

Characteristics for the two-staged GWAS subjects are summarised in table 1. No significant differences were found between samples of stages 1 and 2 regarding the mean age, smoking distribution and mean BMI (p>0.05). The concentrations of testosterone, oestradiol, FSH, LH and SHBG were generally within normal ranges, and no significant difference was observed between the two stages (p>0.05).

Table 1

General characteristics of the study population

Genome-wide association study

We performed a genome-wide association test for testosterone, oestradiol, FSH, LH and SHBG on 709 211 genotyped SNPs in 1999 men from FAMHES. Age, log-BMI and the top 2 eigenvectors were adjusted as covariates. Principal component analysis of GWAS samples for the first two dimensions is presented in online supplementary figure S1. The −log10 p by chromosome location for testosterone, SHBG, oestradiol, LH and FSH are shown in figures 1A,B and 2A–C, respectively. Quantile–quantile (Q–Q) plots are presented in online supplementary figure S2A–E. The inflation factors range from 1.015 to 1.066, indicating a minimum impact for population stratification. Thus, the reported p values are not corrected for genomic inflation factors. A total of 32 SNPs that showed independent association (except rs11078697, r2=0.49 with rs2075230) with a p<1.0×10−5 were investigated in stage 2, including 6 SNPs for testosterone, 10 SNPs for FSH, 8 SNPs for oestradiol, 8 SNPs for LH and 2 SNPs for SHBG (see online supplementary table S1). Among these SNPs, rs2075230 was associated with both testosterone and SHBG while rs2414095 was associated with both oestradiol and FSH.

Figure 1

(A and B) Manhattan plot of genome-wide association analyses for testosterone and sex hormone binding globulin. X-axis shows chromosomal positions. Y-axis shows −log10 p from linear regression. The horizontal solid line indicates the preset threshold of p=1×10−7. Access the article online to view this figure in colour.

Figure 2

(A–C) Manhattan plot of genome-wide association analyses for oestradiol, luteinising hormone and follicle-stimulating hormone. X-axis shows chromosomal positions. Y-axis shows −log10 p from linear regression. The horizontal solid line indicates the preset threshold of p=1×10−7. Access the article online to view this figure in colour.

A combined analysis (adjusted for age, log-BMI and stage) was conducted for all individuals included in stages 1 and 2. As shown in table 2, three SNPs showed genome-wide association with a combined p<5×10−8, including rs2075230 for testosterone (p=1.13×10−8) and SHBG (p=4.75×10−19); rs2414095 for oestradiol (p=6.54×10−31) and FSH (p=1.59×10−16); and rs2445762 for oestradiol (p=7.75×10−28). We found that rs2075230 contributed to 0.7% variance of testosterone and 1.2% variance of SHBG; rs2414095 contributed to 3.9% variance of oestradiol and 1.5% variance of FSH; and rs2445762 contributed to 3.2% variance of oestradiol. All three SNPs were genotyped SNPs. Associations including both genotyped and imputed data at the two loci are presented in online supplementary figure S3. LD structure from 7409–7479 kb at chromosome 17 is provided in online supplementary figure S4. We did not observe any significant association at a genome-wide level (p<5×10−8) for LH (see online supplementary table S1). Since rs2414095 and rs2445762 were located within the same gene (CYP19A1), we performed multivariate logistic regression in all 3495 subjects in this study to further investigate whether rs2414095 and rs2445762 affected oestradiol independently. We found that rs2414095 remained genome-wide significant (p=2.92×10−16) after adjusting for rs2445672 as covariate; rs2445762 remained genome-wide significant after adjusting for rs2414095 as covariate (p–1.16×10−12).

Table 2

SNPs associated with testosterone, oestradiol, FSH and SHBG at genome-wide level

Smoking was reported to influence circulating testosterone level in men28 ,29; therefore, we performed linear regression for rs2075230, with smoking status as an additional covariate in the model. In stage 1, rs2075230 was associated with testosterone level with β=−0.25, SE=0.06, p=1.69×10−5; in stage 2, rs2075230 was associated with testosterone level with β=−0.32, SE=0.09, p=1.82×10−4. The effect sizes of rs2075230 on testosterone level were similar to those shown in table 2, which were not adjusted for smoking status.

Considering subjects included in stage 2 were of different ancestry, we carried out stratified analysis in 996 subjects of Chinese Ancestry and 500 Subjects of Chinese Zhuang Ancestry. According to the stratified analysis (see online supplementary table S2), we observed little difference of minor allele frequencies between Chinese Han and Zhuang populations. In addition, the magnitudes of associations in the two populations were similar.

In addition, the previous GWAS reported loci for testosterone or SHBG levels in Caucasians were also examined in our first-stage samples. These loci included 5 SNPs in three genomic regions for testosterone and 11 SNPs in nine genomic regions for SHBG levels. However, two SNPs, rs12150660 and rs6258 at 17p13, were not polymorphic in the Chinese population. Therefore, association results of the remaining 3 SNPs for testosterone and 10 SNPs for SHBG level are presented in table 3. For testosterone, none of the three SNPs identified in Caucasians showed significant association in the Chinese population of this study (p>0.05). For serum SHBG level, three SNPs showed consistent associations with the original reports, including rs780093 at 2p23 (p=1.48×10−4), rs8023580 at 15q26 (p=1.47×10−5) and rs727428 at 17p13 (p=2.57×10−5). Since rs727428 was located 50.7 kb downstream of the top SNP (rs2075230), we conducted multivariate linear regression analysis to explore their independent effect on SHBG level. We found that rs727428 was not significant after adjusting for rs2075230 (p=0.75) as covariate. However, rs2075230 remained genome-wide significant (p=8.89×10−8) after adjusting for rs727428.

Table 3

Results for SNPs associated with testosterone and SHBG (p<5×10−8 in previous GWAS) in our first-stage GWAS (N=1999)


Using a two-stage design with a total sample size of 3495 healthy men, we conducted a GWAS study to identify genetic loci influencing five sex hormones, including testosterone, oestradiol, FSH, LH and SHBG. We successfully identified two genomic regions on 17p13.1 and 15q21.1 that are significantly associated with serum level of testosterone, SHBG, oestradiol and FSH. To the best of our knowledge, our study represents the first GWAS conducted for FSH and LH for any ethnicities and is the first GWAS study in the Chinese population for testosterone, oestradiol and SHBG.

Genomic region 15q21.1 was significantly associated with serum level of oestradiol. The most significant SNP, rs2414095, is located in the third intron of the CYP19A1 gene. Another SNP, rs2445762, is located in the first intron of the CYP19A1 gene. These two SNPs were in low LD with an r2 of 0.15 and affect oestradiol level independently. CYP19A1 encodes aromatase, an enzyme that catalyses the conversion of the C19 androgens, androstenedione and testosterone to the C18 oestrogens, oestrone and oestradiol, respectively. Genetic variants in the CYP19A1 gene have been identified as candidate loci that may influence oestradiol-circulating level. An association study using a candidate gene approach found that rs727479 in CYP19A1 (p=5.06×10−5) was associated with oestradiol-circulating level in Caucasian men from the BPC3 cohort (NCI-Breast and Prostate Cancer Cohort Consortium).30 Consistent with this finding, a GWAS performed by Prescott et al19 also suggested a potential role of CYP19A1 genetic variants in determining oestradiol concentration in postmenopausal women, although this SNP did not reach a genome-wide significant level of 5×10−8 (rs727479, p=5.11×10−7). The SNP rs727479 is located in the second intron of CYP19A1 and is in complete LD (r2=1, HapMap CHB+JPT) with the top hit (rs2414095) in our Chinese population.

In addition, rs2414095 was also significantly associated with FSH (p=1.59×10−16). The minor allele of rs2414095 was associated with higher FSH but lower oestradiol level, with β=0.12 (95% CI 0.09% to 0.15%) and −0.09 (95% CI −0.11% to −0.07%). Therefore, men who carry the ‘A’ allele of rs2414095 have lower oestradiol levels but higher FSH levels. Similar to rs2414095, the minor allele ‘C’ of rs2445762 was also associated with lower oestradiol (β=−0.06) level and higher FSH level (β=0.06), although the p value did not achieve genome-wide significant level (p=0.0007). Previous functional studies have observed a direct inhibitory effect of oestradiol on gonadotropin (FSH and LH) secretion at the pituitary level in men.31 ,32 In our study, oestradiol was negatively correlated with FSH (correlation coefficient −0.20, see online supplementary table S4), which helped to explain the opposite effect that rs2414095 exerted on serum oestradiol and FSH levels. However, more studies are needed to clarify the molecular mechanisms through which rs2414095 affects FSH-circulating and oestradiol-circulating levels in an opposite direction.

Besides oestradiol and FSH, SNPs with genome-wide significance were also identified for testosterone and SHBG in this study. The minor allele ‘G’ of rs2075230 was associated with both lower circulating testosterone and lower SHBG level, which might be explained by the fact that most circulating testosterones bind to SHBG. Association results for testosterone and SHBG need to be interpreted with caution due to the correlation of serum testosterone and SHBG levels (Pearson correlation coefficient=0.66, see online supplementary table S4).

Moreover, we compared the associations in the Chinese population of this study with the results of previous studies in Caucasians. For testosterone, none of the previously reported SNPs in Caucasians (rs10822186, rs727428 and rs5934505)18 ,20 was significantly associated with testosterone in our study (p=0.10, 0.21 and 0.28). Among the SNPs associated with SHBG in Caucasians,1922 three were significantly associated with SHBG in our study, including rs780093 at 2p23 (p=1.48×10−4), rs8023580 at 15q26 (p=1.47×10−5) and rs727428 at 17p13 (p=2.57×10−5). For rs780093, MAF was similar between Chinese (MAF=0.38) and Caucasians (MAF=0.40),22 and the minor allele was associated with lower SHBG level in both Chinese and Caucasians (β=−0.04 and −0.03, respectively); for rs8023580, although MAF was slightly different between Chinese (0.45) and Caucasians (0.28), the minor allele had similar effect on SHBG (β=0.05 and 0.03 in Chinese and Caucasians, respectively)22; for rs727428, which was located 50.7 kb upstream of rs2075230 (R2=0.45), we found that the association between rs727428 and SHBG disappeared (p=0.75) after adjusting for the top SNP rs2075230, indicating that rs727428 was not likely to affect serum SHBG level independently in Chinese men.

The major advantages of this study design are (1) healthy participants were recruited from one centralised resource and (2) stringent inclusion and exclusion criteria were used. However, the limitations of our study should be noted. First, this study was conducted in males. Therefore, the association result for sex steroid hormones and gonadotropins should be interpreted with caution and further studies in females are warranted. Second, the identified variants for each trait only account for a small proportion of the total variance. More genetic determinants need to be discovered. Third, no genetic variant associated with LH was identified, which might be due to the lack of power. Future studies with larger sample size are needed to understand the genetic architecture of LH.

In conclusion, we discovered a novel locus, CYP19A1 at 15q21.2, which was significantly associated with FSH-circulating level. In addition, we are among the first to show that CYP19A1 was associated with oestradiol concentration at a genome-wide significant level. In addition, we confirmed the association between SHBG at 17p13.1 and testosterone and SHBG levels in Chinese men, which suggests a common genetic component shared among Caucasians and Chinese. Taken together, these findings provide new insights into the genetic architecture of sex hormones, gonadotropins and SHBG.


We thank the local research teams from Fangchenggang First People's Hospital, Guigang People's Hospital and Yulin First People's Hospital for their contribution to the recruitment of study subjects. We thank X-W Zou, H-C Zheng and O Li at the Genergy Biotechnology (Shanghai) Co, Ltd, for their assistance in the genotyping. Finally, we thank all study subjects for their participation of the study.


Supplementary materials

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  • ZC, ST, YG, JZ, YH and LM contributed equally to this study.

  • Contributors ZM and JX conceived and designed the experiments. YH, YG, AT, LM and SLZ performed the experiments.ST, ZC, JS and S-TK analysed the data. XQ, LL, SLZ and ZM contributed reagents/materials/analysis tools. ZC, ST, YG and JS wrote the paper. YG, XY, HZ, YW, AT and SZ prepared and collected samples .ZC, ST, YG, JS, JX and ZM reviewed and revised the manuscript.

  • Funding This study was partially supported by grants from the National Key Basic Research Program grant 973 (2012CB518300, 2012CB518303), National Natural Science Foundation of China (81060213, 81272853, 81360378, 81360099, 81370857), Key Program and University Talents Highland Innovation Team of Guangxi (2012012D003, GJR201147-09), Chairman Science and Technology Fund and Tackle Program of Guangxi (1116-03, GKG1298003-07-01), Guangxi Natural Science Foundation (2011GXNSFB018100, 2012GXNSFAA053152, 2013GXNSFFA019002), Guangxi Provincial Department of Finance and Education (2009GJCJ150), Youth Science Foundation of Guangxi Medical University (GXMUYSF201201), intramural funding from Fudan-VARI Center for genetic Epidemiology and intramural funding from Fudan University Institute of Urology.

  • Competing interests None.

  • Patient consent Obtained.

  • Ethics approval Ethics Committee.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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