Carcinogenesis vol.33 no.3 pp.598–603, 2012
Advance Access publication January 4, 2012
A genome-wide search for loci interacting with known prostate cancer risk-associated
Sha Tao1, Zhong Wang2,3, Junjie Feng2,3, Fang-Chi Hsu2,4,
Guangfu Jin2,3, Seong-Tae Kim2,3, Zheng Zhang2,3,
Henrik Gronberg5, Lilly S.Zheng2,3, William B.Isaacs6,
Jianfeng Xu1,2,3,7and Jielin Sun2,3,?
1Center for Genetic Epidemiology and Prevention, Van Andel Research
Institute, Grand Rapids, MI, USA,2Center for Cancer Genomics, Medical
Center Boulevard,3Center for Genomics and Personalized Medicine
Research,4Department of Biostatistical Sciences, Wake Forest University
School of Medicine, Winston-Salem, NC 27157, USA,5Department of
Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm
17177, Sweden,6Department of Urology, Johns Hopkins Medical Institutions,
Baltimore, MD 21201, USA and7Department of Urology, Wake Forest
University School of Medicine, Winston-Salem, NC 27157, USA
?To whom correspondence should be addressed. Tel: þ1 (336) 713 7500;
Fax: þ1 (336) 713-7566;
Genome-wide association studies (GWAS) have identified ?30
single-nucleotide polymorphisms (SNPs) consistently associated
with prostate cancer (PCa) risk. To test the hypothesis that other
sequence variants in the genome may interact with those 32
known PCa risk-associated SNPs identified from GWAS to affect
PCa risk, we performed a systematic evaluation among three ex-
isting PCa GWAS populations: CAncer of the Prostate in Sweden
population, a Johns Hopkins Hospital population, and the Cancer
Genetic Markers of Susceptibility population, with a total sample
size of 4723 PCa cases and 4792 control subjects. Meta-analysis of
the interaction term between each of those 32 SNPs and SNPs in
the genome was performed in three PCa GWAS populations. The
most significant interaction detected was between rs12418451 in
MYEOVand rs784411 in CEP152, with a Pinteractionof 1.15 3 1027
in the meta-analysis. In addition, we emphasized two pairs of
interactions with potential biological implication, including an
interaction between rs7127900 near insulin-like growth factor-2
(IGF2)/IGF2AS and rs12628051 in TNRC6B, with a Pinteractionof
3.39 3 1026and an interaction between rs7679763 near TET2 and
rs290258 in SYK, with a Pinteractionof 1.49 3 1026. Those results
show statistical evidence for novel loci interacting with known
risk-associated SNPs to modify PCa risk. The interacting loci
identified provide hints on the underlying molecular mechanism
of the associations with PCa risk for the known risk-associated
SNPs. Additional studies are warranted to further confirm the
interaction effects detected in this study.
Prostate cancer (PCa) is the most common non-skin cancer affecting
men in western countries. Inherited genetic variants play an impor-
tant role in contributing to familial aggregation of PCa. Since 2007,
genome-wide association studies (GWAS) successfully identified at
least 33 PCa risk-associated single-nucleotide polymorphisms (SNPs)
Although those risk-associated SNPs arewell replicated in multiple
studies (16–20), very few studies assess the potential epistasis or
gene–gene interaction between those SNPs and the rest of SNPs that
reside in the genome. In fact, epistatic effect is the norm rather than
exception for complex diseases, such as PCa. Inference from tumor-
igenesis and results from genetic modeling studies suggest that mul-
tiple susceptibility genes, either additively or multiplicatively,
determine individual risk to PCa. The importance of epistasis is also
supported by empirical evidence from model organisms and human
The evidence of epistatic effect from the empirical data suggests
that gene–gene interactions need to be examined in GWAS when
searching for PCa risk variants. Actually, assessment of gene–gene
interaction may reveal additional PCa risk variants, especially in the
situation where multiple risk-associated variants have been identified.
It is computationally possible to use a logistic regression model to
search thegenome and to identify additional variants that interact with
these known risk variants to modify the risk of developing PCa.
Recently, Ciampa et al. (24) reported a two-stage GWAS of epis-
tasis between 13 known PCa risk-associated SNPs and SNPs across
the genome in the National Cancer Institute Cancer Genetic Markers
of Susceptibility (CGEMS) Stage I population with 523 841 SNPs
and Stage II population with 27 383 SNPs, which were selected based
on the main effects in Stage I. No SNP–SNP interaction was identified
that reached a genome-wide significance level in Stage I or Stage II
data, and a list of top interactions were suggested and warranted
replication in other studies. The lack of replication data in Ciampa’s
study emphasized the importance of evaluating gene–gene interaction
in multiple GWAS populations. More importantly, combining indi-
vidual level data of multiple GWAS can improve the power to identify
SNPs that interact withthe knownrisk-associated SNPs toimpact PCa
risk. To this end, we performed a combined genome-wide search for
SNPs that interact with 32 PCa risk-associated variants identified
from GWAS in three case–control populations of European descents,
including 1583 PCa cases and 519 control subjects from the CAncer
Prostate in Sweden (CAPS), 1964 PCa cases and 3172 control sub-
jects from a Johns Hopkins Hospital (JHH) PCa and iControl database
and 1176 PCa cases and 1101 control subjects in the National Cancer
Institute CGEMS study. We also evaluated the list of SNP–SNP in-
teractions suggested by Ciampa’s study in the two independent
GWAS populations (CAPS and JHH).
Materials and methods
The first GWAS population included 1583 PCa patients and 519 control subjects
that matched the age distribution of case subjects from CAPS, a population-
based PCa case–control study from Sweden (CAPS) (6). Briefly, the CAPS
population was recruited from four regional cancer registries in Sweden and
diagnosed between July 2001 and October 2003. The clinical characteristics
of these patients are presented in Supplementary Table 1, available at
The second population was from a JHH PCa GWAS, which included 1964
PCa cases and 3172 control subjects. The cases are Caucasian PCa patients
who underwent radical prostatectomy for the treatment of PCa at JHH from
1 January 1999 through 31 December 2008 (25). The clinical characteristics of
these patients are presented in Supplementary Table 2, available at Carcino-
genesis Online. The control subjects for this population were an independent
group of Caucasian individuals from the Illumina iControlDB (iControls) data-
The third population was obtained from Stage I of the National Cancer
Institute CGEMS study. It included 1176 PCa cases and 1101 control subjects,
selected from the Prostate, Lung, Colon and Ovarian Cancer Screening Trial
(6,9). The genotype and phenotype data of the study are publicly available and
our use of the data was approved by CGEMS.
Genotype data, imputation and quality control
GWAS of the CAPS population was performed using Affymetrix 5.0 chip.
GWAS of the JHH case population was performed using the Illumina 610K
chip (24). GWAS of the iControls population (25) was performed using
Abbreviations: CAPS, CAncer of the Prostate in Sweden; CGEMS, Cancer
Genetic Markers of Susceptibility; CI, confidence interval; GWAS, genome-
wide association studies; IGF, insulin-like growth factor; JHH, Johns Hopkins
Hospital; OR, odds ratio; PCa, prostate cancer; RNAi, RNA interference; SNP,
? The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com
This work was supported by the Department of Defense (W81XWH-
09-1-0488 to J.S.); an intramural funding from the Van Andel Re-
search Institute to J.X. and the National Cancer Institute (CA129684
We thank all of the study subjects who participated in the CAncer of the
Prostate in Sweden study and the urologists who provided their patients to
the CAncer of the Prostate in Sweden and Johns Hopkins Hospital studies. We
acknowledge the contribution of multiple physicians and researchers in de-
signing and recruiting study subjects, including Dr H.-O.Adami. We also ac-
knowledge the National Cancer Institute Cancer Genetic Markers of
Susceptibility Initiative for making the data publicly available.
Conflict of Interest Statement: None declared.
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Received October 26, 2011; revised December 13, 2011;
accepted December 15, 2011
Genome-wide epistasis scan for PCa