Genome-wide association (GWA) studies of testicular germ cell tumor (TGCT) have identified 18 susceptibility loci, some containing genes encoding proteins important in male germ cell development. Deletions of one of these genes, DMRT1, lead to male-to-female sex reversal and are associated with development of gonadoblastoma. To further explore genetic association with TGCT, we undertook a pathway-based analysis of SNP marker associations in the Penn GWAs (349 TGCT cases and 919 controls). We analyzed a custom-built sex determination gene set consisting of 32 genes using three different methods of pathway-based analysis. The sex determination gene set ranked highly compared with canonical gene sets, and it was associated with TGCT (FDRG = 2.28 × 10−5, FDRM = 0.014 and FDRI = 0.008 for Gene Set Analysis-SNP (GSA-SNP), Meta-Analysis Gene Set Enrichment of Variant Associations (MAGENTA) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (i-GSEA4GWAS) analysis, respectively). The association remained after removal of DMRT1 from the gene set (FDRG = 0.0002, FDRM = 0.055 and FDRI = 0.009). Using data from the NCI GWA scan (582 TGCT cases and 1056 controls) and UK scan (986 TGCT cases and 4946 controls), we replicated these findings (NCI: FDRG = 0.006, FDRM = 0.014, FDRI = 0.033, and UK: FDRG = 1.04 × 10−6, FDRM = 0.016, FDRI = 0.025). After removal of DMRT1 from the gene set, the sex determination gene set remains associated with TGCT in the NCI (FDRG = 0.039, FDRM = 0.050 and FDRI = 0.055) and UK scans (FDRG = 3.00 × 10−5, FDRM = 0.056 and FDRI = 0.044). With the exception of DMRT1, genes in the sex determination gene set have not previously been identified as TGCT susceptibility loci in these GWA scans, demonstrating the complementary nature of a pathway-based approach for genome-wide analysis of TGCT.

INTRODUCTION

Testicular germ cell tumor (TGCT) is the most frequently occurring malignancy in men aged 25–34 years. The epidemiology of TGCT is characterized by an increasing incidence over the past few decades and considerable racial disparity, with the highest incidence among men of European ancestry (1). Several lines of evidence suggest a substantial genetic contribution to TGCT susceptibility, including higher risk for brothers (5- to 19-fold) or for sons (2- to 4-fold) of affected men (27).

TGCT can be divided into seminomatous and non-seminomatous tumors (810), which both originate from carcinoma in situ cells derived from primordial germ cells (PGCs) (1013). During normal embryogenesis, germ cells arise as pluripotent PGCs that ultimately differentiate into the cells and tissues of an adult gonad (14,15). Important processes in gonad and germ cell development are the migration, proliferation, survival, differentiation and maturation of the PGCs. Interestingly, several recently identified TGCT susceptibility loci contain genes encoding proteins important for germ cell migration, differentiation and maturation (1624).

It is only after their arrival in the gonad that PGCs follow a sex-specific fate. Cells in the fetal testis enter mitotic arrest, while those in the fetal ovary undergo sex-specific entry into meiosis (2528). This mitosis–meiosis switch is among other processes controlled by Dmrt1 in a sex-specific manner in mice (27,28), and DMRT1 may play a similar role in the regulation of meiotic entry in humans (29). DMRT1 is a member of a zinc finger-like DNA-binding motif (DM domain) gene family. This gene family is highly conserved and plays crucial roles in male germ cell development and sex determination across the phylogenetic spectrum (3033). Dmrt1 is required for testis differentiation in chicken and other birds (31,34,35). In mice, Dmrt1 is expressed only in the gonad (3639), and the deficiency of Dmrt1 is associated with TGCT development (40,41). In humans, deletion of the region on 9p containing DMRT1 leads to male-to-female sex reversal and is associated with the development of gonadoblastoma (4246).

Importantly, several recent studies have associated genetic variants near and within the DMRT1 locus with an increased risk of TGCT in both adults and children (2023). We hypothesized that other genes important for sex determination are associated with TGCT. Pathway-based approaches have been used to detect additional risk loci or combined effects of many loci in genome-wide association (GWA) studies (4750). Therefore, we undertook a pathway-based analysis of genes associated with sex determination to identify additional TGCT susceptibility loci.

RESULTS

In the discovery (Penn) dataset, the sex determination gene set ranked high in comparison with the canonical gene sets, and it was statistically significantly associated with TGCT in the Gene Set Analysis-SNP (GSA-SNP; FDRG = 2.28 × 10−5), Meta-Analysis Gene Set Enrichment of Variant Associations (MAGENTA; FDRM = 0.014) and Improved Gene Set Enrichment Analysis for Genome-wide Association Study (i-GSEA4GWAS; FDRI = 0.008) pathway-based analyses (Table 1; Supplementary Tables S1–S3). The sex determination gene set remained statistically significantly associated with TGCT even after the removal of DMRT1 from the gene set in both GSA-SNP (FDRG = 0.002) and i-GSEA4GWAS (FDRI = 0.009) pathway-based analyses and approached significance using MAGENTA (FDRM = 0.055). Within the sex determination gene set, the rank order of the representative markers, following DMRT1, suggested that FGF9, MAP3K1, WWOX and AKR1C4 may be novel susceptibility loci in the Penn dataset (Table 2).

Table 1.

Association of TGCT with set of genes implicated in sex determination

Dataset Set size GSA-SNP
 
iGSEA4GWAS
 
MAGENTA
 
Nominal P FDRa Rankb Nominal P FDR Rankb Nominal P FDR Rankb 
Penn 32 2.78×10−8 2.28E−05 <0.001 0.008 13 0.0141 0.014 14 
Penn 31c 1.25×10−5 0.002 18 <0.001 0.009 22 0.0554 0.055 59 
UK 32 2.98×10−9 1.04E−06 <0.001 0.025 12 0.0163 0.016 18 
UK 31c 1.59×10−7 3.00E−05 13 <0.001 0.044 16 0.0556 0.056 57 
NCI 32 1.63×10−4 0.006 65 <0.001 0.033 82 0.0143 0.014 23 
NCI 31c 0.003 0.039 165 0.002 0.055 117 0.0501 0.050 86 
Dataset Set size GSA-SNP
 
iGSEA4GWAS
 
MAGENTA
 
Nominal P FDRa Rankb Nominal P FDR Rankb Nominal P FDR Rankb 
Penn 32 2.78×10−8 2.28E−05 <0.001 0.008 13 0.0141 0.014 14 
Penn 31c 1.25×10−5 0.002 18 <0.001 0.009 22 0.0554 0.055 59 
UK 32 2.98×10−9 1.04E−06 <0.001 0.025 12 0.0163 0.016 18 
UK 31c 1.59×10−7 3.00E−05 13 <0.001 0.044 16 0.0556 0.056 57 
NCI 32 1.63×10−4 0.006 65 <0.001 0.033 82 0.0143 0.014 23 
NCI 31c 0.003 0.039 165 0.002 0.055 117 0.0501 0.050 86 

aFDR-corrected P-value.

bRank of the sex determination gene set compared with canonical gene sets.

cAfter removal of DMRT1 from the gene set.

Table 2.

Top SNP markers in and proximal to gene loci in the sex determination gene set

Gene Average rank Penn
 
UK
 
NCI
 
Marker P-value Ranking Marker P-value Ranking Marker P-value Ranking 
DMRT1 rs7040024 9.67×10−7 rs755383 1.71×10−18 rs17372837 9.71×10−8 
WWOX rs12929743 3.42×10−4 rs7196702 0.008 rs8064138 0.001 
GATA4 rs17153755 0.002 rs4840584 0.004 rs809204 0.003 
IGF1R rs1357112 0.033 16 rs2715442 0.002 rs2715442 0.009 
ZFPM2 rs10505080 0.002 rs13279707 6.33×10−6 rs1481024 0.053 18 
MAP3K4 rs9458135 0.008 rs9364586 0.065 19 rs7768457 0.010 
RSPO1 rs11264075 0.016 13 rs4653305 0.059 17 rs591567 0.013 
FGF9 rs628137 1.66×10−5 rs10492454 0.078 20 rs4770192 0.053 17 
EMX2 rs7088735 0.003 rs451123 0.004 rs2532665 0.101 26 
FOXL2 10 rs4894333 0.008 10 rs7641916 0.030 11 rs17485052 0.054 19 
MAP3K1 11 rs252906 1.89×10−5 rs16886420 0.055 16 rs252890 0.074 22 
FGFR2 12 rs1219643 0.011 12 rs1047057 0.182 26 rs1219643 0.009 
HSD17B3 13 rs280663 0.054 21 rs9409407 0.011 rs912465 0.036 14 
CYP11A1 14 rs2959011 0.095 24 rs4243086 0.015 rs885740 0.021 11 
AKR1C2 15 rs7076886 0.031 15 rs7909151 0.026 10 rs12268038 0.056 20 
CBX2 16 rs11657217 0.017 14 rs8066940 0.162 24 rs9890723 0.018 
NR5A1 17 rs2416933 0.009 11 rs2416933 0.126 23 rs7037254 0.033 13 
WT1 18 rs2067666 0.044 17 rs7395210 0.042 15 rs3901671 0.050 16 
LHX9 19 rs3894699 0.154 27 rs435062 0.094 21 rs1909507 0.006 
INSR 20 rs3745546 0.047 18 rs17253937 0.035 13 rs4804366 0.071 21 
FST 21 rs12520984 0.100 25 rs3943928 0.123 22 rs1469101 0.021 10 
SOX9 22 rs759499 0.049 20 rs2229989 0.065 18 rs918077 0.095 24 
AR 23 rs12011793 0.213 29 rs5919390 9.83×10−5 rs12014709 0.250 30 
SOX8 24 rs2573147 0.123 26 rs428233 0.032 12 rs606198 0.097 25 
SOX3 25 rs11095827 0.175 28 rs203652 0.035 14 rs2340412 0.079 23 
DHH 26 rs6580698 0.085 23 rs12580349 0.372 31 rs6580699 0.025 12 
AKR1C4 27 rs11253019 0.001 rs1931679 0.296 30 rs2050308 0.313 31 
CTNNB1 28 rs9821710 0.229 31 rs9838277 0.184 27 rs9849601 0.049 15 
NR0B1 29 rs2764761 0.067 22 rs5927496 0.170 25 rs4141069 0.135 27 
WNT4 30 rs2807371 0.048 19 rs2807377 0.190 29 rs2807357 0.167 28 
SOX10 31 rs8142174 0.224 30 rs5995529 0.186 28 rs3026659 0.227 29 
SRY 32 N/A N/A 32 N/A N/A 32 N/A N/A 32 
Gene Average rank Penn
 
UK
 
NCI
 
Marker P-value Ranking Marker P-value Ranking Marker P-value Ranking 
DMRT1 rs7040024 9.67×10−7 rs755383 1.71×10−18 rs17372837 9.71×10−8 
WWOX rs12929743 3.42×10−4 rs7196702 0.008 rs8064138 0.001 
GATA4 rs17153755 0.002 rs4840584 0.004 rs809204 0.003 
IGF1R rs1357112 0.033 16 rs2715442 0.002 rs2715442 0.009 
ZFPM2 rs10505080 0.002 rs13279707 6.33×10−6 rs1481024 0.053 18 
MAP3K4 rs9458135 0.008 rs9364586 0.065 19 rs7768457 0.010 
RSPO1 rs11264075 0.016 13 rs4653305 0.059 17 rs591567 0.013 
FGF9 rs628137 1.66×10−5 rs10492454 0.078 20 rs4770192 0.053 17 
EMX2 rs7088735 0.003 rs451123 0.004 rs2532665 0.101 26 
FOXL2 10 rs4894333 0.008 10 rs7641916 0.030 11 rs17485052 0.054 19 
MAP3K1 11 rs252906 1.89×10−5 rs16886420 0.055 16 rs252890 0.074 22 
FGFR2 12 rs1219643 0.011 12 rs1047057 0.182 26 rs1219643 0.009 
HSD17B3 13 rs280663 0.054 21 rs9409407 0.011 rs912465 0.036 14 
CYP11A1 14 rs2959011 0.095 24 rs4243086 0.015 rs885740 0.021 11 
AKR1C2 15 rs7076886 0.031 15 rs7909151 0.026 10 rs12268038 0.056 20 
CBX2 16 rs11657217 0.017 14 rs8066940 0.162 24 rs9890723 0.018 
NR5A1 17 rs2416933 0.009 11 rs2416933 0.126 23 rs7037254 0.033 13 
WT1 18 rs2067666 0.044 17 rs7395210 0.042 15 rs3901671 0.050 16 
LHX9 19 rs3894699 0.154 27 rs435062 0.094 21 rs1909507 0.006 
INSR 20 rs3745546 0.047 18 rs17253937 0.035 13 rs4804366 0.071 21 
FST 21 rs12520984 0.100 25 rs3943928 0.123 22 rs1469101 0.021 10 
SOX9 22 rs759499 0.049 20 rs2229989 0.065 18 rs918077 0.095 24 
AR 23 rs12011793 0.213 29 rs5919390 9.83×10−5 rs12014709 0.250 30 
SOX8 24 rs2573147 0.123 26 rs428233 0.032 12 rs606198 0.097 25 
SOX3 25 rs11095827 0.175 28 rs203652 0.035 14 rs2340412 0.079 23 
DHH 26 rs6580698 0.085 23 rs12580349 0.372 31 rs6580699 0.025 12 
AKR1C4 27 rs11253019 0.001 rs1931679 0.296 30 rs2050308 0.313 31 
CTNNB1 28 rs9821710 0.229 31 rs9838277 0.184 27 rs9849601 0.049 15 
NR0B1 29 rs2764761 0.067 22 rs5927496 0.170 25 rs4141069 0.135 27 
WNT4 30 rs2807371 0.048 19 rs2807377 0.190 29 rs2807357 0.167 28 
SOX10 31 rs8142174 0.224 30 rs5995529 0.186 28 rs3026659 0.227 29 
SRY 32 N/A N/A 32 N/A N/A 32 N/A N/A 32 

P-values of ≤0.05 are depicted in bold.

The sex determination gene set was statistically significantly associated with TGCT in the UK replication dataset (Table 1). In the UK scan, the sex determination gene set was also highly ranked in comparison with other gene sets using the GSA-SNP (FDRG = 1.04 × 10−6), MAGENTA (FDRM = 0.016) and i-GSEA4GWAS (FDRI = 0.025) pathway-based approaches (Table 1; Supplementary Material, Tables S4–S6). We found that the association of TGCT with the sex determination gene set after the removal of DMRT1 remained statistically significant using GSA-SNP (FDRG = 3.00 ×10−5) and i-GSEA4GWAS (FDRI = 0.044) and approached significance using MAGENTA (FDRM = 0.056) (Table 1; Supplementary Material, Tables S4–S6). In the UK dataset, the rank order of the representative markers within the sex determination gene set suggested that ZFPM2, AR, IGF1R and EMX2, following DMRT1, may be TGCT susceptibility loci (Table 2).

The sex determination gene set also was significantly associated with TGCT in the NCI dataset [GSA-SNP (FDRG = 0.006), MAGENTA (FDRM = 0.014) and i-GSEA4GWAS (FDRI = 0.033)] (Table 1; Supplementary Material, Tables S7–S9). After the removal of DMRT1 from the gene set, the statistical significance of its association with TGCT was FDRG = 0.039, FDRM = 0.050 and FDRI = 0.055 (Table 1; Supplementary Material, Tables S7–S9). In the NCI dataset, the most significant markers were in WWOX, GATA4, LHX9 and FGFR2, following DMRT1 (Table 2).

We determined a full ranking across studies of the most significant SNP markers within or proximate to genes in the sex determination set by taking the average of the study-specific ranks and then ranking the average (Table 2). Following DMRT1, we found that SNP markers within or proximate to GATA4, IGF1R, WWOX and ZFPM2 gene regions had the highest averaged ranking across the Penn, UK and NCI GWA studies. For combined SNP marker associations obtained from meta-analysis, we found that markers in or near to the DMRT1, GATA4, IGF1R, MAP3K1 and ZFPM2 regions ranked in the top 25 (of 1944) marker associations in terms of statistical significance (Table 3; Supplementary Material, Table S10and figure).

Table 3.

Top meta-analyzed SNP markers in gene regions in the sex determination gene set

Gene Rank Marker A1 A2 P OR (95% CI) I2(%)* PHet
DMRT1 rs755383 1.16×10−30 0.63 (0.58–0.68) 0.77 
DMRT1 rs4742419 1.53×10−15 1.34 (1.25–1.45) 0.04 0.37 
DMRT1 rs2370200 6.43×10−13 0.71 (0.65–0.78) 0.37 
DMRT1 rs7029148 3.34×10−12 0.77 (0.72–0.83) 8.2 0.34 
DMRT1 rs7035551 1.06×10−11 0.76 (0.71–0.82) 4.7 0.35 
DMRT1 rs4740943 4.43×10−8 0.81 (0.75–0.87) 0.50 
DMRT1 rs4742527 3.66×10−7 0.80 (0.73–0.87) 0.37 
DMRT1 rs7859987 2.03×10−6 0.80 (0.73–0.88) 0.97 
DMRT1 rs881684 2.74×10−6 1.19 (1.11–1.28) 0.91 
DMRT1 rs2025307 3.21×10−6 1.19 (1.11–1.28) 0.69 
DMRT1 rs16924776 6.39×10−6 1.26 (1.14–1.40) 0.63 
MAP3K1 rs2441132 1.46×10−5 0.82 (0.75–0.89) 22.9 0.27 
DMRT1 rs3812523 4.90×10−5 1.22 (1.11–1.35) 0.73 
ZFPM2 rs3779770 1.99×10−4 0.86 (0.79–0.93) 0.38 
DMRT1 rs10977244 2.44×10−4 1.15 (1.07–1.23) 30.4 0.24 
GATA4 rs17153747 3.22×10−4 1.22 (1.10–1.36) 0.96 
IGF1R rs2715442 3.43×10−4 1.15 (1.07–1.24) 41.3 0.18 
GATA4 rs4840584 3.66×10−4 1.26 (1.11–1.43) 0.91 
AKR1C2 rs11252845 5.24×10−4 1.29 (1.12–1.49) 0.55 
CTNNB1 rs4973937 7.42×10−4 1.20 (1.08–1.34) 36.9 0.21 
DMRT1 rs4742545 1.35×10−3 0.82 (0.73–0.93) 0.71 
DMRT1 rs10815567 1.58×10−3 0.89 (0.82–0.95) 0.40 
DMRT1 rs10977270 1.61×10−3 0.88 (0.81–0.95) 0.83 
GATA4 rs2645444 2.17×10−3 1.15 (1.05–1.26) 5.0 0.35 
NR5A1 rs2416933 2.49×10−3 1.12 (1.04–1.20) 0.52 
RSPO1 rs501252 3.38×10−3 1.13 (1.04–1.22) 0.62 
EMX2 10 rs242965 3.85×10−3 0.88 (0.81–0.96) 24.5 0.27 
ZFPM2 rs1375960 4.96×10−3 0.86 (0.77–0.96) 0.39 
WWOX 11 rs9319519 5.08×10−3 1.11 (1.03–1.19) 0.89 
ZFPM2 rs13252177 5.91×10−3 1.24 (1.07–1.45) 38.0 0.20 
CYP11A1 12 rs4887142 5.95×10−3 0.89 (0.81–0.97) 0.49 
MAP3K4 13 rs1247351 6.81×10−3 0.90 (0.83–0.97) 0.76 
IGF1R rs4966036 9.18×10−3 1.12 (1.03–1.23) 0.63 
SOX8 14 rs428233 9.37×10−3 1.12 (1.03–1.21) 0.94 
GATA4 rs6990313 9.38×10−3 1.18 (1.04–1.33) 40.6 0.19 
LHX9 15 rs1909507 1.36×10−2 1.12 (1.02–1.23) 38.7 0.20 
AR 16 rs5919390 1.49×10−2 1.14 (1.03–1.28) 32.2 0.23 
WT1 17 rs7395210 1.51×10−2 0.91 (0.85–0.98) 42.5 0.18 
AKR1C4 18 rs11252994 1.96×10−2 1.09 (1.01–1.17) 30.0 0.24 
HSD17B3 19 rs2476927 2.04×10−2 0.92 (0.85–0.99) 0.65 
WNT4 20 rs2807347 2.96×10−2 1.13 (1.01–1.25) 0.87 
NR0B1 21 rs4141069 3.44×10−2 0.90 (0.82–0.99) 0.82 
FGFR2 22 rs1219648 3.56×10−2 0.92 (0.86–0.99) 5.97 0.35 
INSR 23 rs17253937 4.22×10−2 1.10 (1.00–1.21) 0.47 
SOX3 24 rs5907596 4.39×10−2 1.26 (1.01–1.57) 16.9 0.30 
SOX9 25 rs2229989 4.82×10−2 1.09 (1.00–1.19) 0.80 
FGF9 26 rs264724 5.04×10−2 0.89 (0.79–1.00) 0.77 
FST 27 rs745321 6.32×10−2 1.07 (1.00–1.16) 0.91 
CBX2 28 rs4243253 9.66×10−2 0.93 (0.86–1.01) 0.46 
SOX10 29 rs5995529 1.52×10−1 0.95 (0.88–1.02) 0.73 
FOXL2 30 rs9809852 1.61×10−1 1.05 (0.98–1.13) 47.2 0.15 
DHH 31 rs7975791 1.64×10−1 1.16 (0.94–1.42) 0.80 
SRY 32 N/A N/A N/A N/A N/A N/A N/A 
Gene Rank Marker A1 A2 P OR (95% CI) I2(%)* PHet
DMRT1 rs755383 1.16×10−30 0.63 (0.58–0.68) 0.77 
DMRT1 rs4742419 1.53×10−15 1.34 (1.25–1.45) 0.04 0.37 
DMRT1 rs2370200 6.43×10−13 0.71 (0.65–0.78) 0.37 
DMRT1 rs7029148 3.34×10−12 0.77 (0.72–0.83) 8.2 0.34 
DMRT1 rs7035551 1.06×10−11 0.76 (0.71–0.82) 4.7 0.35 
DMRT1 rs4740943 4.43×10−8 0.81 (0.75–0.87) 0.50 
DMRT1 rs4742527 3.66×10−7 0.80 (0.73–0.87) 0.37 
DMRT1 rs7859987 2.03×10−6 0.80 (0.73–0.88) 0.97 
DMRT1 rs881684 2.74×10−6 1.19 (1.11–1.28) 0.91 
DMRT1 rs2025307 3.21×10−6 1.19 (1.11–1.28) 0.69 
DMRT1 rs16924776 6.39×10−6 1.26 (1.14–1.40) 0.63 
MAP3K1 rs2441132 1.46×10−5 0.82 (0.75–0.89) 22.9 0.27 
DMRT1 rs3812523 4.90×10−5 1.22 (1.11–1.35) 0.73 
ZFPM2 rs3779770 1.99×10−4 0.86 (0.79–0.93) 0.38 
DMRT1 rs10977244 2.44×10−4 1.15 (1.07–1.23) 30.4 0.24 
GATA4 rs17153747 3.22×10−4 1.22 (1.10–1.36) 0.96 
IGF1R rs2715442 3.43×10−4 1.15 (1.07–1.24) 41.3 0.18 
GATA4 rs4840584 3.66×10−4 1.26 (1.11–1.43) 0.91 
AKR1C2 rs11252845 5.24×10−4 1.29 (1.12–1.49) 0.55 
CTNNB1 rs4973937 7.42×10−4 1.20 (1.08–1.34) 36.9 0.21 
DMRT1 rs4742545 1.35×10−3 0.82 (0.73–0.93) 0.71 
DMRT1 rs10815567 1.58×10−3 0.89 (0.82–0.95) 0.40 
DMRT1 rs10977270 1.61×10−3 0.88 (0.81–0.95) 0.83 
GATA4 rs2645444 2.17×10−3 1.15 (1.05–1.26) 5.0 0.35 
NR5A1 rs2416933 2.49×10−3 1.12 (1.04–1.20) 0.52 
RSPO1 rs501252 3.38×10−3 1.13 (1.04–1.22) 0.62 
EMX2 10 rs242965 3.85×10−3 0.88 (0.81–0.96) 24.5 0.27 
ZFPM2 rs1375960 4.96×10−3 0.86 (0.77–0.96) 0.39 
WWOX 11 rs9319519 5.08×10−3 1.11 (1.03–1.19) 0.89 
ZFPM2 rs13252177 5.91×10−3 1.24 (1.07–1.45) 38.0 0.20 
CYP11A1 12 rs4887142 5.95×10−3 0.89 (0.81–0.97) 0.49 
MAP3K4 13 rs1247351 6.81×10−3 0.90 (0.83–0.97) 0.76 
IGF1R rs4966036 9.18×10−3 1.12 (1.03–1.23) 0.63 
SOX8 14 rs428233 9.37×10−3 1.12 (1.03–1.21) 0.94 
GATA4 rs6990313 9.38×10−3 1.18 (1.04–1.33) 40.6 0.19 
LHX9 15 rs1909507 1.36×10−2 1.12 (1.02–1.23) 38.7 0.20 
AR 16 rs5919390 1.49×10−2 1.14 (1.03–1.28) 32.2 0.23 
WT1 17 rs7395210 1.51×10−2 0.91 (0.85–0.98) 42.5 0.18 
AKR1C4 18 rs11252994 1.96×10−2 1.09 (1.01–1.17) 30.0 0.24 
HSD17B3 19 rs2476927 2.04×10−2 0.92 (0.85–0.99) 0.65 
WNT4 20 rs2807347 2.96×10−2 1.13 (1.01–1.25) 0.87 
NR0B1 21 rs4141069 3.44×10−2 0.90 (0.82–0.99) 0.82 
FGFR2 22 rs1219648 3.56×10−2 0.92 (0.86–0.99) 5.97 0.35 
INSR 23 rs17253937 4.22×10−2 1.10 (1.00–1.21) 0.47 
SOX3 24 rs5907596 4.39×10−2 1.26 (1.01–1.57) 16.9 0.30 
SOX9 25 rs2229989 4.82×10−2 1.09 (1.00–1.19) 0.80 
FGF9 26 rs264724 5.04×10−2 0.89 (0.79–1.00) 0.77 
FST 27 rs745321 6.32×10−2 1.07 (1.00–1.16) 0.91 
CBX2 28 rs4243253 9.66×10−2 0.93 (0.86–1.01) 0.46 
SOX10 29 rs5995529 1.52×10−1 0.95 (0.88–1.02) 0.73 
FOXL2 30 rs9809852 1.61×10−1 1.05 (0.98–1.13) 47.2 0.15 
DHH 31 rs7975791 1.64×10−1 1.16 (0.94–1.42) 0.80 
SRY 32 N/A N/A N/A N/A N/A N/A N/A 

* SNP markers with PHet ≤ 0.05 or I2 > 50 are not tabulated.

Fourteen genes were located within a 250-kb flanking region of the four lead SNP markers at DMRT1, GATA4, IGF1R and ZFPM2 (Supplementary Material, Table S11), which were the gene regions mutually identified by the two ranking schemes noted earlier. All four top SNPs were located in an intronic region of a sex determination gene; and thus, all four (100%) sex determination genes were in closer proximity to a top marker than was any of the 14 nearby genes (0%; PFisher’s = 0.0004). Using HaploReg (51), we evaluated the ENCODE and Roadmap resources (52,53) for surrogate SNP markers in linkage disequilibrium (LD) with the top-ranking SNP markers (Supplementary Material, Tables S10 and S12). Two (50%) of the sex determination genes housed a surrogate marker (r2 ≥ 0.5, 1000 Genomes CEU) that was a non-synonymous substitution, but none (0%) of the 14 neighboring genes did so (PFisher’s= 0.0008; Supplementary Material, Table S12). The non-synonymous substitutions were rs3739583 at codon 45 of DMRT1 (r2 = 0.95 with rs3812523; c.133T>A; p.Ser45Thr) and rs3729856 at codon 377 of GATA4 (r2 = 0.88 with rs17153747; c.1129A>G; p.Ser377Gly) (Supplementary Material, Table S12); however, both variants are predicted by PolyPhen-2 to be tolerated (54). Neither the 4 sex determination genes nor the 14 neighboring genes contained a surrogate marker (r2 ≥ 0.5, 1000 Genomes CEU) present within an eQTL (PFisher's = 1.0) (Supplementary Material, Table S12). We noted that 10 surrogate SNP markers (r2 ≥ 0.8, 1000 Genomes CEU) in the DMRT1 region, 18 in GATA4, 7 in IGFR1 and 6 in ZFPM2 map to regions reported to be within both an enhancer and DNAse hypersensitivity region and report having a changed transcription-binding factor motif. In addition, 5 surrogate SNP markers (r2 ≥ 0.8, 1000 Genomes CEU) in the DMRT1 region, 14 in GATA4, 2 in IGFR1 and 2 in ZFPM2 map to regions reported to be within both an promoter and DNAse hypersensitivity region and report having a changed transcription-binding factor motif (Supplementary Material, Table S12).

DISCUSSION

GWA studies have identified several TGCT susceptibility loci containing genes that are important for male germ cell differentiation and maturation. One of these genes, DMRT1, plays crucial roles in male germ cell development and sex determination across the phylogenetic spectrum. We undertook pathway-based analyses of genes influencing sex determination to identify additional loci associated with the development of TGCT. We completed analyses using three different programs because consistency of findings across approaches can diminish the potential of false-positive findings. We found evidence of a statistically significant association with a set of genes involved in sex determination, including DMRT1, with TGCT status in our GWA dataset and in two additional independent TGCT GWAs datasets. Even after removing DRMT1 from the sex determination gene set, there were strong indications of a statistically significant association with TGCT in the datasets. These results suggest that our pathway-based finding is not solely driven by the associations of DMRT1 SNP markers and that variation in other genes included in this gene set are involved in the development of TGCT.

Following DMRT1, SNP markers within or proximate to the GATA4, IGF1R and ZFPM2 gene regions were highly ranked across the Penn, UK and NCI GWA studies. GATA4 is a member of the GATA family of zinc-finger transcription factors and is located on 8p23.1-p22. It is expressed in the fetal mouse gonads, and later in embryonic development expression is up-regulated in XY Sertoli cells while down-regulated in all cell types of XX gonads (55,56). Gata4 interacts with several proteins, including Nr5a1 and Zfpm2 [zinc-finger protein, FOG (friend of GATA) family member 2] to regulate the expression of several other sex determining genes, including Sry, Sox9 and Dmrt1 (5759). ZFPM2 is located on 8q23 and belongs to the FOG family of proteins, as mentioned before interacts with Gata4, and ZFPM2 can both activate and down-regulate the expression of GATA-target genes (60,61). IGF1R is a member of the insulin family of growth factors (IGF) and is located on 15q26.3. This family is well known as a key regulator of energy metabolism and growth. IGF1R is involved in transformation and is highly expressed in malignant tissues, where it functions as an anti-apoptotic agent by enhancing cell survival (62). Of interest, IGF1R and other members of the IGF-family have been studied previously using candidate-gene approaches as potentially influencing TGCT risk; however, no associations between polymorphisms or haplotypes of the IGF genes and TGCT risk were observed (63).

Pathway-based analyses are based on the premise that biological processes are driven by several molecular pathways, and the combination of non-overlapping, non-concurrent disruptions in a sufficient fraction of those pathways can lead to a disorder. Other groups have successfully used pathway-based approaches to test whether a group of genes in the same functional pathway are jointly associated with disease (4750). These types of analyses focus on the combined effects of many loci, each making only a small contribution to overall disease susceptibility (64). Thus, our results suggest that individuals with TGCT share a disrupted sex determination pathway although the associated gene variants within this pathway may be heterogeneous, and the fractional contribution of each gene variant may be low. Moreover, the observed perturbation in the sex determination pathway is not solely driven by the associations of DMRT1 SNP markers indicating that variation in other genes important to sex determination are involved in the development of TGCT. Of note, both DMRT1 and GATA4 housed a non-synonymous substitution in high LD with its top-ranking SNP marker, a feature not observed in any of the 14 genes flanking the DMRT1, GATA4, IGF1R or ZFPM2 regions. As well, the top-ranking SNP at these four regions is located in an intronic region of the gene itself. Together, these findings lend further support to the importance of inherited variation in genes in the sex determination pathway to TGCT disease status.

At the gene level, the rank order of the best representative marker varies among individual GWA scans, and thus replicating these potentially weaker susceptibility markers might present a challenge. Indeed, even replicating stronger susceptibility markers can be challenging, and lack of replication may be due to small sample sizes resulting in lack of power. As well, observed heterogeneity across datasets can be due to differences in genetic makeup of the white non-Hispanic population, differences in frequency of tumor subtype or variability in clinical stage or subgroup analysis. For example, in the UK study, SNP markers proximate to ATF7IP reached genome-wide significance (22), although this locus did not reach genome-wide significance in the smaller-sized Penn Study (19). Interestingly, markers near ATF7IP were found to be associated with more aggressive forms of TGCT in a modest-sized replication effort in a Croatian population (65). Additionally, KITLG markers were strongly associated with the seminoma subtype and weakly with the non-seminoma subtype in a smaller-sized replication effort in an Italian population (66), whereas no differences between subtypes were observed in the UK and Penn GWA scans (18,19,22,23). In our pathway analysis, such heterogeneity may explain the difference in ranking of the most representative SNP markers/genes within the sex determination gene set across the three scans, although observed differences across the scans may also be related to design differences in genotyping platforms.

We also addressed the issue of SNP marker heterogeneity across the three GWA scans by completing meta-analysis and assessing combined genetic associations. Results from meta-analysis supported findings based on ranking of the gene markers. Of the five top gene regions identified by either ranking schema, four regions (DMRT1, GATA4, IGF1R and ZFPM2) were mutually identified. Still, we did note heterogeneity of association at many SNP markers in several gene regions in the meta-analysis, and these were removed from further consideration.

Taken together, we were able to identify genes involved in sex determination as additional biological underpinnings of TGCT using a pathway-based approach. These findings may serve to augment the pool of established TGCT susceptibility loci to be used when assessing risk of disease. The results presented here further emphasize that genes involved in processes linked to normal germ cell development are the major drivers of TGCT.

MATERIAL AND METHODS

Pathway-based analysis

We completed a search in September 2013 of OMIM (http://omim.org/) and PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) to identify a set of genes encoding proteins implicated in sex reversal and sex determination in human and mice using the search terms ‘sex reversal’ and ‘sex determination’. We only included genes in which reported mutations, deletions and/or copy-number changes lead to sex reversal in humans and mice, and we identified a set of 32 genes (Supplementary Material, Table S13). We used the software tools i-GSEA4GWAS, MAGENTA and GSA-SNP to conduct pathway-based analysis on the sex determination gene set using input parameters recommended by the developers (6769). i-GSEA4GWAS and GSA-SNP map SNP markers within a 20-kb (default value) region upstream or downstream of a given gene locus, whereas MAGENTA maps 110 kb upstream and 40 kb downstream (default values) of a given gene locus. If multiple markers are assigned to the same locus, GSA-SNP chooses the second most significant P-value of all mapped SNP marker associations as the gene's P-value, thus decreasing the likelihood of spurious associations (68). In contrast, i-GSEA4GWAS and MAGENTA assign the most significant P-value of all mapped SNP markers as the gene's P-value (67,69). The MAGENTA algorithm also adjusts for characteristics that may confound observed gene set associations such as LD patterns, gene size and variant number (69). We included in the pathway-based analysis canonical pathways (Biocarta, KEGG and Reactome) and gene ontology gene sets (GO biological processes, GO cellular components and GO molecular functions) downloaded from the Molecular Signatures Database version 4 (70). We report FDR-corrected P-values, i.e. q-values (FDRG for GSA-SNP, FDRI for i-GSEA4GWAS and FDRM for MAGENTA.

TGCT case–control studies used for pathway-based analysis

As discovery input data for the pathway-based analysis, we used P-values from our previously conducted Penn GWA scan, for which we had determined the association of TGCT with 609 482 SNP markers passing quality control by Fisher's exact test, implemented in PLINK (71). Genotyping was conducted among 349 TGCT cases and 919 CAD controls on the Affymetrix® Genome-Wide Human SNP Array 6.0 (Supplementary Material, Table S14); the complete details of quality control and study description were reported previously (23).

As replication for the pathway-based analysis, we used data from SNP markers that passed quality control in the NCI (499 529 SNP markers) and UK GWA (307 263 SNP markers) scans. The NCI GWAS is a combined analysis of The US Servicemen's Testicular Tumor Environmental and Endocrine Determinants Study and the NCI Familial Testicular Cancer Study in which genotypes from 582 TGCT cases and 1056 controls were obtained using the Illumina 660 K array (Supplementary Material, Table S14). Complete details regarding study design and quality control processes have been reported previously (16,24). For the UK GWAS, a total of 979 TGCT cases including unrelated TGCT cases identified from a UK study of familial testicular cancer and a national collection of TGCT cases treated within the UK were genotyped on the Illumina HumanCNV370-Duo and compared with 4947 controls from the 1958 Birth Cohort and the UK National Blood Service that were genotyped on the Illumina Human 1.2M by the Wellcome Trust Case Control Consortium (WTCCC2) (Supplementary Material, Table S14). Complete details of quality control and study description were reported previously (1618,22). All subjects are of European descent, and each participant provided written informed consent approved by their local Institutional Review Boards.

Imputation and meta-analysis

In order to maximize the number of SNP markers common across the three GWA datasets, we imputed all gene regions in the sex determination gene set in the Penn GWA scan (which was the only study to use an Affymetrix array). Imputation was conducted with IMPUTE2 (72), using phased haplotypes from the full 1000 Genomes reference dataset (release December 2013) downloaded from the IMPUTE2 website. We analyzed case–control associations for imputed SNP markers using SNPTEST (73).

Meta-analysis of observed or imputed markers from Penn and observed markers from the UK and NCI and GWA scans that were available in all three datasets was performed. PLINK was first used to estimate the combined P-value and OR using a fixed-effects model for all genetic regions in the sex determination gene set (71,74). Then, for top hits, METAL was used to calculate the combined P-value, OR and 95% CI based on the inverse variance method; and Cochran Q (PHet ≤ 0.05) and I2 (>50%) were used to assess heterogeneity of association across studies (75).

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

The Penn GWAS (PENN) was supported by the Abramson Cancer Center at the University of Pennsylvania and US National Institutes of Health grant R01CA114478 to P.A.K. and K.L.N. The NCI GWAS work was supported by the Intramural Research Program of the NCI and by support services contract HHSN261200655004C with Westat, Inc. The UK testicular cancer study was supported by the Institute of Cancer Research, Cancer Research UK and the Wellcome Trust and made use of control data generated by the Wellcome Trust Case Control Consortium 2 (WTCCC2).

ACKNOWLEDGEMENTS

We thank M. McDermoth, S.L. Ciosek and R. Letrero for their contribution to the Penn TGCT study. We thank C. Berg and P. Prorok, Division of Cancer Prevention, NCI, the screening center investigators and the staff of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, T. Riley and staff at Information Management Services, Inc., and B. O'Brien and staff at Westat, Inc., for their contributions to the PLCO Cancer Screening Trial.

Conflict of Interest statement. None declared.

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Author notes

Present address: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA.
Present address: Center for Biostatistics in AIDS research, Harvard School of Public Health, Boston, MA, USA.
These authors jointly directed this work.
§
Present address: Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA.