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. Author manuscript; available in PMC: 2017 Dec 12.
Published in final edited form as: Nat Genet. 2017 Jun 12;49(7):1141–1147. doi: 10.1038/ng.3879

Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor

Zhaoming Wang 1,†, Katherine A McGlynn 1, Ewa Rajpert-De Meyts 2, D Timothy Bishop 3, Charles Chung 1, Marlene D Dalgaard 2,4, Mark H Greene 1, Ramneek Gupta 4, Tom Grotmol 5, Trine B Haugen 6, Robert Karlsson 7, Kevin Litchfield 8, Nandita Mitra 9, Kasper Nielsen 4, Louise C Pyle 10,11, Stephen M Schwartz 12, Vésteinn Thorsson 13, Saran Vardhanabhuti 14, Fredrik Wiklund 7, Clare Turnbull 8,15, Stephen J Chanock 1, Peter A Kanetsky 16,18, Katherine L Nathanson 10,17,18, for the Testicular Cancer Consortium
PMCID: PMC5490654  NIHMSID: NIHMS872189  EMSID: EMS72577  PMID: 28604732

Introductory Paragraph

The international TEsticular CAncer Consortium (TECAC) combined five published genome-wide association studies of testicular germ cell tumors (TGCT; 3,558 cases and 13,970 controls) to identify novel susceptibility loci. We conducted a fixed effects meta-analysis, including the first analysis of the X chromosome. Eight new loci mapping to 2q14.2, 3q26.2, 4q35.2, 7q36.3, 10q26.13, 15q21.3, 15q22.31, and Xq28 achieved genome-wide significance (P < 5×10−8). Most loci harbor biologically plausible candidate genes. We refined previously reported associations at 9p24.3 and 19p12 by identifying one and three additional independent SNPs, respectively. In aggregate, the 39 independent markers identified to date explain 37% of father-to-son risk, 8% of which can be attributed to the 12 new signals reported here. Our findings substantially increase the number of known TGCT susceptibility alleles, move the field closer to a comprehensive understanding of the underlying genetic architecture of TGCT, and provide further clues into the etiology of TGCT.

In Europe and the United States, testicular germ cell tumors (TGCT) are the most common cancers in young men aged 20 to 39 years1. The incidence of TGCT is rising, and is highest in men of Northern European and lowest in men of African ancestry1–3. Risk factors for TGCT include cryptorchidism, adult height, prior diagnosis and familial history of TGCT4–8; its heritability ranges from 37% to 49%9,10. Despite the multiple lines of evidence demonstrating a considerable genetic component of TGCT risk, linkage and candidate gene approaches to find rare, highly-penetrant susceptibility genes involved in TGCT etiology were unsuccessful11.

In contrast, genome wide association studies (GWAS) of TGCT have had remarkable success in identifying susceptibility loci with strong effects. Of the 27 replicated loci, most were discovered using GWAS chip-based microarray platforms12–18, with 13 identified after replication on the iCOGs array19–21 and one identified as a candidate region22. The genes mapping at or near identified susceptibility loci have revealed several biological themes that are highly likely to be important to TGCT development, including male germ cell maturation and differentiation, KIT-MAPK signaling, DNA damage response, and chromosomal segregation.

We imputed each of five published TGCT GWAS scans12,18,20,23, and combined the association test statistics for a total of 8,960,654 autosomal and 249,696 chromosome X single nucleotide polymorphisms (SNPs), after excluding those with INFO score < 0.3 or minor allele frequency (MAF) < 0.01. We conducted a fixed-effects meta-analysis for 3,558 cases and 13,970 controls (Methods and Supplementary Table 1). The genomic control factor λ = 1.037 suggests little systematic inflation from population stratification (Supplementary Fig. 1). We identified eight new TGCT susceptibility loci surpassing genome-wide significance, and an additional four novel independent loci in two previously established regions (9p24.3 and 19p12) (Table 1). Two of these loci (rs6837349 and rs12912292) showed evidence of effect measure heterogeneity (I2 > 0.50) across the five sample sets. We also determined the Bayes false discovery probability (BFDP)24 for these 12 loci using a prior probability of 0.0001 and odds ratio of 1.2 (Supplementary Table 2). Two loci, rs61408740 and rs17336718, failed to surpass a BFDP < 0.05, likely because of their low minor allele frequencies (0.023 and 0.053, respectively).

Table 1.

TGCT meta-analysis association results for novel loci and new independent SNPs in established loci

Cytoband Gene Neighborhood SNP Position Study Info Controls Cases Reference allele Effect allele Effect allele frequency OR CI P Phet I2
Control Case
2q14.2 TFCP2L1 rs2713206 122007941 NCI 0.92 1055 581 C T 0.15 0.20 1.45 (1.18–1.79) 4.19E-04
UK 0.98 4945 985 C T 0.15 0.17 1.15 (1.00–1.31) 4.43E-02
PENN 0.90 918 480 C T 0.16 0.20 1.32 (1.06–1.64) 1.25E-02
Norway/Sweden 0.93 6687 1326 C T 0.14 0.17 1.25 (1.09–1.44) 1.74E-03
Denmark 0.85 363 183 C T 0.17 0.21 1.41 (0.98–2.03) 6.39E-02
Combined 13968 3555 1.26 (1.16–1.36) 1.68E-08 0.39 3.6
3q26.2 GPR160 rs3755605 169756119 NCI 0.97 1055 581 C T 0.41 0.44 1.14 (0.98–1.33) 9.31E-02
UK 0.98 4946 985 C T 0.39 0.43 1.21 (1.09–1.33) 2.25E-04
PENN 0.97 918 480 C T 0.41 0.43 1.08 (0.92–1.27) 3.54E-01
Norway/Sweden 0.98 6687 1326 C T 0.40 0.44 1.24 (1.12–1.37) 2.08E-05
Denmark 0.99 363 183 C T 0.39 0.43 1.19 (0.91–1.55) 1.94E-01
Combined 13969 3555 1.19 (1.12–1.26) 3.87E-09 0.67 0.0
4q35.2 ZFP42 rs6837349 188921355 NCI 0.99 1055 581 G T 0.66 0.63 0.84 (0.72–0.98) 2.71E-02
UK 0.99 4945 985 G T 0.66 0.62 0.83 (0.75–0.92) 3.98E-04
PENN 0.69 918 480 G T 0.66 0.68 1.15 (0.94–1.40) 1.80E-01
Norway/Sweden 0.99 6687 1326 G T 0.68 0.63 0.77 (0.70–0.86) 1.18E-06
Denmark 0.74 363 183 G T 0.66 0.65 0.96 (0.70–1.32) 8.00E-01
Combined 13968 3555 0.84 (0.79–0.89) 3.13E-08 0.02 67.4
7q36.3 NCAPG2 rs11769858 158501492 NCI 0.93 1055 581 T C 0.67 0.65 0.89 (0.76–1.06) 1.86E-01
UK 0.95 4945 985 T C 0.69 0.65 0.84 (0.76–0.94) 1.51E-03
PENN 0.88 918 480 T C 0.64 0.60 0.81 (0.68–0.96) 1.69E-02
Norway/Sweden 0.91 6687 1326 T C 0.70 0.66 0.82 (0.73–0.91) 3.89E-04
Denmark 0.90 363 183 T C 0.68 0.64 0.82 (0.62–1.08) 1.59E-01
Combined 13968 3555 0.84 (0.79–0.89) 2.38E-08 0.92 0.0
9p24.3* DMRT1 rs55873183 878563 NCI 0.73 1055 581 A G 0.06 0.07 1.34 (0.93–1.93) 1.14E-01
UK 0.82 4945 985 A G 0.06 0.09 1.90 (1.52–2.38) 1.54E-08
PENN 0.73 918 480 A G 0.06 0.09 1.97 (1.36–2.84) 3.05E-04
Norway/Sweden 0.80 6687 1326 A G 0.07 0.11 2.08 (1.70–2.54) 1.21E-12
Denmark 0.81 363 183 A G 0.07 0.11 1.84 (1.11–3.03) 1.75E-02
Combined 13968 3555 1.89 (1.67–2.14) 2.18E-23 0.36 8.1
10q26.13 LHPP rs61408740 126274612 NCI 0.99 1056 582 C G 0.02 0.04 1.68 (1.09–2.60) 1.89E-02
UK 0.95 4945 985 C G 0.03 0.04 1.64 (1.22–2.20) 1.05E-03
PENN 0.94 919 480 C G 0.03 0.04 1.92 (1.22–3.03) 4.92E-03
Norway/Sweden 1.00 6687 1326 C G 0.02 0.03 1.53 (1.12–2.09) 7.79E-03
Denmark 0.96 363 183 C G 0.02 0.03 1.61 (0.69–3.76) 2.75E-01
Combined 13970 3556 1.65 (1.38–1.96) 1.75E-08 0.95 0.0
15q21.3 PRTG rs12912292 56038707 NCI 0.95 1055 581 G A 0.51 0.55 1.25 (1.07–1.46) 4.76E-03
UK 0.99 4946 985 G A 0.53 0.55 1.09 (0.99–1.20) 9.18E-02
PENN 0.95 918 480 G A 0.47 0.56 1.44 (1.23–1.70) 8.74E-06
Norway/Sweden 0.95 6687 1326 G A 0.52 0.58 1.26 (1.14–1.39) 3.42E-06
Denmark 0.95 363 183 G A 0.51 0.57 1.30 (1.00–1.69) 4.81E-02
Combined 13969 3555 1.22 (1.15–1.29) 1.38E-11 0.03 61.4
15q22.31 MAP2K1, TIPIN rs60180747 66663261 NCI 0.99 1055 582 A C 0.26 0.30 1.27 (1.07–1.50) 5.59E-03
UK 0.99 4946 986 A C 0.26 0.30 1.23 (1.10–1.37) 2.34E-04
PENN 0.99 919 481 A C 0.27 0.34 1.37 (1.15–1.63) 4.53E-04
Norway/Sweden 1.00 6687 1326 A C 0.26 0.28 1.18 (1.05–1.31) 3.89E-03
Denmark 1.00 363 183 A C 0.27 0.31 1.22 (0.93–1.60) 1.59E-01
Combined 13970 3558 1.23 (1.16–1.32) 1.10E-10 0.70 0.0
19p12* ZNF728 rs58521262 23205184 NCI 0.99 1056 581 G A 0.151 0.106 0.69 (0.55–0.85) 6.64E-04
UK 1.00 4946 985 G A 0.140 0.106 0.74 (0.64–0.86) 4.83E-05
PENN 0.98 919 481 G A 0.154 0.123 0.78 (0.63–0.98) 2.95E-02
Norway/Sweden 0.99 6687 1326 G A 0.173 0.136 0.74 (0.65–0.84) 4.90E-06
Denmark 0.99 363 183 G A 0.183 0.137 0.71 (0.51–1.00) 5.10E-02
Combined 13971 3556 0.74 (0.68–0.80) 4.87E-14 0.95 0.0
19p12* ZNF726 rs34601376 24050828 NCI 0.84 1055 581 A T 0.216 0.220 1.04 (0.85–1.28) 6.77E-01
UK 0.92 4945 985 A T 0.202 0.242 1.29 (1.14–1.46) 5.08E-05
PENN 0.83 918 480 A T 0.191 0.229 1.32 (1.07–1.64) 1.02E-02
Norway/Sweden 0.88 6687 1326 A T 0.195 0.238 1.39 (1.22–1.58) 4.19E-07
Denmark 0.69 363 183 A T 0.146 0.168 1.32 (0.85–2.05) 2.23E-01
Combined 13968 3555 1.29 (1.20–1.39) 2.40E-11 0.23 29.2
19p12* ZNF257 rs73019876 22267849 NCI 0.93 1055 581 T G 0.45 0.42 0.89 (0.76–1.04) 1.35E-01
UK 0.96 4946 985 T G 0.45 0.40 0.83 (0.75–0.91) 1.51E-04
PENN 0.95 918 480 T G 0.51 0.49 0.91 (0.78–1.07) 2.59E-01
Norway/Sweden 0.95 6687 1326 T G 0.43 0.41 0.85 (0.77–0.94) 1.22E-03
Denmark 0.94 363 183 T G 0.44 0.36 0.72 (0.55–0.93) 1.25E-02
Combined 13969 3555 0.85 (0.80–0.90) 2.04E-08 0.54 0.0
Xq28 TKTL1 rs17336718 153536119 NCI 0.97 1056 582 C T 0.05 0.09 1.33 (1.07–1.64) 8.85E-03
UK 0.63 4945 986 C T 0.05 0.07 1.46 (1.19–1.80) 3.71E-04
PENN 0.78 918 480 C T 0.05 0.09 1.59 (1.23–2.06) 4.06E-04
Denmark 0.84 363 183 C T 0.06 0.08 1.15 (0.78–1.69) 4.71E-01
Combined 7282 2231 1.41 (1.25–1.59) 3.84E-08 0.51 0.0
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*

New independent SNPs in established loci

Prior reports identified 27 independent SNPs, at 25 distinct regions, and the gr/gr deletion associated with TGCT susceptibility (Fig. 1). In our current study, 19 of the SNPs (at 17 loci) reached a level of genome-wide significance (Table 2). Eight previously reported susceptibility markers were not identified in our meta-analysis likely related to limited study power, staged replication of GWAS chip-based array results in published studies, and possible residual population substructure. Considering these limitations and to further place context around our findings, we calculated the BFDP for these 27 loci using a prior probability of 0.10, which assumes 10% of the previously established loci are true positives (Supplementary Table 2). This threshold is more liberal than one that would be used to identify novel loci, but still may be too conservative for the re-identification of previously identified susceptibility markers. Only one locus of all 27, rs11705932, failed to surpass a BFDP < 0.05.

Figure 1. All identified SNP markers associated with TGCT susceptibility to date.

Figure 1

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In the ideogram, red dots and red rs number annotation indicate SNPs identified and described in the current study (P ≤ 1 × 10−8); blue dots and blue rs number annotation represent previously identified SNP markers achieving genome wide significance (P ≤ 1 × 10−8) in the current study; and gray dots and gray rs number annotation are previously identified SNPs that fail to achieve genome wide significance in this study (P > 1 × 10−8).

Table 2.

TGCT meta-analysis association results for previously published susceptibility loci

Cytoband Gene Neighborhood SNP Position OR CI P Phet I2
1q22 KIAA0446
SLC25A44 		rs2072499 156169610 1.20 (1.13–1.27) 1.63E-09 0.78 0.0
1q24.1 UCK2 rs3790672 165873392 1.27 (1.20–1.35) 2.15E-14 0.84 0.0
3p24.3 DAZL rs10510452 16625048 0.82 (0.77–0.87) 3.36E-10 0.76 0.0
3q23* TFDP2
DKFZp434G222 		rs11705932 141818850 0.88 (0.82–0.94) 3.51E-04 0.90 0.0
3q25.31 rs1510272 156300724 0.83 (0.78–0.88) 7.15E-09 0.45 0.0
4q22.3* SMARCAD1
HPGDS 		rs17021463 95224812 0.87 (0.82–0.92) 1.36E-06 0.06 56.5
4q24 CENPE
CENPE variant protein 		rs2720460 104054686 0.78 (0.74–0.83) 9.88E-17 0.92 0.0
5p15.33 TERT
hTERT 		rs2736100 1286516 1.29 (1.22–1.37) 7.69E-20 0.41 0.0
5p15.33 CLPTM1L rs4635969 1308552 1.46 (1.37–1.57) 2.83E-27 0.15 41.0
5q31.1* CATSPER3
PITX1
AK026965 		rs3805663 134366200 0.88 (0.83–0.93) 9.16E-06 0.36 8.2
5q31.3 SPRY4 rs4624820 141681788 1.51 (1.42–1.59) 2.59E-46 0.51 0.0
6p21.31 BAK1
AY383626
C6orf227 		rs210138 33542538 1.55 (1.44–1.66) 2.51E-34 0.66 0.0
7p22.3 MAD1L1 rs12699477 1968953 1.21 (1.14–1.28) 2.24E-10 0.13 43.7
8q13.3* PRDM14 rs7010162 70976505 0.86 (0.81–0.91) 1.42E-07 0.33 13.5
9p24.3 DMRT1 rs7040024** 845516 0.67 (0.62–0.71) 1.21E-32 0.04 59.3
9p24.3 DMRT1 rs755383** 863635 1.49 (1.41–1.58) 6.52E-41 0.52 0.0
11q14.1* GAB2 rs7107174 77997936 1.19 (1.10–1.29) 6.35E-06 0.36 8.7
12p13.1 ATF7IP
PLBD1 		rs2900333 14653867 0.85 (0.80–0.90) 2.71E-08 0.20 33.6
12q21.32 KITLG rs3782181 88953561 2.02 (1.88–2.18) 1.32E-76 0.90 0.0
16p13.13* BCAR4
CATX-11
RSL1D1 		rs4561483 11920037 0.86 (0.81–0.91) 4.19E-07 0.45 0.0
16q12.1 HEATR3
AF086132 		rs8046148 50142944 1.24 (1.15–1.33) 3.15E-09 0.21 32.2
16q23.1 RFWD3 rs4888262 74670458 0.83 (0.78–0.88) 5.65E-11 0.08 52.7
16q24.2* ZFPM1 rs55637647 88549264 1.18 (1.11–1.26) 1.33E-07 0.40 1.4
17q12 HNF1B rs7501939 36101156 1.26 (1.19–1.34) 1.27E-14 0.42 0.0
17q22 TEX14 rs9905704 56632543 1.27 (1.19–1.35) 1.99E-14 0.68 0.0
19p12 AK125686 rs2195987 24149545 1.23 (1.15–1.32) 1.21E-09 0.89 0.0
21q22.3* MCM3APAS
MCM3AP 		rs2839186 47690068 1.13 (1.07–1.20) 2.00E-05 0.02 67.1
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*

Indicates sub genome-wide statistical significance.

**

Pairwise r2=0.38.

rs12912292 is the most statistically significant novel SNP marker in our study, (OR=1.22; P = 1.38 ×10−11), marking a 131 kb haploblock on 15q21.3 (Table 1 and Supplementary Fig. 2a). This region contains only a single gene, PRTG, a member of the immunoglobulin superfamily implicated in neurogenesis25. PRTG is highly expressed in thyroid, testes and uterus (Supplementary Fig. 3a). No variant in this region is an eQTL in either normal testes or TGCT.

The SNP marker rs60180747 (OR=1.23; P = 1.10 ×10−10) marks a 261 kb haploblock on 15q22.31 that contains several genes, including TIPIN (TIMELESS-interacting protein) MAP2K1 (mitogen-activated protein kinase kinase 1), DIS3L (DIS3 like exosome 3′–5′ exoribonuclease), SNAPC5 (small nuclear RNA activating complex, polypeptide 5), and LCTL (lactose-like) (Table 1 and Supplementary Fig. 2b). Several of these proteins, particularly ZWILCH and TIPIN, have high and somewhat specific expression in testes (Supplementary Fig. 3b–g). TIPIN coordinates the DNA replication checkpoint by interacting with Replication protein A26; ZWILCH is a kinetochore protein important for proper chromatid alignment during cell division27. A missense mutation in ZWILCH, p.Ser230Gly, lies within the LD block (Supplementary Table 3). The LD block also contains a single eQTL to RP11-653J6.1, which is a long non-coding (lnc) RNA highly specific to testes (Supplemental Fig. 3h). RP11-653J6.1 levels and eQTLs were not measured in TGCT Cancer Genome Atlas (TCGA). Further dissection of this signal is needed to pinpoint the candidate gene.

The SNP marker rs3755605 (OR=1.19; P = 3.87 ×10−9) identifies a 213 kb haploblock containing three genes, GPR160 (G protein-coupled receptor 160), PHC3 (polyhomeotic homolog 3), and PRKCI (protein kinase C, iota form) on 3q26.2 (Table 1 and Supplementary Fig. 2c). Several SNPs across this block are eQTLs for GPR160 (Supplementary Table 3, Supplementary Fig. 4a); the risk allele is associated with increased expression in both normal testes and TGCT (Supplementary Fig. 5a and Supplementary Fig. 6a). Several SNPs in the haploblock also are eQTLs in normal testes with RP11-469J4.3, a lncRNA of unknown function. RP11-469J4.3 is highly expressed in normal testes, but lies outside the haploblock (Supplementary Fig. 3l, Supplementary Fig. 4b, and Supplementary Fig. 5b). RP11-469J4.3 expression was not measured in the TCGA.

The SNP marker rs2713206 (OR = 1.26; P = 1.68 ×10−8) lies within a smaller LD region of only 48 kb on 2q14.2. The gene TFCP2L1 (transcription factor CP2-like 1) overlies the entirety of the haploblock (Table 1 and Supplementary Fig. 2d); SNPs in the region are eQTLs (Supplementary Table 3) with the risk allele associated with decreased expression of TFCP2L1 in TGCT (Supplementary Fig. 6b). TFCP2L1 is not expressed in normal adult testes (Supplementary Fig. 3m) but it is highly expressed in fetal gonocytes and in germ cell neoplasia in situ, the precursor of TGCT28,29. TFCP2L1 is upregulated in human primordial germ cells during embryogenesis at the time of epigenetic reprogramming30, but downregulated during transition from fetal gonocytes into spermatogonia29. The SNP marker rs6837349 (OR = 0.84; P = 3.13 ×10−8) localizes to an intron of ZFP42 (zinc finger protein 42) on 4q35.2, and marks a small 11 kb haploblock containing no other genes (Table 1 and Supplementary Fig. 2e). The region has no eQTLs in either normal testes or TGCT, although ZFP42 is expressed exclusively in normal testes (Supplementary Fig. 3n), specifically in human spermatogonia and TGCT29,31. Additionally, both ZFP42 and TFCP2L1 are involved in embryonal stem cell pluripotency30,32.

The SNP marker rs61408740 (OR= 1.65; P = 1.75 ×10−8) localizes to an intron of LHPP (phospholysine phosphohistidine inorganic pyrophosphate phosphatase) on 10q26.13 (Table 1 and Supplementary Fig. 2f). Only two SNPs were identified with pair-wise r2>0.4, one in the region of the second gene, FAM175B (Supplementary Table 3); neither are eQTLs. LHPP encodes an inorganic diphosphatase that functions in oxidative phosphorylation33.

The SNP marker rs11769858 (OR = 0.84; P = 2.38 ×10−8) identifies an 82 kb LD block on 7q36.3 that contains a large portion of NCAPG2 (non-SMC condensin II complex subunit G2) (Table 1 and Supplementary Fig. 2g). NCAPG2 encodes a regulatory subunit of the condensin II complex, which is highly expressed in testes (Supplementary Fig. 3q), and plays a role in chromosome assembly and segregation during mitosis34.

We also identified a locus marked by SNP rs17336718 (OR = 1.41; P = 3.84 ×10−8) on chromosome Xq28 (Table 1 and Supplementary Fig. 2h) in an intron of TKTL1 (transketolase-like 1), which is highly expressed in normal testes. The SNP is an eQTL for TKTL1 in the TGCT TCGA data (Supplementary Figure 6c). TKTL1 converts sedoheptulose to ribose and glyceraldehyde to xylulose, linking the pentose phosphate pathway to the glycolytic pathway. Interestingly, although overexpression of TKTL1 is associated with the Warburg effect and poor prognosis in several cancer types35–38, the risk allele is associated with lower expression.

At the previously-reported TGCT susceptibility locus DMRT1 on chromosome 9, we identified a third independent signal, rs55873183 (OR = 1.89; P = 2.18 ×10−23) (Table 1, Fig. 2a, and Supplementary Table 4a). This intronic SNP marker has an r2 of 0.03 and 0.06 with the two previously published SNP markers, rs7040024 and rs75538314,17, respectively; it retained genome-wide significance in conditional analysis (Table 1, Supplementary Table 4b, Supplementary Fig. 2i). We also identified three additional independent signals at 19p12: rs58521262, rs34601376 and rs73019876 (Table 1, Fig. 2b, Supplementary Tables 5a and 5b, and Supplementary Figs. 2j, 2k, 2l). We identified a SNP, rs2194275 (P = 9.23 ×10−12; OR = 0.76), in moderate LD (r2 = 0.7) with the previously published rs2195987 (P = 1.21 ×10−9; OR = 0.81), which was more statistically significant in this meta-analysis (Supplementary Table 5b). 19p12 contains a cluster of Krüppel-associated box zinc finger genes (KRAB-ZFPs)39. KRAB-ZFPs are highly and differentially expressed in germ cells, and important for the epigenetic reprogramming requisite for normal germ cell development30. A number of different GWA studies, including one for telomere length40, have identified significant SNPs in this region. The 19p12 LD blocks are large: rs58521262 marks a 219 kb block, and rs73019876 marks a 184 kb block, each containing over 200 relevant SNPs and several genes. The rs73019876 haploblock is extremely eQTL rich, with ZNF729 and ZNF676 both eQTLs in normal testes (Supplemental Figs. 4c and 4d). rs58521262 is an eQTL with ZNF728 and CTD-2291D10.2, which like ZNF729 (Supplemental Figs. 3t), are only expressed in normal testes (Supplemental Fig. 3v). Associated SNPs in the LD block also include two putative missense mutations in ZNF728 (Supplemental Table 3) not predicted to be deleterious. Given the multiple independent signals in this region, further study will be required to determine which, if any, are causally involved in TGCT.

Figure 2. Genetic association between SNP markers and TGCT risk for regions with multiple independent signals.

Figure 2

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The strength of the association signals (−log10 P-values) for individual SNPs at (a) 9p24.3 and (b) 19p12-11 are plotted on the Y-axis relative to their genomic locations (GRCh37) along the X-axis. Red diamonds are the newly identified independent SNPs, blue diamonds are previously reported SNP markers, and all other SNPs are colored gray. The line graph shows likelihood ratio statistics (right Y-axis) for recombination hotspots calculated with SequenceLDhot software using 1000 Genomes Project CEU population data. Gene annotation along the X-axis is based on NCBI RefSeq genes from the UCSC Genome Browser.

Similar to prior reports, several of the loci identified in the current study contain biologically plausible genes implicating pathways involved in male germ cell development and pluripotency (TFCP2L1, ZFP42), kinetochore function (ZWILCH), DNA damage response (TIPIN), and metabolic mitochondrial function (TKTL1 and LHPP). We identified eight such loci in novel genomic regions and four in previously identified regions. These additional loci bring the cumulative total of independent susceptibility alleles for TGCT to 40. Interestingly, racial differences in risk allele frequencies that parallel population-specific TGCT risk also continue to be apparent. Of the 40 identified susceptibility loci, the allele frequencies of all but one differ significantly across continents based on analysis of data from the AFR, AMR, ASN and EUR populations available in the 1000 Genomes project41, with most comparisons having a P-value surpassing strict Bonferroni correction (P < 0.00125) (Supplementary Table 6). The 12 newly identified susceptibility alleles account for 5.3% of the genetic risk to the brothers and 8.0% of risk to the sons of TGCT patients, increasing estimates of heritability to 25% and 37% of the risk to brothers and sons, respectively. The newly identified TGCT susceptibility markers continue to demonstrate moderate effects with ORs that range from 1.17 to 1.89. In comparison with other cancer types, we have accounted for a high proportion of site-specific heritability with fewer loci42–44.

ONLINE METHODS

Studies

Detailed characteristics and genotype quality control metrics of the study populations (Denmark, NCI [STEED, FTCS], Norway/Sweden, Penn, UK) have been previously described12,15,18,19,23. Subjects used in the current study are all of European descent, and data from each study were collected and analyzed in accordance with local ethical permissions and informed consent.

Genotype imputation

Genotype imputation was conducted by each center following a similar protocol. SNPs with a call rate < 95% or Hardy-Weinberg proportion test P-value < 0.000001 or MAF < 1% were removed prior to imputation. Imputation was conducted using IMPUTE2 software version 2.2.2 and version 3 of the 1,000 Genomes Project Phase 1 data as the reference set. First, the genomic coordinates were lifted over from NCBI human genome build 36 to build 37 using the UCSC lift over tool. Second, the strand of the inference data was aligned with the 1,000 Genomes data informed by allele state comparison or allele frequency matching for A/T and G/C SNPs. A pre-phasing strategy with SHAPEIT software version 1 was adopted to improve the imputation performance. The phased haplotypes from SHAPEIT were imported directly into the IMPUTE2 program. We applied sliding windows of 4Mb with 250kb as an overlapping buffer and generated 744 segments for imputing autosomes. For Chromosome X, the pseudoautosomal region 1 (PAR1), PAR2, and the remaining region, which was split into 37 sections, were imputed separately. We excluded imputed loci with INFO score < 0.3 or MAF < 0.01 from further association analysis. Further, we acknowledge the limitations of imputation, including that the accuracy of imputation depends on the linkage disequilibrium between markers in the reference panel and markers to be imputed, and that the quality of imputation across scans differs because of imperfect population matching of data sets to the reference panel.

Statistical analysis

Within each data set, a test for trend was performed for each SNP using SNPTEST software version 2.2 or 2.5. Fixed-effects meta-analysis was used to combine individual within-study association estimates from five imputed GWAS scans. Genetic effect heterogeneity across studies was assessed by using I2 and P-value calculated from the Cochran’s Q statistic. To refine the association signals of each risk region, we first performed LD pruning using pairwise R2 > 0.3 and then conducted the conditional association analyses to estimate the independent effect of each SNP by simultaneously including all specified SNPs from the same region and with their unconditional P-values < 5 × 10−8 into the same logistic regression model.

Heritability analysis

To evaluate the familial risk explained by the new loci identified in our study, we estimated the contribution of each SNP based on the formula h2SNP = β2 × 2f (1–f), where β is the log per allele odds ratio and f is the risk allele frequency45. We calculated the proportion of familial risk explained by dividing the summed contribution of all h2SNP by the total heritability, which was derived from the log relative risk (RR), where RR = 4 for affected father and RR = 8 for affected brothers46.

In silico bioinformatics analysis

We used HaploReg v4.1 and RegulomeDB v1.1 to explore potential non-coding functional annotation within the ENCODE database in the genomic region surrounding our SNPs of interest, with particular attention to annotations in induced pluripotent stem cells (IPSC) and embryonic stem cells (ESC), as we considered these tissue types as best proxies for TGCT. Specifically, we interrogated the linkage disequilibrium (LD) block of neighboring SNPs in a haploblock defined as pair-wise r2 > 0.4 with the index SNP (Supplementary Table 4). We also searched the GTEx v6 database to determine whether the haploblock SNPs were implicated as eQTLs in their sample of 157 normal adult testis tissues with available genotype. Of note, the normal testis contains an abundance of stromal cells (i.e., Sertoli and Leydig cells), so may not be an exact surrogate for germ cells, and in particular the primordial germ cells from which TGCT is believed to develop. Finally, we assessed our 12 novel susceptibility loci for eQTLs among the 128 cases of TGCT with linked genotype data available in The Cancer Genome Atlas.

For the correlation between genotype and expression data from the TGCT TCGA data set, the genotype data was downloaded from the NCI’s Genomic Data Commons (https://gdc.nci.nih.gov/). Data was converted to PLINK v1.0747 format. Subjects were screened for discordant sex, insufficient genotype call rate (>0.05), and excessive heterozygosity (> +/− 3 SD from the mean). SNPs were screened for MAF (>0.01), Hardy-Weinberg equilibrium violations (p<0.0001), and missingness (>0.01). All quality control steps were performed in PLINK. A total of five subjects were removed (all for heterozygosity violations), leaving 145 valid for analysis. 1000 Genomes Phase 341 was used as the reference set. Alignment to the reference set and haplotype estimation was performed using Shapeit v248, and additional SNPs were imputed using Impute249. Imputed SNPs with an info score <0.4 were discarded. For the 12 SNPs of interest, the risk allele was calculated as the allele with increased odds of TGCT (OR>1). For each subject, the zygosity with respect to the risk allele was calculated, and genotypes were tabulated.

All available TCGA TGCT data were retreived from the TCGA Data Coordinating Center and processed through the TCGA pipeline at the TCGA Genome Data Analysis Center at the Institute for Systems Biology. Gene expression matrices were generated for 133 primary tumor samples using available (TCGA Level 3) gene expression values from RNA sequencing, expressed as RSEM values50. Imputed genotypes for all novel SNPs reported in this paper were related to gene expression, for the 128 cases with both genotype and gene expression levels available. Assocations were tested using a linear regression model (using the lm function in R).

Technical validation of imputed SNPs

To technically validate our imputation findings, we optimized TaqMan assays (Applied Biosystems) for 12 loci based on the standard pipeline at the Cancer Genomics Research Laboratory at National Cancer Institute (Supplementary Table 7). For six loci that failed initial TaqMan assay design, LD surrogate SNPs were used. We randomly selected about 1000 samples previously scanned in one of three GWAS (~300 each from NCI, Penn and Norway/Sweden) for TaqMan genotyping. For the imputed probabilistic genotypes, a threshold of 0.80 was applied to derive the discrete genotypes. The average concordance rates are 0.98, 0.97 and 0.93 for NCI, Penn and Norway/Sweden respectively (Supplementary Table 7).

Supplementary Material

1

Acknowledgments

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services nor does the mention of trade names, commercial products or organization indicate endorsement by the U.S. Government. The authors thank Ms. Benita Weathers for her coordination of TECAC, Mr. John Pluta for biostatistical assistance, and Mr. Kurt D’Andrea for expert assistance with SNP genotyping. We thank Drs. Douglas R. Stewart and Jennifer T. Loud for critical support of the NCI Clinical Genetics Branch Familial Testicular Cancer Project (NCI 02-C-0178; NCT-00039598). The authors also thank all previous contributors to the GWAS analyzed in this study, including Drs. Nils Weinhold, Daniel Edsgärd, Henrik Leffers, and Profs. Anders Juul, Niels E. Skakkebæk, and Søren Brunak from the Danish study.

The Testicular Cancer Consortium is supported by the National Institutes of Health grant U01CA164947 to KLN, PAK and SMS. A portion of this work was supported by the Intramural Research Program of the National Cancer Institute and by a support services contract HHSN26120130003C with IMS, Inc. The Penn GWAS (Penn) was supported by the Abramson Cancer Center at the University of Pennsylvania and National Institute of Health grant CA114478 to KLN and PAK. The UK testicular cancer study was supported by the Institute of Cancer Research, Cancer Research UK and made use of control data generated by the Wellcome Trust Case Control Consortium (WTCCC) 2. CT is supported by the Movember foundation. KL is supported by a PhD fellowship from Cancer Research UK. LCP is supported by T32-GM008638. The contribution from the University of Leeds was funded by Cancer Research UK. Norwegian/Swedish study was supported by the Norwegian Cancer Society (grants number 418975 – 71081 – PR-2006-0387 and PK01-2007-0375); the Nordic Cancer Union (grant number S-12/07) and the Swedish Cancer Society (grant numbers 2008/708, 2010/808, 2011/484, and CAN2012/823). The Danish GWAS was supported by Villum Kann Rasmussen Foundation, a NABIIT grant from the Danish Strategic Research Council, the Novo Nordisk Foundation, the Danish Cancer Society, and the Danish Childhood Cancer Foundation.

Testicular Cancer Consortium

  • Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA

    Katherine McGlynn, Mark Greene, Stephen Chanock

  • Department of Growth and Reproduction, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark

    Ewa Rajpert-De Meyts

  • Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK

    D. Timothy Bishop, Jeremie Nsengimana

  • Center of Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark

    Ramneek Gupta

  • Cancer Registry of Norway, Oslo, Norway

    Tom Grotmol

  • Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Oslo, Norway

    Trine B. Haugen

  • Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

    Fredrik Wiklund, Robert Karlsson

  • Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK

    Clare Turnbull

  • Department of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Katherine L. Nathanson

  • Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

    Stephen M. Schwartz

  • Genomics England, London, UK

    Clare Turnbull

  • Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA

    Peter A. Kanetsky

  • Abramson Cancer Center, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Katherine L. Nathanson

  • Department of Medicine, Unit of Andrology and Reproductive Medicine, University of Padova, Padova, Italy

    Alberto Ferlin

  • Medical Oncology, University Medical Center Groningen, Groningen, The Netherlands

    Jourik A. Gietema

  • Department of Clinical Preventive Medicine, Krek School of Medicine at University of Southern California, Los Angeles, CA

    Victoria Cortessis

  • Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA

    Russ Hauser

  • Department of Epidemiology, Division of OVP, Cancer Prevention and Population Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX

    Michelle Hildebrandt

  • Radboud University Medical Centre, Radhoud Institute for Health Sciences, Nijmegen, Nijmegen, The Netherlands

    Lambertus A. Kiemeney

  • Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Davor Lessel, Christian Kubisch

  • deCode Genetics, Reykjavik, Iceland

    Thorunn Rafnar

  • Department of Biostatistics and Epidemiology, University of Turin, Turin, Italy

    Lorenzo Richiardi

  • Department of Molecular Oncology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway

    Rolf Skotheim

  • Department of Epidemiology, Brown University, Providence, RI

    Tongzhang Zheng

Footnotes

Conflict of Interest

There are no conflicts of interest.

Author Contributions

K.L.N. and P.A.K. supervised the overall study. K.A.M., E.R-D.M, D.T.B., M.D.D., M.H.G., R.G., T.G., T.B.H., K.L., K.N., S.M.S., F.W., C.T., P.A.K, and K.L.N. contributed to recruitment, study and data management. Z.W., K.A.M., E.R-D.M, D.T.B., M.D.D., M.H.G., R.G., T.G., T.B.H., R.K., K.L., N.M., K.N., S.V., F.W., C.T., S.J.C., P.A.K, and K.L.N. contributed to genotyping or association analysis of individual studies. Z.W., C.C.C., L.C.P., V.T., S.J.C, P.A.K. and K.L.N. carried out the meta-analysis and the additional bioinformatics analyses, including using GTeX and TCGA TGCT data. Z.W., P.A.K. and K.L.N. drafted the initial manuscript, and all authors reviewed and contributed to the manuscript.

Data Availability

Individual level data from the UK GWAS data has been deposited into European Genome-phenome Archive (EGA) accession number EGAS00001001302. Individual level data has been deposited into dbGAP from University of Pennsylvania (phs001307.v1.p1) and NCI (phs001303.v1.p1). The summary data from the TECAC meta-analysis also has been deposited into dbGAP (phs001349.v1.p1).

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Supplementary Materials

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