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Human Genetics

, Volume 132, Issue 1, pp 5–14 | Cite as

HOXB13 is a susceptibility gene for prostate cancer: results from the International Consortium for Prostate Cancer Genetics (ICPCG)

  • Jianfeng Xu
  • Ethan M. Lange
  • Lingyi Lu
  • Siqun L. Zheng
  • Zhong Wang
  • Stephen N. Thibodeau
  • Lisa A. Cannon-Albright
  • Craig C. Teerlink
  • Nicola J. Camp
  • Anna M. Johnson
  • Kimberly A. Zuhlke
  • Janet L. Stanford
  • Elaine A. Ostrander
  • Kathleen E. Wiley
  • Sarah D. Isaacs
  • Patrick C. Walsh
  • Christiane Maier
  • Manuel Luedeke
  • Walther Vogel
  • Johanna Schleutker
  • Tiina Wahlfors
  • Teuvo Tammela
  • Daniel Schaid
  • Shannon K. McDonnell
  • Melissa S. DeRycke
  • Geraldine Cancel-Tassin
  • Olivier Cussenot
  • Fredrik Wiklund
  • Henrik Grönberg
  • Ros Eeles
  • Doug Easton
  • Zsofia Kote-Jarai
  • Alice S. Whittemore
  • Chih-Lin Hsieh
  • Graham G. Giles
  • John L. Hopper
  • Gianluca Severi
  • William J. Catalona
  • Diptasri Mandal
  • Elisa Ledet
  • William D. Foulkes
  • Nancy Hamel
  • Lovise Mahle
  • Pal Moller
  • Isaac Powell
  • Joan E. Bailey-Wilson
  • John D. Carpten
  • Daniela Seminara
  • Kathleen A. Cooney
  • William B. Isaacs
  • International Consortium for Prostate Cancer Genetics
Open Access
Original Investigation

Abstract

Prostate cancer has a strong familial component but uncovering the molecular basis for inherited susceptibility for this disease has been challenging. Recently, a rare, recurrent mutation (G84E) in HOXB13 was reported to be associated with prostate cancer risk. Confirmation and characterization of this finding is necessary to potentially translate this information to the clinic. To examine this finding in a large international sample of prostate cancer families, we genotyped this mutation and 14 other SNPs in or flanking HOXB13 in 2,443 prostate cancer families recruited by the International Consortium for Prostate Cancer Genetics (ICPCG). At least one mutation carrier was found in 112 prostate cancer families (4.6 %), all of European descent. Within carrier families, the G84E mutation was more common in men with a diagnosis of prostate cancer (194 of 382, 51 %) than those without (42 of 137, 30 %), P = 9.9 × 10−8 [odds ratio 4.42 (95 % confidence interval 2.56–7.64)]. A family-based association test found G84E to be significantly over-transmitted from parents to affected offspring (P = 6.5 × 10−6). Analysis of markers flanking the G84E mutation indicates that it resides in the same haplotype in 95 % of carriers, consistent with a founder effect. Clinical characteristics of cancers in mutation carriers included features of high-risk disease. These findings demonstrate that the HOXB13 G84E mutation is present in ~5 % of prostate cancer families, predominantly of European descent, and confirm its association with prostate cancer risk. While future studies are needed to more fully define the clinical utility of this observation, this allele and others like it could form the basis for early, targeted screening of men at elevated risk for this common, clinically heterogeneous cancer.

Keywords

Prostate Cancer Prostate Cancer Patient Prostate Cancer Risk European Descent Johns Hopkins Hospital 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Introduction

By sequencing coding regions of more than 200 genes in a previously identified region of linkage at 17q21–22 (Lange et al. 2003; Gillanders et al. 2004; Xu et al. 2005; Lange et al. 2007; Cropp et al. 2011) a rare but recurrent mutation (G84E) in HOXB13 was recently identified in four of 94 probands from prostate cancer families. (Ewing et al. 2012) The mutation co-segregated with prostate cancer in these four families and was found to be significantly more common among 5,083 unrelated prostate cancer patients (1.4 %) than control subjects (0.1 %) of European descent (p = 8.5 × 10−7) leading to odds ratio (OR) estimates of tenfold or more. In this initial report, the frequency of the mutation was higher in prostate cancer patients with early-onset disease (age at diagnosis ≤55 years old, 2.2 %) or with a positive family history (2.2 %), and most common in patients with both of these features (3.1 %). If confirmed, these findings provide support for the concept that rare, moderately penetrant mutations as well as common, low-penetrance prostate cancer risk-associated variants identified from genome-wide association studies (GWAS) (Gudmundsson et al. 2007a, b, 2008, 2009; Yeager et al. 2007, 2009; Thomas et al. 2008; Eeles et al. 2008, 2009; Sun et al. 2008; Xu et al. 2010; Kote-Jarai et al. 2011a; Takata et al. 2010; Akamatsu et al. 2012; Haiman et al. 2011) both contribute to prostate cancer risk. The identification and characterization of genetic variants reproducibly associated with substantial increases in prostate cancer risk would provide enhanced ability to identify men most likely to benefit from early disease screening.

Prostate cancer demonstrates wide differences in incidence and mortality across populations within the United States and throughout the world. In an attempt to confirm and expand the observations of Ewing et al. (2012), we examined the frequency of HOXB13 G84E mutations in prostate cancer families across different ancestries and geographic regions. We genotyped this mutation and other known variants in HOXB13 in 2,443 hereditary prostate cancer families recruited by members of the International Consortium for Prostate Cancer Genetics (ICPCG), a large NCI-funded collaborative resource for studies of genetic susceptibility for hereditary prostate cancer.

Subjects and methods

Study population

The ICPCG study cohort has been described in detail previously (Schaid and Chang 2005; Xu et al. 2005). Fifteen groups participated in the present study, including those from Europe [Finland (Tampere University), Sweden (Karolinska Institute), UK (Institute of Cancer Research and Royal Marsden NHS Foundation Trust, University of Cambridge, ACTANE), Germany (University of Ulm), and France (CeRePP)], North America (Fred Hutchinson Cancer Research Center, Johns Hopkins Hospital, Louisiana State University, Mayo Clinic, McGill University, Northwestern University, Stanford University, University of Michigan, and University of Utah), and Australia (University of Melbourne) (Supplementary Table 1).

Each ICPCG group recruited its study population via different methods of pedigree ascertainment and utilized different methods to confirm prostate cancer diagnosis. In this study, men were considered “affected” if their prostate cancer diagnosis was confirmed by either medical records or death certificates. All other men were assigned as “unknown phenotype.” A total of 2,443 families were included in the study, including 6,422 affected men and 1,902 men without a prostate cancer diagnosis (unknown), and 1,803 women whose DNA samples were available (Supplementary Table 1). Research protocols and study documentation were approved by each group’s Institutional Review Board.

SNPs selection and genotyping

Five mutations in the HOXB13 gene, selected from the original paper of Ewing et al. (2012) and the ESP database (Exome Variant Server, NHLBI Exome Sequencing Project, Seattle, WA, USA (URL: http://evs.gs.washington.edu/EVS/) [1/2012]) were genotyped in the ICPCG dataset, including G84E (c.251G > A, rs138213197), T105I (c.314C > T, rs140492479), R217C (c.649C > T, rs13945791), R229G (c.685C > G), and T253P (c.757A > C). In addition, ten polymorphic SNPs (rs890435, rs2326017, rs7212669, rs8064938, rs3809773, rs1054072, rs8556, rs3809771, rs4793980, rs3110601) flanking the HOXB13 gene and spanning 108,191 base pairs (bp) from 46,719,399 to 46,827,590 (Build 37) were genotyped to estimate allele frequencies and haplotypes. The G84E mutation, due to a change in the second position of codon 84 (GGA → GAA), results in a nonconservative substitution in a conserved putative protein–protein binding motif of HOXB13 (Ewing et al. 2012).

Genotyping was performed using the MassARRAY iPLEX (Sequenom, Inc., San Diego, CA, USA). Duplicates and negative controls were included in each 96-well plate to ensure quality control (QC). Genotyping was performed by technicians blinded to the sample status. The average concordance rate was 99.7 % for 6,300 genotypes among QC duplicates.

Statistical methods

Frequency of the G84E mutation was determined at either family level or individual level. At a family level, the proportion of families with at least one G84E mutation carrier was determined for the entire set as well as for each ICPCG group. The difference in the proportion among different ICPCG groups was tested using Chi-square with a degree of freedom (df) of 14. At an individual level, the proportion of G84E mutation carriers was compared among men with a diagnosis of prostate cancer (affecteds) and the remaining men within the families (unknowns). The difference of G84E mutation carrier rate between affected and unknown men was tested based on a marginal model that accounts for relatedness of subjects within families using generalized estimating equations (GEE). An exchangeable working correlation matrix was assumed.

A family-based association test was performed to test association of the G84E mutation and other SNPs with prostate cancer by assessing over-transmission of alleles from parents to affected offspring using the computer program FBAT (Xu et al. 2002). Empirical variance test statistics were used to account for the correlation of transmitted alleles among multiple affected individuals in the same family.

Haplotypes of each individual based on these 15 SNPs were estimated using Genehunter-plus (Kruglyak et al. 1996) and PLINK (Purcell et al. 2007). The haplotypes with the highest likelihood were selected. For subjects whose inferred haplotypes were different based on these two methods, manual inspection was performed to resolve the difference, with priority given to haplotypes based on linkage disequilibrium among markers in this study population.

Results

Among five previously observed mutations in HOXB13 (Ewing et al. 2012) two were observed in this study—R217C (rs13945791) and G84E (rs138213197). The rare R217C variant was found one time each in two families of European descent and did not co-segregate with prostate cancer. The G84E mutation was found in 283 subjects from 112 families of European descent, including 194 men with prostate cancer (Table 1). This represented 4.6 % of all 2,443 prostate cancer families and 4.8 % of 2,298 prostate cancer families of European descent. The proportion of families with at least one G84E mutation carrier differed significantly across the 15 ICPCG groups (P = 9.4 × 10−8). The proportion was highest in families from the Nordic countries of Finland (22.4 %) and Sweden (8.2 %) and lower in North America (0–6.1 %) and Australia (2.6 %). The G84E mutation was not found in families of any other race or ethnicity, including those of African (N = 58), Ashkenazi Jewish (N = 46), or other descent (N = 28). Obviously, larger numbers of families of these and other races and ethnicities will need to be examined to more fully characterize the population distribution of this mutation.
Table 1

G84E mutation of HOXB13 in prostate cancer families of International Consortium for Prostate Cancer Genetics (ICPCG)

 

No. of families

No. of families with G84E carriers (%)

Subjects in families with at least one G84E carrier

Affected

Unknown (Men)

Unknown (Women)

All

European descent

All

European descent

N

No. of G84E carriers (%)

N

No. of G84E carriers (%)

N

N of G84E carriers (%)

Europe

 Finland, University of Tampere

76

76

17 (22.4 %)

17 (22.4 %)

54

37 (69 %)

69

22 (31 %)

97

29 (30 %)

 Sweden, Umea University

110

110

9 (8.2 %)

9 (8.2 %)

17

13 (76 %)

15

5 (33 %)

13

4 (31 %)

 Germany, University of Ulm

378

378

13 (3.4 %)

13 (3.4 %)

21

19 (90 %)

1

0 (0 %)

2

0 (0 %)

 UK, ACTANE

145

142

5 (3.4 %)

5 (3.5 %)

12

7 (58 %)

1

0 (0 %)

1

0 (0 %)

 France, CeRePP

159

156

2 (1.3 %)

2 (1.3 %)

5

3 (60 %)

1

0 (0 %)

0

0

North America

 BC/CA/HI

98

83

6 (6.1 %)

6 (7.2 %)

20

12 (60 %)

7

1 (14 %)

7

1 (14 %)

 Fred Hutchinson Cancer Research Center

255

241

14 (5.5 %)

14 (5.8 %)

45

25 (56 %)

14

5 (36 %)

16

2 (13 %)

 Johns Hopkins Hospitala

234

176

5 (2.1 %)

5 (2.8 %)

20

14 (70 %)

7

2 (29 %)

10

4 (40 %)

 MAYO Clinic

185

185

6 (3.2 %)

6 (3.2 %)

15

10 (67 %)

2

0 (0 %)

0

0

 University of Michigana

317

282

11 (3.5 %)

11 (3.9 %)

36

26 (72 %)

13

4 (31 %)

5

2 (40 %)

 McGill University

18

17

1 (5.9 %)

1 (5.9 %)

2

2 (100 %)

0

0

0

0

 North Western University

33

32

0 (0 %)

0 (0 %)

0

0

0

0

0

0

 University of Utah

348

348

21 (6 %)

21 (6 %)

132

23 (17 %)

6

2 (33 %)

11

3 (27 %)

 Louisiana State University

10

10

0 (0 %)

0 (0 %)

0

0

0

0

0

0

Australia

 Australia

77

73

2 (2.6 %)

2 (2.7 %)

3

3 (100 %)

1

1 (100 %)

3

2 (67 %)

Total

2,443

2,309

112 (4.6 %)

112 (4.9 %)

382

194 (51 %)

137

42 (31 %)

165

47 (28 %)

Totala

1,892

1,851

96 (5.0 %)

96 (5.2 %)

326

154 (47 %)

117

36 (31 %)

150

41 (27 %)

aA subset of families from these centers were included in the original discovery report (Ewing et al. 2012). These total values reflect the results obtained after omitting all families from these two centers

In the 112 families with at least one G84E mutation carrier, the mutation was found in both affected and unaffected men. However, the carrier rate was significantly higher in affected men (194 of 382, 51 %) than other men in these families (i.e. men of unknown status [(42 of 137, 31 %), p = 9.9 × 10−8]) (Table 1). Using a statistical test that considered the relatedness of subjects within carrier families, the odds ratio (OR) for prostate cancer was 4.42 [95 % confidence interval (CI) 2.56–7.64] for the G84E mutation carriers. We repeated our analyses excluding families from the University of Michigan and Johns Hopkins Hospital, some of which were included in the initial report describing HOXB13 as a prostate cancer susceptibility gene (Ewing et al. 2012). In particular, the former study included HOXB13 G84E genotype data from only the youngest prostate cancer case in a subset of University of Michigan and Johns Hopkins Hospital families. The carrier rate in ICPCG families remained significantly higher in affected men (154 of 326, 47 %) than unknown men [(36 of 117, 31 %), P = 3.3 × 10−6] and the OR for prostate cancer was 4.3 [95 % confidence interval (CI) 2.32–7.96] for the G84E mutation carriers after excluding all families from these two institutions (Table 1).

A mixed pattern of co-segregation of the G84E mutation with prostate cancer was found in these 112 families. While complete co-segregation was found in 34 families, incomplete co-segregation was more commonly observed, revealing genetic heterogeneity (affected but not carriers) and incomplete penetrance of the mutation (carriers but unaffected men).

We also examined transmission of G84E mutation and alleles of other genotyped SNPs at the region in all 2,443 families using a family-based association test (Table 2). The risk allele (A) corresponding to the G84E mutation was observed to be transmitted significantly more often than expected from parents to affected sons (P = 6.5 × 10−6). A significant result was also observed when all families from the University of Michigan and Johns Hopkins Hospital were removed from this analysis (P = 1.2 × 10−4) (Supplementary Table 2), strongly indicating the G84E mutation is associated with prostate cancer risk.
Table 2

Family-based association test for SNPs at HOXB13 region in ICPCG families

Chr

Position

rs#

Gene

Mutation

Rare allele

Allele frequency

No. of informative families

S-E(S)a

Var(S)

Z

P

17

46,719,399

rs890435

Intergenic

 

G

0.41

509

−7.38

243.77

−0.47

0.64

17

46,720,565

rs2326017

Intergenic

 

T

0.33

496

3.10

248.24

0.20

0.84

17

46,727,289

rs7212669

Intergenic

 

G

0.10

244

−4.89

107.42

−0.47

0.64

17

46,780,829

rs8064938

Intergenic

 

A

0.16

353

−6.12

136.42

−0.52

0.60

17

46,784,039

rs3809773

Intergenic

 

A

0.33

485

1.42

245.54

0.10

0.93

17

46,799,812

rs1054072

PRAC

 

C

0.47

518

−13.41

268.62

−0.82

0.41

17

46.804,250

 

HOXB13

T253P

 

0

0

N/A

N/A

N/A

N/A

17

46,804,322

 

HOXB13

R229G

G

0.0001

1

−0.40

0.16

−1.00

0.32

17

46,804,358

rs139475791

HOXB13

R217C

A

0.0001

2

−1.60

1.36

−1.37

0.17

17

46,805,590

rs8556

HOXB13

 

T

0.15

342

−10.77

145.60

−0.89

0.37

17

46,805,642

rs140492479

HOXB13

T105I

A

0.0001

2

1.64

1.41

1.38

0.17

17

46,805,705

rs138213197

HOXB13

G84E

A

0.02

38

17.50

15.07

4.51

6.53E−06

17

46,807,919

rs3809771

5′

 

G

0.06

171

−8.92

64.24

−1.11

0.27

17

46,813,531

rs4793980

5′

 

T

0.16

306

2.22

116.03

0.21

0.84

17

46,827,590

rs3110601

5′

 

C

0.12

274

−7.46

114.18

−0.70

0.49

Based on an FBAT analysis of 2,437 pedigrees (10,217 nuclear families; 40,246 subjects)

aS-E(S) is the statistical score for the observed number of rare allele transmissions minus the statistical score for the expected number of transmissions

To assess association in our family set while adjusting for variable pedigree structures, we randomly selected one affected man (proband) in the second generation from each of 2,443 pedigrees and then counted the number of G84E carriers among probands, first-relatives, and second-degree relatives or higher (Table  3). The G84E mutation carrier rate among probands was 2.8 %. Among the first-degree relatives, the carrier rate was significantly higher in affected men (75 %) than in those with an unknown phenotype (48 %), P = 0.002, OR = 4.26 (95 % CI 1.69–10.75). Among the second-degree relatives or higher, the carrier rate was also significantly higher in affected men (58 %) than in unknown men (23 %), P = 0.004, OR = 4.81 (95 % CI 1.64–14.12).
Table 3

G84E HOXB13 mutation carriers among randomly selected affected probands and their relatives

Proband G84E Carrier

G84E carriers in first-degree relatives

G84E carriers in second-degree relatives or higher

Affected

Unknown

OR (95 % CI)

P value

Affected

Unknown

OR (95 % CI)

P value

Yes (51)

56/75 (74.7 %)

16/34 (47.6 %)

4.26 (1.69–10.75)

0.002

11/19 (57.9 %)

9/39 (23.1 %)

4.81 (1.64–14.12)

0.004

No (1,755)

21/2,502 (0.8 %)

3/759 (0.4 %)

2.31 (0.82–6.51)

0.11

15/973 (1.5 %)

6/651 (0.9 %)

2.21 (0.39–12.71)

0.37

The prostate cancer patients who carried the mutation had a wide spectrum of clinical disease, including cancers with high risk of disease progression (Table 4), as indicated by moderate to poor tumor differentiation (tumor grade of Gleason score 7 or higher) in over one-third of the cases with available data, and over one-quarter having non-organ confined disease at diagnosis (tumor stage T3 or higher). The mean age at diagnosis of carriers was 62.8 years. In comparison, the mean age at diagnosis for the 6,172 prostate cancer patients without the mutation was 64.4 years (P = 0.04; relatedness of subjects within families was considered). The mean age at last contact of G84E carriers without a prostate cancer diagnosis was 56.3.
Table 4

Clinicopathologic variables of prostate cancers in HOXB13 G84E carriers

 

No. of patients

 % of patients

Tumor grade (Gleason Score)

 ≤6

67

63.2 

 7

32

30.2 

 8

4

3.8 

 ≥9

3

2.8 

Tumor stage

 T1c or lower

47

39.2 

 T2

41

34.2 

 T3 or higher

32

26.7 

Metastasis at diagnosis

 Yes

4

3.1 

Serum PSA level at diagnosis

 ≤10

49

48.0 

 11–20

25

24.5 

 ≥20

28

27.5 

Age at diagnosis

 ≤55

24

18.6 

 56–80

105

81.4 

 ≥80

0

0.0 

Death from prostate cancer

 Yes

9

7.0 

Finally, to assess a potential founder effect for the G84E mutation, we estimated haplotypes based on the 15 genotyped SNPs in this region. The mutation (allele A) of G84E was predicted to be on eight different haplotypes. However, 95 % (269 out of 283) of the occurrences were predicted to be on a single rare haplotype (frequency of 2 %). Among the 269 G84E mutation carriers predicted to carry the common haplotype, 83 were from Finland while the remaining were from 12 other ICPCG groups. One individual from Finland was homozygous for all 15 markers, allowing unambiguous assignment of the haplotype. This individual was diagnosed with moderately differentiated (Gleason 7), clinically localized prostate cancer at age 60.

We note that the genotype data for all 269 G84E mutation carriers were consistent with a single shared haplotype spanning the 15 genotyped SNPs (i.e. there were no SNPs that had homozygous genotypes for opposite alleles among the 269 carriers) and it is possible that with additional genotype data the most likely haplotype configuration for G84E carriers would be a single founder haplotype.

Discussion

By evaluating germline mutations of the HOXB13 gene in 2,433 prostate cancer families from the ICPCG, this study confirmed the observation that the G84E mutation is significantly associated with prostate cancer in subjects of European descent with family history of the disease. The results remained significant when families used in the original report were not included in the analysis, providing independent confirmation of the original finding. Although there is a large degree of variability in the number of individuals sampled per pedigree in the ICPCG, approximately 5 % of prostate cancer families had at least one member with the G84E mutation. These results are consistent with the hypothesis that HOXB13 G84E is a prostate cancer susceptibility allele that significantly increases the risk of prostate cancer.

The search for hereditary prostate cancer genes has been challenging due to a number of factors including the late-onset nature of the disease and the high background rate of sporadic disease in the general population. Although rare variants of other genes such as RNASEL (Carpten et al. 2002), MSR1 (Xu et al. 2002), and ELAC2 (Tavtigian et al. 2001) have been previously identified in prostate cancer families and proposed as prostate cancer susceptibility alleles, follow-up studies have not supported their candidacy. On the other hand, mutations in BRCA2 have been reproducibly associated with prostate cancer risk (Edwards et al. 2003), but their frequency is low in prostate cancer families (Agalliu et al. 2007; Kote-Jarai et al. 2011b).

More recently, GWAS studies have led to the identification of over 40 prostate cancer risk-associated SNPs that have been replicated in multiple study populations. These variants are common in the general population (5 % or higher), confer low risk with ORs, typically in the range of 1.1–1.4 (Gudmundsson et al. 2007a, b, 2008, 2009; Yeager et al. 2007, 2009; Thomas et al. 2008; Eeles et al. 2008, 2009; Sun et al. 2008; Xu et al. 2010; Kote-Jarai et al. 2011a; Takata et al. 2010; Akamatsu et al. 2012; Haiman et al. 2011), and have been estimated to account for ~25 % of the risk associated with a positive family history (Kote-Jarai et al. 2011a). Although more common prostate cancer risk-associated variants are likely to be identified in the future, rare variants with larger effects have been proposed as an alternative mechanism to account for ‘missing inheritance’ (Iyengar and Elston 2007; Bodmer and Bonilla 2008). In this respect, the establishment of a rare and moderate- to high-penetrance mutation in HOXB13 as a prostate cancer susceptibility allele provides empirical evidence for this alternative hypothesis. Indeed, like colorectal and breast cancer, at least some significant fraction of prostate cancer risk is conferred by this class of coding sequence variants.

The estimated frequency of the HOXB13 G84E mutation in prostate cancer families is influenced by the number of individuals in any given family as well as family structure. For example, some extended families, particularly in the Utah collection, have more than 100 subjects and have multiple affected generations. Similarly, estimated ORs for G84E in relation to prostate cancer risk are impacted by the mixed degrees of relatedness among relatives, as the covariance matrices used in the GEE models do not explicitly account for family structure. The analysis presented in Table 3 was designed to provide better odds ratio estimates for first- and second-degree relatives of G84E carriers. Of interest, the carrier rate was lower among second-degree affected relatives (58 %) compared with first-degree affected relatives (75 %), suggesting the presence of genetic heterogeneity across families. The OR estimates from our analyses should be interpreted only in the context of the current study. We note that the odds ratios are calculated based on many “controls” that have limited phenotype information; most have not been screened for disease or screening results are missing. Further, familial controls not currently affected by prostate cancer are more likely to develop disease in the future compared with randomly selected men from the general population given the strong history of disease in these families. Finally, our familial cases are more likely to carry moderate to high penetrance risk alleles compared with typical unselected prostate cancer cases. Large population-based studies that include screened men will be necessary to obtain more accurate measures of G84E mutation frequency and penetrance. As we observed, the frequency of G84E mutations are likely population specific.

Our results implicate a geographical frequency gradient of the G84E mutation across the European continent, with the mutation being more common in Nordic countries, notably Finland. This finding highlights the strength of the current study as family-based association methods provide the strongest protection against type I error due to population stratification. It remains to be seen how various analytic methods (e.g. those based on principal components that capture the major sources of genetic variation between subjects across common genetic variants) will protect against population stratification when analyzing uncommon genetic variants that disproportionately occur in specific European-derived populations in case–control settings.

In summary, analysis of the large ICPCG family collection establishes the HOXB13 G84E allele as a reproducible risk factor for prostate cancer. Our identification of a common haplotype among the majority of HOXB13 G84E carriers indicates that there is a founder effect with a higher frequency of the mutant allele in Nordic populations. Additional studies using population-based case–control and/or familial samples will be useful to define the penetrance of this mutation, which will have important clinical implications for families that carry the G84E mutation.

Notes

Acknowledgments

We would like to express our gratitude to the many families who participated in the studies involved in the International Consortium for Prostate Cancer Genetics (ICPCG). The ICPCG is funded by a grant from the National Institutes of Health U01 CA89600 (to W.B.I.). Additional support to members within the ICPCG is as follows: University of Michigan Group acknowledges NIH grants R01 CA79596, R01 CA079596-10-S1 (ARRA), R01 CA136621, and P50 CA69568. University of Utah Group: The authors thank the support from the University of Utah Huntsman Cancer Institute (to Lisa A. Cannon-Albright). FHCRC/NHGRI Group: Partial support was provided by the Fred Hutchinson Cancer Research Center (to Janet L. Stanford) and National Human Genome Research Institute (to Elaine A. Ostrander). ACTANE Group: We acknowledge support from CR-UK grant C5047/A7357 and the NIHR to the Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden NHS Foundation Trust and Prostate Action (to Ros Eeles), and Cancer Research UK (to Doug Easton). This work was also supported by the European Commission’s Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), University of Umeå Group: Partial support was provided by Swedish Cancer Society and a Spear grant from the Umeå University Hospital, Umeå, Sweden (to Henrik Grönberg). University of Tampere Group: Partial support was provided from The Competitive Research Funding of the Pirkanmaa Hospital District (9M094), Finnish Cancer Organisations, Sigrid Juselius Foundation and Academy of Finland (116437 and 251074) (to Johanna Schleutker). Australian Group: Recruitment was funded by Cancer Council Victoria, Tattersalls and The Whitten Foundation; JLH is an Australia Fellow of the National Health and Medical Research Council. Northwestern University Group: Partial support was provided from Robert H Lurie Comprehensive Cancer Center and the Urological Research Foundation (to William J. Catalona). LSUHSC-NO Group: Louisiana Board of Regents, Centers for Disease Control and Prevention. Data Coordinating Center: Partial support was provided by NCI CA119069 and CA129684 (to Jianfeng Xu). We also thank other investigators who contributed to this work: ACTANE Group: Daniel Leongamornlert, Ed Saunders, Malgorzata Tymrakiewicz, Lynne O’Brien, Emma Sawyer, Rosemary Wilkinson, and Stephen Edwards from The Institute of Cancer Research, Sutton, Surrey; Jacques Simard, from the Human Molecular Endocrinology Research Center, CHUL Research Center, Laval University, Quebec City, Canada; Timothy Bishop from Cancer Research UK, Genetic Epidemiology Laboratory, St James’ University Hospital, Leeds, UK; Michael Badzioch; Tokhir Dadaev, Lesley McGuffog, Koveela Govindasami, and Michelle Guy from the UKGPCS Team. University of Ulm Group: Antje Rinckleb and Mark Schrader from Department of Urology, University of Ulm, Germany; Josef Hoegel and Christian Kubisch from Institute of Human Genetics, University of Ulm, Germany; and Kathleen Herkommer from Department of Urology, Technical University of Munich, Germany. Fred Hutchinson Cancer Research Center Group: Laura McIntosh. We thank Liesel FitzGerald for helpful comments and review. William Foulkes thanks Celia Greenwood for advice.

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Supplementary material

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Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Jianfeng Xu
    • 1
    • 2
  • Ethan M. Lange
    • 3
    • 4
  • Lingyi Lu
    • 1
    • 2
  • Siqun L. Zheng
    • 1
    • 2
  • Zhong Wang
    • 1
    • 2
  • Stephen N. Thibodeau
    • 5
    • 6
  • Lisa A. Cannon-Albright
    • 7
    • 8
  • Craig C. Teerlink
    • 7
    • 8
  • Nicola J. Camp
    • 7
    • 8
  • Anna M. Johnson
    • 3
    • 9
  • Kimberly A. Zuhlke
    • 3
    • 9
  • Janet L. Stanford
    • 10
    • 11
  • Elaine A. Ostrander
    • 10
    • 12
  • Kathleen E. Wiley
    • 13
    • 14
  • Sarah D. Isaacs
    • 13
    • 14
  • Patrick C. Walsh
    • 13
    • 14
  • Christiane Maier
    • 15
    • 16
  • Manuel Luedeke
    • 15
    • 16
  • Walther Vogel
    • 15
    • 17
  • Johanna Schleutker
    • 18
    • 19
    • 20
  • Tiina Wahlfors
    • 18
    • 19
  • Teuvo Tammela
    • 18
    • 21
  • Daniel Schaid
    • 5
    • 22
  • Shannon K. McDonnell
    • 5
    • 22
  • Melissa S. DeRycke
    • 5
    • 6
  • Geraldine Cancel-Tassin
    • 23
    • 25
  • Olivier Cussenot
    • 23
    • 24
  • Fredrik Wiklund
    • 26
    • 27
  • Henrik Grönberg
    • 26
    • 27
  • Ros Eeles
    • 28
    • 29
  • Doug Easton
    • 28
    • 30
  • Zsofia Kote-Jarai
    • 28
    • 29
  • Alice S. Whittemore
    • 31
    • 32
    • 33
  • Chih-Lin Hsieh
    • 31
    • 34
  • Graham G. Giles
    • 28
    • 35
    • 36
  • John L. Hopper
    • 28
    • 35
    • 36
  • Gianluca Severi
    • 28
    • 35
    • 36
  • William J. Catalona
    • 37
    • 38
  • Diptasri Mandal
    • 39
    • 40
  • Elisa Ledet
    • 39
    • 40
  • William D. Foulkes
    • 28
    • 41
    • 42
  • Nancy Hamel
    • 28
    • 41
    • 42
  • Lovise Mahle
    • 28
    • 43
  • Pal Moller
    • 28
    • 43
  • Isaac Powell
    • 44
    • 45
  • Joan E. Bailey-Wilson
    • 44
    • 46
  • John D. Carpten
    • 44
    • 47
  • Daniela Seminara
    • 48
  • Kathleen A. Cooney
    • 3
    • 9
  • William B. Isaacs
    • 13
    • 14
  • International Consortium for Prostate Cancer Genetics
  1. 1.Data Coordinating Center for the ICPCGWake Forest University School of MedicineWinston-SalemUSA
  2. 2.Center for Cancer GenomicsWake Forest University School of MedicineWinston-SalemUSA
  3. 3.University of Michigan ICPCG GroupUniversity of Michigan Medical SchoolAnn ArborUSA
  4. 4.Departments of Genetics and BiostatisticsUniversity of North CarolinaChapel HillUSA
  5. 5.Mayo Clinic ICPGC GroupMayo ClinicRochesterUSA
  6. 6.Department of Laboratory Medicine and PathologyMayo ClinicRochesterUSA
  7. 7.University of Utah ICPCG GroupUniversity of Utah School of MedicineSalt Lake CityUSA
  8. 8.Department of MedicineUniversity of Utah School of MedicineSalt Lake CityUSA
  9. 9.Department of Internal MedicineUniversity of Michigan Medical SchoolAnn ArborUSA
  10. 10.Fred Hutchinson Cancer Research Center (FHCRC) ICPCG GroupSeattleUSA
  11. 11.Division of Public Health SciencesFHCRCSeattleUSA
  12. 12.Cancer Genetics BranchNational Human Genome Research Institute, NIHBethesdaUSA
  13. 13.Johns Hopkins University ICPCG GroupBaltimoreUSA
  14. 14.Department of Urology, Johns Hopkins Medical InstitutionsJohns Hopkins HospitalBaltimoreUSA
  15. 15.University of Ulm ICPCG GroupUniversity of UlmUlmGermany
  16. 16.Department of UrologyUniversity of UlmUlmGermany
  17. 17.Institute of Human GeneticsUniversity of UlmUlmGermany
  18. 18.University of Tampere ICPCG GroupUniversity of Tampere and Fimlab LaboratoriesTampereFinland
  19. 19.Institute of Biomedical Technology/BioMediTechUniversity of Tampere and Fimlab LaboratoriesTampereFinland
  20. 20.Department of Medical Biochemistry and GeneticsUniversity of TurkuTurkuFinland
  21. 21.Department of UrologyTampere University HospitalTampereFinland
  22. 22.Department of Health Sciences ResearchMayo ClinicRochesterUSA
  23. 23.CeRePP ICPCG GroupParisFrance
  24. 24.Department of UrologyAPHP, Hospital TenonParisFrance
  25. 25.CeRePP UPMC UniversityParisFrance
  26. 26.Karolinska ICPCG GroupKarolinska InstitutetStockholmSweden
  27. 27.Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
  28. 28.ACTANE (Anglo/Canadian/Texan/Australian/Norwegian/EU Biomed) Consortium ICPCG GroupSurreyUK
  29. 29.Institute of Cancer Research and Royal Marsden NHS Foundation TrustSurreyUK
  30. 30.Strangeways Laboratory, Department of Oncology, Centre for Cancer Genetic EpidemiologyUniversity of CambridgeCambridgeUK
  31. 31.BC/CA/HI ICPCG GroupStanford School of MedicineStanfordUSA
  32. 32.Department of Health Research and PolicyStanford School of MedicineStanfordUSA
  33. 33.Stanford Comprehensive Cancer CenterStanford School of MedicineStanfordUSA
  34. 34.Department of Urology and Department of Biochemistry and Molecular BiologyUniversity of Southern CaliforniaLos AngelesUSA
  35. 35.Cancer Epidemiology Centre, Cancer Council VictoriaMelbourneAustralia
  36. 36.Centre for Molecular, Environmental, Genetic and Analytical EpidemiologyUniversity of MelbourneMelbourneAustralia
  37. 37.Northwestern University ICPCG GroupChicagoUSA
  38. 38.Northwestern University Feinberg School of MedicineChicagoUSA
  39. 39.Louisiana State University ICPCG GroupNew OrleansUSA
  40. 40.Department of GeneticsLouisiana State University Health Sciences CenterNew OrleansUSA
  41. 41.Program in Cancer Genetics, Departments of Oncology and Human GeneticsMcGill UniversityMontrealCanada
  42. 42.Research Institute of the McGill University Health CentreMontrealCanada
  43. 43.The Norwegian Radium HospitalOsloNorway
  44. 44.African American Hereditary Prostate Cancer ICPCG GroupDetroitUSA
  45. 45.Karmanos Cancer InstituteWayne State UniversityDetroitUSA
  46. 46.Inherited Disease Research BranchNational Human Genome Research Institute, NIHBethesdaUSA
  47. 47.Genetic Basis of Human Disease Research DivisionTranslational Genomics Research InstitutePhoenixUSA
  48. 48.National Cancer Institute, NIHBethesdaUSA

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