If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USAFeinberg School of Medicine, Northwestern University, Chicago, IL, USA
Department of Urology and the James Buchanan Brady Urologic Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USASidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, USA
Despite strong evidence of heritability, few studies have attempted to unveil the genetic underpinnings of testosterone levels.
Objective
To identify testosterone-associated loci in a large study and assess their biological and clinical implications.
Design, setting, and participants
The participants were men from the UK Biobank. A two-stage genome-wide association study (GWAS) was first used to identify/validate loci for low testosterone (LowT, <8 nmol/l) in 80% of men (N = 148 902). The cumulative effect of independent LowT risk loci was then evaluated in the remaining 20% of men.
Outcome measurements and statistical analysis
Associations of single nucleotide polymorphisms (SNPs) with LowT were tested using an additive model. Analyses of the expression quantitative trait loci (eQTLs) were performed to assess the associations between significant SNPs and expression of nearby genes (within 1 Mbp). A genetic risk score (GRS) was used to assess the cumulative effect of multiple independent SNPs on LowT risk.
Results and limitations
The two-stage GWAS found SNPs in 141 loci of 41 cytobands that were significantly associated with LowT (p < 5 × 10–8), including 94 novel loci from 38 cytobands. An eQTL analysis of these 141 loci revealed significant associations with RNA expression of 155 genes, including previously implicated (SHBG and JMJD1C) and novel (LIN28B, LCMT2, and ZBTB4) genes. Among the 141 loci, 42 were independently associated with LowT after a multivariable analysis. The GRS based on these 42 loci was significantly associated with LowT risk in independent individuals (N = 37 225, ptrend = 3.16 × 10–162). The risk ratio for LowT between men in the top and those in the bottom GRS deciles was 4.98-fold. Results are limited in generalizability as only Caucasians were studied.
Conclusions
Identification of the genetic variants associated with LowT may improve our understanding of its etiology and identify high-risk men for LowT screening.
Patient summary
We identified 141 new genetic loci that can be incorporated into a genetic risk score that can potentially identify men with low testosterone.
]. The cause of LowT is often multifactorial as it is intimately related to different comorbidities such as obesity, diabetes, and the metabolic syndrome [
]. Manifestations of LowT are variable, and most guidelines do not suggest replacement unless men have symptoms consistent with testosterone deficiency (TD) [
in: European Association of Urology Guidelines 2019. Presented at the EAU Annual Congress Barcelona 2019. European Association of Urology Guidelines Office,
Arnhem, The Netherlands2019
]. While TD is less prevalent than LowT, affecting 6% of men between the ages of 40 and 79 years, this is likely in part due to a lack of standardized screening and heterogeneity in the diagnostic criterion [
While LowT and TD can be associated with other comorbidities, numerous genetic factors have been implicated in causing low serum testosterone levels with or without symptoms [
]. Previously conducted twin studies suggest that androgen expression has a strong hereditary component, with genetic variance estimates as high as 57% [
], it is hypothesized that common genetic variants account for the vast majority of the genetic susceptibility in the population.
Previous genome-wide association studies (GWASs) have focused on the association between common single nucleotide polymorphisms (SNPs) and overall testosterone levels [
Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis: evidence from the International Consortium for Prostate Cancer Genetics (ICPCG).
]. These studies identified SNPs in three cytobands (10q21.3, 17p13.1, and Xp22.31) associated with serum testosterone as a continuous variable among Caucasians. The limited number of identified testosterone-associated cytobands, compared with high heritability, is likely due to a small sample size (<20 000 men). Furthermore, while analyzing testosterone as a continuous variable is informative, studying testosterone as a dichotomous phenotype based on clinical diagnosis is more relevant to clinical care.
In this study, we performed a two-stage GWAS to identify and validate SNPs associated with LowT, a categorical testosterone phenotype defined as <8 nmol/l per European Association of Urology guidelines, in a large population-based cohort (UK Biobank [UKB]). To provide further evidence for the implicated SNPs and understand their biology, we tested their associations with the RNA expression of nearby genes in several androgen-related tissues. Finally, we assessed the performance of a genetic risk score (GRS) based on multiple independent LowT-associated SNPs to stratify men’s risk of LowT in independent study participants.
2. Patients and methods
2.1 Study population
The participants were white men with measured serum testosterone levels and SNP data across the genome (genotyped or imputed) in the UKB. Detailed information of the UKB population has been described elsewhere [
]. We defined the LowT phenotype as having a serum testosterone level of <8 nmol/l (all measured using the Coulter Unicel Dxl 800), the lowest threshold value proposed by the European Association of Urology [
in: European Association of Urology Guidelines 2019. Presented at the EAU Annual Congress Barcelona 2019. European Association of Urology Guidelines Office,
Arnhem, The Netherlands2019
]. Given the complex interaction between some comorbidities and testosterone, men with the confounding medical conditions were excluded from the present study (Supplementary Table 1). Owing to the large number of men with overweight/obesity, metabolic syndrome, and diabetes, these conditions were not excluded from the current study.
Eligible study participants were randomly divided into three subsets with 60% (N = 111 676), 20% (N = 37 225), and 20% (N = 37 160) of the total sample size for further analysis. The first two subsets were used for two-stage GWASs, while the remaining subset was used to test the performance of a GRS for predicting LowT.
The UKB was approved by the North West – Haydock Research Ethics Committee (REC reference: 16/NW/0274; IRAS project ID: 200778). Data from the UKB were accessed through a material transfer agreement under application reference number 50295.
2.2 Statistical methods
A two-stage GWAS was used to identify and validate SNPs associated with LowT. A standard quality control analysis was applied to remove SNPs with poor call rates (<95%) and minor allele frequency (<1%), and SNPs that deviated from the Hardy-Weinberg equilibrium (p > 1 × 10–6). For stage 1 (60% of the participants), a logistic regression model was used to test the association of each SNP across the genome (additive model) with LowT, as implemented in the PLINK software package [
]. Several covariates were adjusted in the association tests, including age at recruitment and body mass index (BMI) as well as genetic background (the top two principal components [PCs] provided by the UKB). Based on these test statistics, a linkage disequilibrium (LD) score regression analysis was used to assess the heritability (h2) explained by SNPs in the genome and inflation (confounding bias) due to population stratification [
]. SNPs with p < 1 × 10–5 from the association tests were selected for validation in stage 2 (20% of the participants). The same logistic regression model and covariates were used for association tests. SNPs with p < 0.05 and the same direction of association as stage 1 were considered validated. For validated SNPs, a combined association test in both stages was performed.
For significant GWAS SNPs (p < 5 × 10–8 in the combined analysis), a CLUMP analysis (distance = 250 kb, r2 = 0.2) was used to identify independent LowT-associated loci accounting for the LD between SNPs. For each independent locus (clump), an index SNP with the strongest p value was identified.
A fine mapping analysis was performed for significant loci in each cytoband using the LocusZoom plot based on the LD structure of the CEU (Utah Residents with Northern and Western European Ancestry) population of the 1000 Genomes Project. LocusZoom plots the association results of index SNPs and other SNPs in the locus (<±1Mb), their LD information, as well as the location and orientation of genes in the region.
We also performed an analysis of the expression quantitative trait loci (eQTLs) for all significant SNPs of each locus to obtain further statistical evidence of their association with LowT and to provide additional insight into their possible mechanism of action. Associations of these SNPs with RNA expression levels of nearby genes in several androgen-related tissues from the Genotype-Tissue Expression (GTEx) project portal, including testis (N = 322), adrenal gland (N = 233), pituitary (N = 237), liver (N = 208), subcutaneous adipose (N = 581), visceral adipose (N = 469), and prostate (N = 221), were tested (data download from: http://www.gtexportal.org).
Finally, a GRS was used to measure the cumulative effect of multiple LowT-associated SNPs on LowT risk in independent individuals (third stage, the remaining 20% of the participants). Only independent SNPs derived from stepwise regression analysis from GWAS stages 1 and 2 were used to calculate the GRS. A GRS is an odds ratio (OR)-weighted and population-standardized polygenic risk score and is calculated as follows:
Wi = fi2ORi2 + 2fi(1 – fi)ORi + (1 – fi)2
where gi stands for the genotype of SNP i in an individual (zero, one, or two risk alleles), ORi stands for the allelic OR of SNP i, and fi stands for the risk allele frequency of SNP i in the population [
]. The OR estimates of each SNP from stages 1 and 2 and allele frequency from gnomAD were used. As a GRS is population standardized, its value can be considered as an individual’s relative risk compared with the risk of the general population. The performance of a GRS for stratifying LowT risk was assessed by estimating the LowT risk in patients in each GRS decile (compared with the entire cohort) and testing for a trend, adjusting for age at recruitment, BMI, diabetes, time of laboratory draw, and the top 10 PCs provided by the UKB (Supplementary Table 2).
3. Results
Among the total 186 062 eligible participants of this study, 22 194 (11.9%) were classified to have LowT (Table 1). Patients with LowT were significantly older (57.7 yr) than non-LowT individuals (56.5 yr, p < 0.0001). LowT patients also had a higher BMI (30.40) than non-LowT individuals (27.52, p < 0.0001). Similar patterns of age and BMI as well testosterone levels were found for LowT and non-LowT patients in all three stages.
Table 1Baseline characteristic of the two stages of genome-wide association study for low testosterone
Stage 1
Stage 2
Stage 3
Low T (n = 13 409)
Non–low T (n = 98 267)
Low T (n = 4426)
Non–low T (n = 32 800)
Low T (n = 4359)
Non–low T (n = 32 801)
Age (yr), median (IQR)
59 (52, 64)
58 (50, 63)
59 (52, 64)
58 (50, 63)
59 (52, 64)
58 (50, 63)
BMI (kg/m2), mean (±SD)
30.43 (±5.17)
27.52 (±3.99)
30.32 (±4.99)
27.51 (±3.98)
30.19 (±4.99)
27.49 (±3.96)
T level (nmol/l), median (IQR)
6.99(6.15, 7.55)
12.16 (10.25, 14.54)
7.00 (6.16, 7.57)
12.18 (10.26, 14.57)
6.98(6.15, 7.57)
12.17 (10.30, 14.52)
T level categories (nmol/l), n (%)
<8.0
13 409 (12.01)
–
4426 (11.89)
–
4359 (11.73)
–
8–12
–
47 090 (42.17)
–
15 690 (42.15)
–
15 673 (42.18)
≥12
–
51 177 (45.83)
–
17 110 (45.96)
–
17 126 (46.09)
BMI = body mass index; IQR = interquartile range; SD = standard deviation; T = testosterone.
After quality control, a total of 8 853 336 SNPs remained for further analyses. In stage 1, an association test for each of these SNPs with LowT was performed, adjusting for age at enrollment, BMI, and genetic background. A quantile-quantile plot of all SNPs revealed a modest inflation factor (λ) of 1.10 (Supplementary Fig. 1). Based on an LD score regression analysis, the deviation from expected 1.00 was primarily driven by polygenic effect (h2 = 0.20), rather than by population stratification (0.02) [
A total of 13 165 SNPs in the genome reached p < 1 × 10–5 in stage 1 (Fig. 1). Associations of these SNPs with LowT were further tested in stage 2 using the same model, 6493 of which were validated (p < 0.05 with the same direction of association as in stage 1). For these validated SNPs, combined association tests with LowT were performed in stages 1 and 2. A total of 5968 SNPs reached genome-wide significance, with p < 5 × 10–8.
Fig. 1Manhattan plot of the first stage genome-wide association study for low testosterone levels in Caucasian men in the UK Biobank cohort.
Considering that many of these significant SNPs are in LD, we performed a CLUMP analysis to identify independent regions that are associated with LowT. SNPs within any clump are in strong LD (r2 > 0.2 and distance <250 kb), while SNPs between clumps are not in LD. A total of 141 LowT-associated clumps (loci) in 41 chromosomal cytobands were identified, including 47 loci in two previously implicated cytobands for testosterone in Caucasians (10q21.3, 17p13.1) and 94 loci in 38 novel cytobands. These 141 loci, their cytobands, as well as their index SNPs (the strongest p value in the clump) are listed in Table 2.
Table 2Significant index single nucleotide polymorphisms associated with low testosterone in each clump
Chromosome
SNP (index SNPs in each LD region)
Base pair
Cytoband
Risk allele
Stage 1
Stage 2
Combine
Independent?
Frequency in low T
Frequency in non–low T
OR
p value
Frequency in low T
Frequency in non–low T
OR
p value
OR
p value
1
rs114165349
27021913
1p36.11
C
0.03
0.02
1.32
4.33E-11
0.03
0.02
1.47
4.87E-08
1.35
3.41E-17
N
1
1:27335529_GGAATGCAGT_G
27335529
1p36.11
G
0.03
0.02
1.33
1.12E-11
0.03
0.02
1.56
4.15E-10
1.38
2.43E-19
Y
1
rs28385651
27696330
1p36.11
C
0.04
0.04
1.17
5.63E-06
0.04
0.04
1.24
2.54E-04
1.18
8.40E-09
N
2
rs1260326
27730940
2p23.3
T
0.42
0.39
1.14
7.71E-22
0.41
0.40
1.11
1.47E-05
1.13
9.93E-26
Y
2
rs3817588
27731212
2p23.3
T
0.82
0.81
1.11
7.57E-09
0.82
0.81
1.08
1.66E-02
1.10
5.21E-10
N
2
rs13013484
27988821
2p23.2
A
0.74
0.73
1.08
2.45E-07
0.74
0.73
1.08
2.86E-03
1.08
2.43E-09
N
2
rs13030345
28003174
2p23.2
T
0.18
0.17
1.11
1.32E-09
0.18
0.17
1.07
2.29E-02
1.10
1.48E-10
N
2
rs77775907
31609942
2p23.1
G
0.97
0.96
1.39
3.96E-15
0.97
0.97
1.29
2.94E-04
1.36
6.90E-18
Y
2
rs113017476
31989359
2p23.1
G
0.97
0.96
1.38
2.09E-16
0.97
0.96
1.38
2.42E-06
1.38
2.71E-21
N
2
rs148325193
32178523
2p22.3
AT
0.46
0.44
1.07
3.33E-07
0.45
0.44
1.08
1.03E-03
1.07
1.33E-09
N
2
rs72796891
32447408
2p22.3
A
0.96
0.95
1.31
2.63E-15
0.96
0.95
1.30
9.83E-06
1.31
1.34E-19
N
2
rs111471249
32834193
2p22.3
C
0.97
0.96
1.30
1.18E-11
0.97
0.96
1.29
1.47E-04
1.29
7.74E-15
N
3
rs7626388
138089038
3q22.3
G
0.36
0.34
1.09
8.55E-10
0.36
0.34
1.05
4.20E-02
1.08
2.10E-10
Y
4
rs9884390
69373407
4q13.2
T
0.78
0.77
1.08
7.86E-07
0.78
0.76
1.12
3.84E-05
1.09
2.22E-10
Y
4
rs6811902
88213884
4q22.1
C
0.45
0.43
1.07
1.18E-06
0.45
0.43
1.07
2.60E-03
1.07
1.24E-08
Y
4
rs114087689
104064037
4q24
T
0.02
0.01
1.28
1.19E-06
0.02
0.01
1.35
7.14E-04
1.30
3.59E-09
N
4
rs17289915
104491078
4q24
G
0.02
0.01
1.52
3.02E-15
0.02
0.01
1.44
5.73E-05
1.50
8.98E-19
Y
4
rs115260227
104774698
4q24
G
0.01
0.01
1.55
2.27E-14
0.02
0.01
1.63
6.54E-07
1.57
8.42E-20
N
6
rs11156429
105364421
6q16.3
T
0.47
0.45
1.08
1.84E-09
0.47
0.45
1.08
1.30E-03
1.08
1.05E-11
Y
7
rs10278686
15031450
7p21.2
C
0.53
0.51
1.11
1.28E-13
0.52
0.50
1.08
1.38E-03
1.10
1.08E-15
Y
7
rs34785619
97946299
7q21.3
C
0.20
0.18
1.12
4.60E-11
0.20
0.18
1.09
3.28E-03
1.11
7.33E-13
Y
10
rs11461906
64768139
10q21.3
T
0.91
0.89
1.13
2.13E-07
0.90
0.90
1.08
4.41E-02
1.11
4.15E-08
N
10
rs10822120
64829314
10q21.3
T
0.63
0.60
1.13
8.63E-18
0.62
0.60
1.08
7.29E-04
1.12
7.73E-20
N
10
rs7896280
64868355
10q21.3
C
0.76
0.74
1.08
3.17E-06
0.76
0.74
1.10
2.42E-04
1.08
4.46E-09
N
10
rs7084569
64876554
10q21.3
G
0.57
0.52
1.20
1.28E-39
0.56
0.52
1.18
3.40E-12
1.19
3.84E-50
Y
10
rs72829138
64907575
10q21.3
C
0.19
0.17
1.12
3.64E-10
0.18
0.17
1.09
6.21E-03
1.11
1.06E-11
N
10
rs117452816
65043795
10q21.3
T
0.93
0.93
1.17
1.14E-08
0.93
0.92
1.14
5.63E-03
1.16
2.33E-10
N
10
10:65082562_CAAA_C
65082562
10q21.3
CAAA
0.21
0.19
1.14
4.72E-15
0.21
0.19
1.18
6.83E-09
1.15
4.47E-22
N
10
rs6479896
65126832
10q21.3
T
0.57
0.52
1.24
8.30E-55
0.57
0.52
1.21
2.65E-16
1.23
3.06E-69
N
10
rs76865584
65205928
10q21.3
G
0.87
0.85
1.13
1.07E-09
0.87
0.85
1.15
7.12E-05
1.13
3.68E-13
N
10
rs72837062
65271048
10q21.3
A
0.19
0.18
1.11
9.56E-10
0.19
0.18
1.09
2.52E-03
1.11
9.86E-12
N
10
rs113772416
65309157
10q21.3
A
0.94
0.93
1.16
4.20E-08
0.93
0.93
1.12
1.22E-02
1.15
1.84E-09
N
10
rs61855876
65357541
10q21.3
T
0.18
0.16
1.13
2.76E-12
0.18
0.16
1.21
5.03E-10
1.15
4.76E-20
N
10
rs3858121
65399997
10q21.3
C
0.50
0.47
1.17
1.91E-30
0.51
0.47
1.16
9.37E-10
1.17
1.17E-38
N
10
rs35311029
65445784
10q21.3
T
0.42
0.40
1.09
5.95E-10
0.42
0.40
1.09
5.25E-04
1.09
1.30E-12
N
10
rs7097842
67245171
10q21.3
G
0.62
0.59
1.10
3.42E-12
0.61
0.59
1.10
4.47E-05
1.10
7.16E-16
Y
11
rs6484426
29147101
11p14.1
C
0.14
0.13
1.15
7.22E-13
0.14
0.13
1.13
3.37E-04
1.14
1.22E-15
Y
11
rs11218882
122771664
11q24.1
T
0.38
0.36
1.08
5.38E-08
0.38
0.37
1.06
1.21E-02
1.08
2.58E-09
Y
12
rs56196860
2908330
12p13.33
C
0.98
0.97
2.04
1.06E-47
0.98
0.97
1.75
2.52E-12
1.96
2.44E-58
Y
12
rs150948148
3077486
12p13.33
A
0.96
0.95
1.19
2.88E-07
0.96
0.95
1.25
1.84E-04
1.21
2.26E-10
N
12
rs4149056
21331549
12p12.1
C
0.16
0.15
1.12
1.13E-10
0.17
0.15
1.17
5.97E-07
1.14
6.59E-16
Y
12
rs11045856
21350689
12p12.1
T
0.77
0.76
1.08
3.35E-06
0.78
0.76
1.15
9.72E-07
1.09
1.01E-10
N
12
rs28849840
50703384
12q13.12
A
0.36
0.35
1.08
4.89E-08
0.36
0.35
1.06
1.22E-02
1.08
2.17E-09
Y
12
rs2250752
51106091
12q13.12
C
0.36
0.34
1.08
2.86E-08
0.36
0.34
1.07
7.06E-03
1.08
7.21E-10
N
12
rs7484541
57714803
12q13.3
A
0.78
0.77
1.08
3.16E-06
0.79
0.77
1.11
1.42E-04
1.09
2.92E-09
Y
14
rs28929474
94844947
14q32.13
C
0.99
0.98
1.52
8.83E-14
0.98
0.98
1.32
2.75E-03
1.47
1.76E-15
Y
15
rs143875230
43278726
15q15.2
A
0.03
0.02
1.25
6.46E-08
0.03
0.02
1.24
3.69E-03
1.25
8.15E-10
Y
15
rs754849914
43611767
15q15.3
C
0.14
0.13
1.10
9.91E-07
0.14
0.13
1.10
4.15E-03
1.10
1.23E-08
N
15
rs150844304
43726625
15q15.3
C
0.03
0.02
1.31
3.21E-12
0.03
0.02
1.33
3.11E-05
1.32
3.40E-16
N
15
rs8030169
44013177
15q15.3
C
0.13
0.12
1.10
2.97E-06
0.13
0.12
1.13
3.43E-04
1.11
4.89E-09
N
15
rs139974673
44027885
15q15.3
C
0.03
0.02
1.34
2.77E-14
0.03
0.02
1.35
1.49E-05
1.35
1.41E-18
N
15
rs138893177
44297617
15q15.3
T
0.03
0.02
1.35
1.89E-14
0.03
0.02
1.34
2.23E-05
1.35
1.48E-18
N
15
rs148489550
44581461
15q15.3
A
0.03
0.02
1.28
5.49E-10
0.03
0.02
1.31
9.73E-05
1.29
2.38E-13
N
15
rs4273010
44947434
15q21.1
C
0.03
0.02
1.29
4.91E-10
0.03
0.02
1.32
7.43E-05
1.30
1.69E-13
N
15
rs77255942
53016517
15q21.3
T
0.04
0.03
1.19
2.58E-06
0.04
0.03
1.20
3.09E-03
1.19
2.79E-08
Y
15
rs79391862
53739426
15q21.3
C
0.02
0.01
1.36
2.21E-08
0.02
0.01
1.47
1.73E-05
1.38
2.72E-12
Y
15
rs5813220
63792758
15q22.31
GT
0.36
0.34
1.09
1.47E-09
0.35
0.34
1.07
3.81E-03
1.09
2.21E-11
Y
15
rs8023580
96708291
15q26.2
T
0.74
0.72
1.13
5.46E-16
0.74
0.72
1.11
4.25E-05
1.13
1.37E-19
Y
16
rs2764772
20060653
16p12.3
T
0.68
0.67
1.08
1.21E-07
0.69
0.67
1.12
6.44E-06
1.09
8.01E-12
Y
17
rs6503017
7273147
17p13.1
C
0.31
0.29
1.14
3.19E-18
0.30
0.29
1.07
5.41E-03
1.12
4.22E-19
N
17
rs7208523
7288228
17p13.1
T
0.13
0.11
1.17
1.68E-14
0.12
0.11
1.08
3.56E-02
1.15
1.19E-14
N
17
rs35386490
7310006
17p13.1
T
0.79
0.76
1.22
6.67E-33
0.78
0.76
1.17
1.33E-08
1.20
1.08E-39
N
17
rs77554485
7310754
17p13.1
G
0.04
0.03
1.21
2.77E-08
0.04
0.03
1.26
6.98E-05
1.23
1.04E-11
N
17
rs74702014
7314543
17p13.1
G
0.96
0.96
1.20
2.73E-07
0.96
0.96
1.16
1.86E-02
1.19
1.91E-08
N
17
rs76749877
7322087
17p13.1
A
0.09
0.08
1.12
1.71E-06
0.09
0.08
1.21
3.81E-06
1.14
1.14E-10
N
17
rs12946520
7336371
17p13.1
G
0.41
0.35
1.32
6.75E-89
0.40
0.35
1.25
1.04E-19
1.30
6.80E-106
Y
17
rs35490807
7368513
17p13.1
C
0.14
0.12
1.16
5.06E-13
0.13
0.12
1.14
3.20E-04
1.15
6.71E-16
N
17
rs763671529
7423230
17p13.1
C
0.42
0.39
1.15
1.20E-24
0.41
0.39
1.14
3.67E-08
1.15
2.66E-31
N
17
rs187079266
7438801
17p13.1
A
0.02
0.01
1.33
2.23E-07
0.02
0.01
1.33
1.86E-03
1.33
1.41E-09
N
17
rs11078694
7448003
17p13.1
T
0.28
0.22
1.43
1.44E-119
0.27
0.21
1.40
2.79E-36
1.42
6.87E-154
Y
17
rs4246413
7461469
17p13.1
T
0.06
0.05
1.28
4.43E-18
0.06
0.05
1.23
3.28E-05
1.27
8.84E-22
N
17
rs183855978
7465735
17p13.1
C
0.03
0.02
1.34
1.29E-11
0.03
0.02
1.56
3.32E-10
1.39
2.30E-19
Y
17
rs10468481
7474992
17p13.1
A
0.38
0.36
1.07
5.56E-07
0.38
0.36
1.09
6.85E-04
1.08
1.67E-09
Y
17
rs9901675
7484812
17p13.1
A
0.06
0.05
1.21
4.15E-11
0.06
0.05
1.27
1.26E-06
1.22
4.54E-16
N
17
rs12944954
7485131
17p13.1
G
0.04
0.02
1.98
4.08E-77
0.04
0.02
2.16
3.46E-34
2.02
4.54E-109
Y
17
17:7493904_AAGCCC_A
7493904
17p13.1
A
0.02
0.02
1.58
4.63E-24
0.02
0.02
1.49
4.63E-07
1.55
1.34E-29
Y
17
rs72829408
7523491
17p13.1
C
0.12
0.10
1.30
5.34E-37
0.13
0.10
1.35
6.85E-17
1.31
5.33E-52
N
17
rs118098353
7531244
17p13.1
C
0.99
0.99
1.41
4.18E-07
0.99
0.99
1.48
8.29E-04
1.43
1.39E-09
N
17
rs1799941
7533423
17p13.1
G
0.80
0.73
1.45
7.52E-111
0.79
0.73
1.40
6.56E-32
1.44
1.34E-140
N
17
rs858517
7534271
17p13.1
C
0.06
0.05
1.39
1.71E-30
0.06
0.05
1.19
7.56E-04
1.33
3.48E-31
N
17
rs6259
7536527
17p13.1
G
0.89
0.87
1.25
1.16E-24
0.89
0.87
1.26
3.40E-10
1.25
2.68E-33
N
17
rs78496430
7565681
17p13.1
A
0.96
0.95
1.19
2.42E-07
0.96
0.95
1.21
9.90E-04
1.19
9.19E-10
N
17
rs1641549
7574775
17p13.1
T
0.30
0.25
1.30
2.75E-69
0.29
0.25
1.30
4.14E-23
1.30
1.70E-90
N
17
rs1642792
7576151
17p13.1
A
0.01
0.01
1.35
4.63E-07
0.01
0.01
1.35
4.02E-03
1.35
6.58E-09
N
17
rs34289079
7593319
17p13.1
C
0.10
0.08
1.36
6.18E-41
0.09
0.08
1.30
1.17E-10
1.34
1.17E-49
N
17
rs181975550
7595379
17p13.1
C
0.98
0.97
1.34
1.08E-10
0.98
0.97
1.48
5.91E-07
1.37
5.64E-16
N
17
rs11870307
7617787
17p13.1
A
0.25
0.21
1.23
7.58E-38
0.24
0.21
1.20
7.98E-11
1.22
6.30E-47
N
17
rs4968188
7629746
17p13.1
C
0.66
0.63
1.16
2.38E-25
0.66
0.62
1.20
2.30E-13
1.17
8.51E-37
N
17
rs117387630
7651906
17p13.1
T
0.03
0.02
1.64
1.36E-30
0.03
0.02
1.65
4.57E-11
1.64
4.19E-40
Y
17
rs117646332
7656668
17p13.1
G
0.95
0.94
1.19
6.39E-09
0.95
0.94
1.31
3.05E-07
1.22
3.16E-14
N
17
17:7686189_GA_G
7686189
17p13.1
GA
0.06
0.05
1.21
3.84E-11
0.05
0.05
1.16
3.49E-03
1.20
6.54E-13
N
17
rs2309810
7692510
17p13.1
C
0.42
0.41
1.09
2.06E-10
0.43
0.41
1.09
5.94E-04
1.09
4.62E-13
N
17
rs62059712
7740170
17p13.1
T
0.93
0.92
1.16
1.80E-08
0.93
0.92
1.11
2.12E-02
1.15
1.74E-09
N
17
rs62623385
7847837
17p13.1
T
0.04
0.03
1.28
4.07E-13
0.04
0.03
1.44
7.42E-10
1.32
9.38E-21
N
17
rs56214516
43836953
17q21.31
A
0.82
0.80
1.09
3.49E-07
0.82
0.81
1.09
4.02E-03
1.09
4.85E-09
N
17
rs62062271
44091988
17q21.31
T
0.79
0.77
1.09
3.53E-08
0.78
0.77
1.06
3.00E-02
1.09
4.64E-09
Y
17
rs2696555
44348370
17q21.31
A
0.79
0.78
1.09
2.22E-07
0.79
0.78
1.07
2.01E-02
1.09
1.59E-08
N
17
rs12941123
47259991
17q21.32
C
0.66
0.65
1.08
4.54E-07
0.66
0.65
1.06
1.64E-02
1.07
2.39E-08
N
17
rs12950511
47320938
17q21.32
T
0.35
0.34
1.07
6.36E-07
0.36
0.33
1.12
8.10E-06
1.09
5.24E-11
N
17
rs11655704
47448172
17q21.33
T
0.71
0.68
1.12
1.34E-15
0.71
0.68
1.14
1.35E-07
1.13
1.38E-21
Y
17
17:47457882_GAA_G
47457882
17q21.33
GAA
0.92
0.91
1.16
1.04E-08
0.92
0.91
1.15
1.55E-03
1.15
6.10E-11
N
18
rs600619
23662377
18q11.2
G
0.30
0.29
1.08
2.85E-07
0.31
0.29
1.08
4.76E-03
1.08
5.10E-09
Y
19
rs55959020
17301935
19p13.11
G
0.97
0.97
1.21
2.46E-06
0.97
0.96
1.26
9.43E-04
1.22
1.20E-08
N
19
rs35318830
46380325
19q13.32
T
0.90
0.89
1.13
2.07E-08
0.89
0.88
1.12
2.68E-03
1.13
2.05E-10
Y
22
rs738409
44324727
22q13.31
C
0.80
0.78
1.09
5.42E-07
0.81
0.78
1.15
1.36E-06
1.10
1.59E-11
Y
X
rs5933682
8783803
Xp22.31
A
0.94
0.92
1.40
7.40E-17
0.95
0.93
1.49
5.43E-08
1.42
3.92E-23
N
X
rs55994082
8784787
Xp22.31
G
0.95
0.94
1.27
3.25E-08
0.95
0.94
1.36
3.75E-05
1.29
6.78E-12
N
X
rs112183418
8848700
Xp22.31
C
0.95
0.93
1.48
3.08E-19
0.96
0.93
1.60
1.49E-09
1.52
1.68E-27
N
X
rs140143913
8900595
Xp22.31
A
0.97
0.96
1.35
9.65E-09
0.97
0.96
1.57
2.07E-06
1.39
2.93E-13
N
X
rs5933694
8902627
Xp22.31
A
0.31
0.27
1.20
1.83E-18
0.31
0.27
1.23
1.78E-08
1.21
4.13E-25
N
X
rs5934505
8913826
Xp22.31
T
0.80
0.72
1.54
4.40E-76
0.81
0.72
1.66
1.64E-34
1.57
2.48E-108
Y
X
rs1316470
8920762
Xp22.31
G
0.88
0.86
1.22
1.14E-11
0.88
0.86
1.22
1.18E-04
1.22
5.17E-15
N
X
rs5933699
8924923
Xp22.31
C
0.55
0.52
1.14
2.00E-12
0.55
0.52
1.16
5.49E-06
1.15
6.14E-17
N
X
rs137908282
8928551
Xp22.31
C
0.95
0.93
1.38
1.00E-13
0.95
0.93
1.56
7.95E-09
1.42
1.63E-20
N
X
rs6651991
56483572
Xp11.21
G
0.81
0.79
1.16
2.45E-09
0.81
0.79
1.09
4.16E-02
1.14
4.62E-10
Y
X
rs4607760
56821840
Xp11.21
A
0.82
0.80
1.17
7.99E-11
0.82
0.80
1.10
3.31E-02
1.15
2.03E-11
N
X
rs56202849
57163183
Xp11.21
G
0.82
0.80
1.17
4.59E-10
0.82
0.80
1.10
2.19E-02
1.15
6.25E-11
N
X
rs141955903
61973907
Xq11.1
C
0.02
0.02
1.36
3.55E-06
0.02
0.02
1.46
6.67E-04
1.39
9.15E-09
N
X
rs149312565
63295055
Xq11.2
C
0.02
0.01
1.42
2.26E-06
0.02
0.01
1.47
2.82E-03
1.44
2.22E-08
N
X
rs187365633
63332756
Xq11.2
T
0.10
0.09
1.17
1.74E-06
0.10
0.09
1.17
5.58E-03
1.17
3.09E-08
N
X
X:63722761_TC_T
63722761
Xq11.2
T
0.02
0.01
1.42
4.51E-06
0.02
0.01
1.57
4.04E-04
1.46
8.87E-09
N
X
X:65604623_AC_A
65604623
Xq12
AC
0.21
0.19
1.14
3.55E-08
0.21
0.19
1.18
4.60E-05
1.15
1.13E-11
N
X
rs149920923
65719169
Xq12
T
0.02
0.01
1.54
4.08E-08
0.01
0.01
1.33
4.81E-02
1.49
8.72E-09
N
X
rs545399
65878187
Xq12
G
0.21
0.19
1.17
5.27E-11
0.21
0.19
1.22
1.38E-06
1.18
1.15E-15
N
X
rs141086308
65897736
Xq12
C
0.99
0.98
1.48
2.46E-06
0.99
0.98
2.06
8.74E-06
1.60
2.37E-10
Y
X
rs149173774
66122885
Xq12
A
0.02
0.01
1.66
1.86E-12
0.02
0.01
1.30
4.82E-02
1.56
1.81E-12
N
X
rs193285839
66165277
Xq12
A
0.99
0.98
1.65
8.56E-08
0.99
0.98
2.60
4.55E-07
1.83
5.24E-13
N
X
rs78907332
66180874
Xq12
T
0.16
0.14
1.19
3.54E-11
0.16
0.14
1.22
1.12E-05
1.20
4.53E-15
N
X
rs7472818
66473124
Xq12
T
0.02
0.01
1.69
6.39E-13
0.02
0.01
1.32
3.78E-02
1.59
5.23E-13
N
X
rs139106020
66489579
Xq12
A
0.09
0.08
1.25
5.77E-11
0.09
0.07
1.28
3.56E-05
1.25
1.56E-14
N
X
rs112482463
66580676
Xq12
A
0.86
0.84
1.19
7.16E-11
0.85
0.83
1.20
1.21E-04
1.19
7.15E-14
N
X
rs146415516
66607743
Xq12
C
0.99
0.98
1.69
7.86E-09
0.99
0.98
2.59
2.25E-07
1.87
2.27E-14
N
X
rs5919411
66918713
Xq12
A
0.09
0.08
1.25
2.55E-11
0.09
0.08
1.32
1.46E-06
1.26
4.26E-16
N
X
rs148526654
67025293
Xq12
A
0.99
0.98
1.80
6.74E-10
0.99
0.98
2.53
4.40E-07
1.95
2.11E-15
N
X
rs142188276
67059111
Xq12
C
0.87
0.86
1.14
7.33E-06
0.88
0.86
1.18
7.80E-04
1.15
2.24E-08
N
X
rs140290317
67135247
Xq12
A
0.02
0.01
1.64
1.40E-11
0.02
0.01
1.33
3.25E-02
1.56
6.43E-12
N
X
rs144254006
67279380
Xq12
C
0.99
0.99
1.66
7.94E-06
0.99
0.99
2.43
6.09E-05
1.82
2.78E-09
N
X
rs7052964
67403723
Xq12
G
0.21
0.18
1.22
3.01E-16
0.20
0.18
1.18
9.85E-05
1.21
1.76E-19
Y
X
rs140555778
67415777
Xq12
A
0.05
0.04
1.30
7.34E-09
0.05
0.04
1.21
1.71E-02
1.27
6.00E-10
N
X
rs5942977
109833905
Xq23
G
0.66
0.60
1.36
1.87E-52
0.64
0.60
1.26
1.92E-11
1.33
1.76E-61
Y
X
rs5943061
109987387
Xq23
A
0.77
0.75
1.14
1.73E-08
0.77
0.75
1.12
4.55E-03
1.13
3.35E-10
N
LD = linkage disequilibrium; N = no; OR = odds ratio; SNP = single nucleotide polymorphism; T = testosterone; Y = yes.
A fine mapping analysis was performed for these 141 loci in each cytoband using the LocusZoom plot. For each cytoband, associations of LowT with SNPs in the boundaries of clumps are plotted as well as LD information between SNPs (Supplementary Fig. 2). In addition, known genes and transcripts within the genomic region are mapped. The gene information of each locus is incorporated in Table 2.
We also performed eQTL analyses for all significant SNPs within the 141 loci to identify specific genes the RNA expression of which is significantly different between SNP genotypes in seven tissue types related to androgen metabolism (testis, adrenal gland, pituitary, liver, subcutaneous adipose, visceral adipose, and prostate). Based on the data from GTEx, significantly different RNA expression levels in any of the seven tissue types between SNP genotypes were found for 155 genes in 79 loci (Fig. 2). They include previously reported testosterone-associated genes, such as SHBG at 17p13.1 and JMJD1C at 10q21.3 (Supplementary Fig. 3A–C); novel genes potentially involved in androgen metabolism, such as LIN28B at 6q16.3, LCMT2 at 15q15.3, and ZBTB4 at 17p13.1 (Supplementary Fig. 3D–F); and other genes whose function in androgen metabolism are yet unknown.
Fig. 2Heatmap of the significant results of expression quantitative trait loci. Slopes of the correlations between the position of loci and gene expression are shown in different colors, ranging from blue (positively correlated) to red (negatively correlated).
Finally, considering that many loci with modest effect on LowT were identified in this study, we assessed their cumulative effect by stratifying LowT risk among independent study participants. A stepwise multivariable analysis of index SNPs from each of the 141 loci in participants of stages 1 and 2 identified 42 SNPs that were independently associated with LowT risk at p < 5 × 10–8 (Table 2). GRS values based on these 42 SNPs were significantly associated with LowT risk in the remaining 20% of eligible patients of the UKB (N = 37 225) in a multivariable analysis adjusting for age at recruitment, BMI, diabetes, time of laboratory draw, and the top 10 PCs provided by the UKB. Higher GRS deciles were significantly associated with a higher prevalence of LowT (OR [95% confidence interval] = 1.49 [1.44–1.54], ptrend = 2.78 × 10–124; Fig. 3). Compared with those at the bottom GRS decile, patients in the top decile had a 4.98-fold higher prevalence of having LowT (p = 1.8 × 10–99).
Fig. 3Odds ratios (OR) of low testosterone by genetic risk score (GRS) deciles and percentiles.
In this present study, we performed a two-stage GWAS for a clinical phenotype of LowT in a large population-based cohort. Results from our study suggest a considerable polygenic effect (many small-effect genes in the genome) for LowT, associated with an estimated heritability of 20% confirming previous twin studies [
]. Specifically, we identified 141 loci in 41 cytobands that are associated with LowT, including the two previously reported cytobands for testosterone levels and 38 novel cytobands. The confirmation of the previous cytobands in our study not only provides further support for the novel findings, but also demonstrates the validity of our study population, phenotype (dichotomous LowT), and analytical methods as well as the power of this large study. Owing to the strong associations of the two previous testosterone cytobands (1.20 at 10q21.3 and 1.98 at 17p13.1 in our study), it is not surprising that they were previously identified in smaller studies. However, the large cohort analyzed in the present study permitted the identification of loci with more modest effects, which significantly contribute to the heritability of LowT; the familial risk due to the loci in the 38 novel cytobands was 4.52%, compared with that in the three previously known cytobands (2.86%). It is also noted that the genetic association findings of our study are similar to those of a recently published seminal study by Ruth et al [
] utilizing the same UKB cohort. However, instead of reporting GWAS findings and delineated genetic loci for LowT, they focused on genetic determinants of testosterone levels between men and women, and their impact on metabolic diseases and cancers.
The eQTL results of our study have three important implications. First, they provide additional statistical evidence for identified loci. Compared with GWASs where the frequencies of SNP genotypes were compared between individuals with or without LowT, eQTLs test the association of SNP genotypes with the RNA expression of nearby genes. Second, in addition to providing statistical evidence, the results of the eQTL analyses implicated 155 genes in these loci. Although in-depth functional studies of these loci are beyond the scope of this study, they serve as important empirical data for biological and mechanistic studies by other groups. Finally, a preliminary examination of these eQTL results provides some insight into the biology of genes in these loci. This includes both previously implicated genes, such as SHBG and JMJD1C [
Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis: evidence from the International Consortium for Prostate Cancer Genetics (ICPCG).
One novel result of the eQTL analyses was that the LowT risk-associated SNPs identified at 6q16.3 were significantly associated with the expression of LIN28B. A recent study suggested that LIN28B expression is positively associated with the expression of hormone-related genes in the hypothalamus and pituitary, such as ESR1 and POMC. High expression of LIN28B could downregulate the serum testosterone level via the hypothalamus-pituitary-gonadal axis [
]. Our eQTL results indicate that risk alleles of SNPs in this region were significantly associated with increased expression of LIN28B and therefore may downregulate testosterone levels by central suppression of gonadotropins.
LowT-associated SNPs near the LCMT2 and ZBTB4 genes may also play an important role in the regulation of testosterone. LCMT2 at 15q15.3, known as leucine carboxyl methyltransferase 2, belongs to a methyltransferase superfamily that regulates hypothalamic gene expression and thereby may alter androgen synthesis; however, there are limited translational studies demonstrating LCMT2’s effects in vivo [
]. SNPs at 17q13.1 were significantly associated with the expression of ZBTB4, a transcriptional repressor for multiple genes, especially for methylated genes [
]. While this gene was found to regulate the expression of genes in different types of cancers, including androgen-related malignancies (eg, prostate cancer), no evidence thus far has linked ZBTB4 to androgen regulation [
In order to better understand the phenotype of LowT, rather than just the association between SNPS and serum testosterone, we performed a qualitative GWAS. Symptoms are often, but not always, associated with testosterone level; therefore, we used testosterone <8 nmol/l as a predictor of TD, which can negatively impact a patient’s quality of life and potentially require therapy [
]. Since routine androgen screening is not recommended, symptomatic hypogonadism is often underdiagnosed in part due to patients’ and physicians’ lack of attention to symptomatology [
]. On the contrary, routine androgen testing could potentially lead to an overdiagnosis of LowT in otherwise asymptomatic individuals.
As a GRS can effectively stratify men’s risk for LowT, it can guide clinicians to screen potential patients at risk for developing LowT. Screening based on a GRS would present a novel mechanism to reduce the number of men diagnosed with asymptomatic LowT, while simultaneously identifying men with TD who would have otherwise gone undiagnosed. Ultimately, a prospective study is necessary to determine the clinical utility of a GRS in diagnosing men with symptomatic LowT.
Our study should be viewed within the scope of its limitations, including the fact that the GWAS was performed in Caucasians only, which may limit its generalizability. As such, other studies in men of other ancestries should be performed. Second, the LowT phenotype was solely based on testosterone levels measured using an immunoassay, and future studies are needed to determine the potential clinical implications of the discovered genotypes and candidate genes. Third, while we used a guideline-directed threshold value to define LowT, this does not necessarily mean that the participants had TD as they may have been asymptomatic. Lastly, the several proposed biological mechanisms associated with LowT have not been well studied in vivo. While this is beyond the scope of the present study, future investigations into these pathways and mechanisms are warranted.
5. Conclusions
This two-stage GWAS from a large population-based cohort identified 141 loci in 41 cytobands that are associated with LowT. The large number of these novel loci may improve our understanding of the etiology of LowT. Furthermore, they can be used to identify high-risk men for LowT screening.
Author contributions: Jianfeng Xu and Brian T. Helfand have full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Xu, Helfand, Fantus, Na.
Acquisition of data: Na, Fantus, Xu, Helfand.
Analysis and interpretation of data: Na, Fantus, Shi, Wei, Xu, Helfand.
Drafting of the manuscript: Na, Fantus, Xu, Helfand.
Critical revision of the manuscript for important intellectual content: Fantus, Na, Wei, Shi, Resurreccion, Halpern, Franco, Hayward, Isaacs, Zheng, Xu, Helfand.
Statistical analysis: Shi, Wei, Na.
Obtaining funding: Xu, Helfand.
Administrative, technical, or material support: Resurreccion.
Supervision: Xu, Helfand.
Other: None.
Financial disclosures: Jianfeng Xu certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.
Funding/Support and role of the sponsor: We are grateful to the Ellrodt-Schweighauser, Chez, and Melman families for establishing Endowed Chairs of Cancer Genomic Research and Personalized Prostate Cancer Care at NorthShore University HealthSystem in support of Dr. Xu and Dr. Helfand, and the Rob Brooks Fund for Personalized Prostate Cancer Care at NorthShore University HealthSystem.
Appendix A. Supplementary data
The following are Supplementary data to this article:
in: European Association of Urology Guidelines 2019. Presented at the EAU Annual Congress Barcelona 2019. European Association of Urology Guidelines Office,
Arnhem, The Netherlands2019
Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis: evidence from the International Consortium for Prostate Cancer Genetics (ICPCG).
00084-7&id=gr1.jpg){kind=link}
00084-7&id=gr2.jpg){kind=link}
00084-7&id=gr3.jpg){kind=link}