Content uploaded by Luis G Carvajal Carmona
Author content
All content in this area was uploaded by Luis G Carvajal Carmona on Dec 15, 2014
Content may be subject to copyright.
1 3
Hum Genet
DOI 10.1007/s00439-014-1515-4
ORIGINAL INVESTIGATION
Candidate locus analysis of the TERT–CLPTM1L cancer risk
region on chromosome 5p15 identifies multiple independent
variants associated with endometrial cancer risk
Luis G. Carvajal‑Carmona · Tracy A. O’Mara · Jodie N. Painter · Felicity A. Lose · Joe Dennis · Kyriaki Michailidou ·
Jonathan P. Tyrer · Shahana Ahmed · Kaltin Ferguson · Catherine S. Healey · Karen Pooley · Jonathan Beesley ·
Timothy Cheng · Angela Jones · Kimberley Howarth · Lynn Martin · Maggie Gorman · Shirley Hodgson ·
National Study of Endometrial Cancer Genetics Group (NSECG) · The Australian National Endometrial Cancer
Study Group (ANECS) · Nicholas Wentzensen · Peter A. Fasching · Alexander Hein · Matthias W. Beckmann ·
Stefan P. Renner · Thilo Dörk · Peter Hillemanns · Matthias Dürst · Ingo Runnebaum · Diether Lambrechts ·
Lieve Coenegrachts · Stefanie Schrauwen · Frederic Amant · Boris Winterhoff · Sean C. Dowdy · Ellen L. Goode ·
Attila Teoman · Helga B. Salvesen · Jone Trovik · Tormund S. Njolstad · Henrica M. J. Werner · Rodney J. Scott ·
Katie Ashton · Tony Proietto · Geoffrey Otton · Ofra Wersäll · Miriam Mints · Emma Tham · RENDOCAS ·
Per Hall · Kamila Czene · Jianjun Liu · Jingmei Li · John L. Hopper · Melissa C. Southey · Australian Ovarian
Cancer Study (AOCS) · Arif B. Ekici · Matthias Ruebner · Nichola Johnson · Julian Peto · Barbara Burwinkel ·
Frederik Marme · Hermann Brenner · Aida K. Dieffenbach · Alfons Meindl · Hiltrud Brauch · The GENICA
Network · Annika Lindblom · Jeroen Depreeuw · Matthieu Moisse · Jenny Chang‑Claude · Anja Rudolph ·
Fergus J. Couch · Janet E. Olson · Graham G. Giles · Fiona Bruinsma · Julie M. Cunningham · Brooke L. Fridley ·
Anne‑Lise Børresen‑Dale · Vessela N. Kristensen · Angela Cox · Anthony J. Swerdlow · Nicholas Orr ·
Manjeet K. Bolla · Qin Wang · Rachel Palmieri Weber · Zhihua Chen · Mitul Shah · Paul D. P. Pharoah ·
Alison M. Dunning · Ian Tomlinson · Douglas F. Easton · Amanda B. Spurdle · Deborah J. Thompson
Received: 18 September 2014 / Accepted: 20 November 2014
© The Author(s) 2014. This article is published with open access at Springerlink.com
reported with endometrial cancer. To evaluate the role of
genetic variants at the TERT–CLPTM1L region in endome-
trial cancer risk, we carried out comprehensive fine-mapping
analyses of genotyped and imputed SNPs using a custom
Illumina iSelect array which includes dense SNP coverage
of this region. We examined 396 SNPs (113 genotyped, 283
imputed) in 4,401 endometrial cancer cases and 28,758 con-
trols. Single-SNP and forward/backward logistic regression
models suggested evidence for three variants independently
associated with endometrial cancer risk (P = 4.9 × 10−6 to
P = 7.7 × 10−5). Only one falls into a haplotype previously
Abstract Several studies have reported associations
between multiple cancer types and single-nucleotide poly-
morphisms (SNPs) on chromosome 5p15, which harbours
TERT and CLPTM1L, but no such association has been
I. Tomlinson, D. F. Easton and A. B. Spurdle have contributed
equally to the study.
See Supplementary material for full list of members.
Electronic supplementary material The online version of this
article (doi:10.1007/s00439-014-1515-4) contains supplementary
material, which is available to authorized users.
L. G. Carvajal-Carmona (*)
Genome Center and Department of Biochemistry and Molecular
Medicine, School of Medicine, University of California, Davis,
CA 95616, USA
e-mail: lgcarvajal@ucdavis.edu
T. A. O’Mara · J. N. Painter · F. A. Lose · K. Ferguson ·
J. Beesley · A. B. Spurdle · The Australian National Endometrial
Cancer Study Group (ANECS) · Australian Ovarian Cancer
Study (AOCS)
QIMR Berghofer Medical Research Institute, Brisbane, QLD,
Australia
J. Dennis · K. Michailidou · K. Pooley · M. K. Bolla · Q. Wang ·
D. F. Easton · D. J. Thompson (*)
Department of Public Health and Primary Care, Centre
for Cancer Genetic Epidemiology, University of Cambridge,
Cambridge, UK
e-mail: djt25@medschl.cam.ac.uk
J. P. Tyrer · S. Ahmed · C. S. Healey · M. Shah · P. D. P. Pharoah ·
A. M. Dunning · D. F. Easton
Department of Oncology, Centre for Cancer Genetic
Epidemiology, University of Cambridge, Cambridge, UK
Hum Genet
1 3
et al. 2002), suggesting that genetic factors play important
roles in the risk of this malignancy (Hemminki et al. 2004).
Endometrial cancer can be caused by rare, highly pen-
etrant mutations in DNA repair or replication genes such
as MLH1, MSH2, MSH6, PMS2, POLE or POLD1 that
result in Lynch Syndrome or in Polymerase Proofreading
Associated Polyposis (Briggs and Tomlinson 2013; Fearon
1997; Palles et al. 2013). Genome-wide association stud-
ies (GWAS) have also been used to dissect the genetics of
endometrial cancer and so far have convincingly identi-
fied one associated SNP, rs4430796, on chromosome 17q
close to the HNF1B gene (Spurdle et al. 2011; Setiawan
et al. 2012; Painter et al. 2014). The rs4430796 G allele is
associated with decreased risks of endometrial and prostate
cancers, but with an increased risk of type 2 diabetes (Gud-
mundsson et al. 2007). Candidate gene studies have also
identified an association between endometrial cancer and
two SNPs in the CYP19A1 gene (Setiawan et al. 2009).
Variants in chromosome 5p15, a region which harbours
the TERT and CLPTM1L genes, have been found through
GWAS to be associated with the risk of bladder, pancreas,
brain, testicular, breast, prostate, skin and lung cancers
and glioma (Haiman et al. 2011; Kote-Jarai et al. 2011,
2013; McKay et al. 2008; Petersen et al. 2010; Rafnar
et al. 2009; Shete et al. 2009; Stacey et al. 2009; Turnbull
et al. 2010; Wang et al. 2014). TERT encodes the catalytic
subunit of the telomerase reverse transcriptase enzyme.
Activation of TERT transcription occurs in most human
cancers where telomerase activity increases to counteract
associated with other cancer types (rs7705526, in TERT
intron 1), and this SNP has been shown to alter TERT pro-
moter activity. One of the novel associations (rs13174814)
maps to a second region in the TERT promoter and the other
(rs62329728) is in the promoter region of CLPTM1L; nei-
ther are correlated with previously reported cancer-associ-
ated SNPs. Using TCGA RNASeq data, we found signifi-
cantly increased expression of both TERT and CLPTM1L
in endometrial cancer tissue compared with normal tissue
(TERT P = 1.5 × 10−18, CLPTM1L P = 1.5 × 10−19). Our
study thus reports a novel endometrial cancer risk locus
and expands the spectrum of cancer types associated with
genetic variation at 5p15, further highlighting the impor-
tance of this region for cancer susceptibility.
Introduction
Endometrial cancer is the second most commonly diag-
nosed gynaecologic cancer in the world and accounts for
~5 % of all cancers in women (Kaaks et al. 2002). World-
wide, about 320,000 women are diagnosed with endome-
trial cancer and approximately 76,000 die of the disease
annually (http://globocan.iarc.fr/Default.aspx). Risk factors
for this malignancy include long reproductive span (early
menarche and/or late menopause), nulliparity, obesity, hor-
mone replacement therapy, tamoxifen, and personal and/or
family history of cancer of the endometrium, breast, ovary,
or colorectum (Beral et al. 2005; Fisher et al. 2005; Kaaks
T. Cheng · A. Jones · K. Howarth · L. Martin · M. Gorman ·
I. Tomlinson · National Study of Endometrial Cancer Genetics
Group (NSECG)
Wellcome Trust Centre for Human Genetics, University
of Oxford, Oxford, UK
S. Hodgson
Department of Clinical Genetics, St George’s Hospital Medical
School, London, UK
N. Wentzensen
Division of Cancer Epidemiology and Genetics, National Cancer
Institute, Bethesda, USA
P. A. Fasching
Division of Hematology/Oncology, Department of Medicine,
David Geffen School of Medicine, University of California at Los
Angeles, Los Angeles, CA, UK
P. A. Fasching · A. Hein · M. W. Beckmann · S. P. Renner ·
A. B. Ekici · M. Ruebner
Department of Gynecology and Obstetrics, Friedrich-Alexander
University Erlangen-Nuremberg, University Hospital Erlangen,
Erlangen, Germany
T. Dörk
Gynaecology Research Unit, Hannover Medical School,
Hannover, Germany
P. Hillemanns
Clinics of Gynaecology and Obstetrics, Hannover Medical
School, Hannover, Germany
M. Dürst · I. Runnebaum
Department of Gynaecology, Jena University Hospital-Friedrich
Schiller University, Jena, Germany
D. Lambrechts · J. Depreeuw · M. Moisse
Vesalius Research Center, VIB, Leuven, Belgium
D. Lambrechts · J. Depreeuw · M. Moisse
Department of Oncology, Laboratory for Translational Genetics,
KU Leuven, Leuven, Belgium
L. Coenegrachts · S. Schrauwen · F. Amant
Department of Oncology, KU Leuven, Leuven, Belgium
F. Amant
Division of Gynaecological Oncology, University Hospital
Leuven, Leuven, Belgium
B. Winterhoff · S. C. Dowdy · A. Teoman
Division of Gynecologic Oncology, Department of Obstetrics
and Gynecology, Mayo Clinic, Rochester, MN, USA
Hum Genet
1 3
(Supplementary Table 1). Germline DNA extracted from
blood was used for genotyping.
Healthy female controls with European ancestry and
known age at sampling were selected from controls gen-
otyped by the Breast Cancer Association Consortium
(BCAC) iCOGS project (Michailidou et al. 2013), or the
Ovarian Cancer Association Consortium (OCAC) iCOGS
project (Pharoah et al. 2013). We selected the 27,062
BCAC controls from studies in the same countries as the
endometrial cancer cases, 744 European-ancestry con-
trols from the Mayo Clinic Ovarian Cancer Case–Control
Study (MAY) and 896 controls from the Australian Ovarian
Cancer Study (AOCS). In addition, 282 Norwegian blood
donor controls with no known history of cancer were geno-
typed for this study (Supplementary Table 1).
Details of cases and controls are described in the Sup-
plementary Note.
SNP selection and genotyping
Cases and controls were genotyped on a custom Illuminia
Infinium iSelect array (“iCOGS”) with 211,155 SNPs,
designed by the Collaborative Oncological Gene–environ-
ment Study, a collaborative project involving four consortia
(Couch et al. 2013; Kote-Jarai et al. 2013; Michailidou et al.
2013; Pharoah et al. 2013). Cases and molecular markers
in treatment of endometrial cancer (MoMaTEC) controls
were genotyped by the Genome Quebec Innovation Center.
telomere shortening, thereby circumventing the normal
limits on cellular proliferation (Kolquist et al. 1998). Lit-
tle is known about CLPTM1L but recent studies have dem-
onstrated it has an anti-apoptotic role in lung and pancre-
atic cancer cells (James et al. 2014; Jia et al. 2014; Wang
et al. 2014). In recent studies, members of the Collabora-
tive Oncological Gene–environment Study (COGS) used
an Illumina iSelect high-density genotyping array (referred
to as the “iCOGS” array) and imputation around the TERT–
CLPTM1L region to identify several independent variants
for breast, ovarian and prostate cancers, and for telomere
length in lymphocytes (Bojesen et al. 2013; Kote-Jarai
et al. 2013). In the current study, we used the iCOGS array
and genotype imputation to investigate whether variants in
the TERT–CLPTM1L candidate region are associated with
the risk of endometrial cancer in populations of European
descent.
Materials and methods
Samples
For the iCOGS genotyping, 5,591 women with a con-
firmed diagnosis of endometrial cancer and European
ancestry were recruited via 11 separate studies in Western
Europe, North America and Australia, collectively called
the Endometrial Cancer Association Consortium (ECAC)
E. L. Goode · J. E. Olson
Division of Epidemiology, Department of Health Science
Research, Mayo Clinic, Rochester, MN, USA
H. B. Salvesen · J. Trovik · T. S. Njolstad · H. M. J. Werner
Department of Clinical Science, Centre for Cancerbiomarkers,
The University of Bergen, Bergen, Norway
H. B. Salvesen · J. Trovik · T. S. Njolstad · H. M. J. Werner
Department of Obstetrics and Gynecology, Haukeland University
Hospital, Bergen, Norway
R. J. Scott · K. Ashton
Hunter Medical Research Institute, John Hunter Hospital,
Newcastle, NSW, Australia
R. J. Scott
Hunter Area Pathology Service, John Hunter Hospital,
Newcastle, NSW, Australia
R. J. Scott · K. Ashton
Centre for Information Based Medicine, School of Biomedical
Science and Pharmacy, University of Newcastle, Newcastle,
NSW, Australia
K. Ashton
Discipline of Medical Genetics, School of Biomedical Sciences
and Pharmacy, University of Newcastle, Newcastle, NSW,
Australia
T. Proietto · G. Otton
School of Medicine and Public Health, University of Newcastle,
Newcastle, NSW, Australia
O. Wersäll · M. Mints
Department of Women’s and Children’s Health, Karolinska
Institutet, Karolinska University Hospital, Stockholm, Sweden
E. Tham · A. Lindblom · RENDOCAS
Department of Molecular Medicine and Surgery, Karolinska
Institutet, Stockholm, Sweden
P. Hall · K. Czene
Department of Medical Epidemiology and Biostatistics,
Karolinska Institutet, Stockholm, Sweden
J. Liu · J. Li
Human Genetics, Genome Institute of Singapore, Singapore,
Singapore
J. L. Hopper · G. G. Giles
Centre for Epidemiology and Biostatistics, Melbourne School
of Population and Global Health, The University of Melbourne,
Melbourne, VIC, Australia
M. C. Southey
Department of Pathology, Genetic Epidemiology Laboratory, The
University of Melbourne, Melbourne, VIC, Australia
Hum Genet
1 3
BCAC and OCAC control samples were genotyped at four
centres. Raw intensity data files for all consortia were sent
to the COGS data coordination centre at the University of
Cambridge for centralized genotype calling and QC, so that
all case and control genotypes were called using the same
procedure.
The study presented here relates only to SNPs within a
200 kb region (chr5:1,200,000–1,400,000) including the
TERT and CLPTM1L genes. For this region, SNPs were
selected for inclusion on the iCOGS array on the basis
of published cancer associations and from the March
2010 release of the 1000 Genomes Project (2012). These
included all known SNPs with MAF >0.02 in Europeans
and r2 > 0.1 with the then-known cancer-associated SNPs
[rs402710 (McKay et al. 2008)] and/or rs3816659 (Shen
et al. 2010), plus a tagging set for all known SNPs in the
linkage disequilibrium blocks encompassing the genes in
the region (SLC6A18, TERT and CLPTM1L). An additional
30 SNPs in TERT were selected through a telomere length
candidate gene approach. In total, 134 SNPs were selected,
121 of which were successfully manufactured.
Quality control
Genotypes were called using Illumina’s proprietary Gen-
Call algorithm, using a cluster file specifically generated
for the project using a subset of samples from each geno-
typing center. SNPs were excluded for call rate <95 %
(<99 % for MAF <5 %), MAF <0.1 % or deviations from
HWE significant at 10−7, based on a stratified Robinson-
Hill test. Samples were excluded for low overall call rate
(<95 %), heterozygosity >5 standard deviations from the
mean, non-female genotype (XO, XY or XXY), or <85 %
estimated European ancestry based on Identical By State
scores between study individuals and individuals in Hap-
Map (http://hapmap.ncbi.nlm.nih.gov/) and multidimen-
sional scaling.
For duplicate samples or those identified as close rela-
tives by IBS probabilities >0.85, the sample with the lower
call rate was excluded, except for case–control relative
pairs for which the case was retained. Among cases, the
minimum duplicate concordance rate was 99.96 %. For
cases, any 96-well plate containing ≥5 excluded samples
was entirely excluded.
For 2,006 cases, we could compare iCOGS genotypes
for 40 SNPs with corresponding genotypes from the rapid
replication stage of our initial GWAS (Spurdle et al. 2011).
Cases with unresolved discrepancies were excluded. After
these exclusions, genotypes were available for 113 SNPs in
the defined region, in 4,401 cases and 28,758 controls.
Imputation
We used ImputeV2 (Howie et al. 2009) to obtain in silico
genotypes for an additional 1,677 SNPs in this region using
two reference panels: the 1000 Genomes Phase 1 (April
Australian Ovarian Cancer Study (AOCS)
Peter MacCullum Cancer Centre, Melbourne, VIC, Australia
N. Johnson
Breakthrough Breast Cancer Research Centre, Institute of Cancer
Research, London, UK
J. Peto
London School of Hygiene and Tropical Medicine, London, UK
B. Burwinkel · F. Marme
Molecular Biology of Breast Cancer, Department of Gynecology
and Obstetrics, University of Heidelberg, Heidelberg, Germany
B. Burwinkel · H. Brenner · A. K. Dieffenbach
Division of Clinical Epidemiology and Aging Research, German
Cancer Research Center (DKFZ), Heidelberg, Germany
F. Marme · H. Brenner · A. K. Dieffenbach
German Cancer Consortium (DKTK), Heidelberg, Germany
A. Meindl
Division of Tumor Genetics, Department of Obstetrics
and Gynecology, Technical University of Munich, Munich,
Germany
H. Brauch · The GENICA Network
Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology
Stuttgart, University of Tuebingen, Tuebingen, Germany
The GENICA Network
Institute for Occupational Medicine and Maritime Medicine,
University Medical Center Hamburg-Eppendorf, Hamburg,
Germany
The GENICA Network
Department of Internal Medicine, Evangelische Kliniken Bonn
gGmbH, Johanniter Krankenhaus, Bonn, Germany
The GENICA Network
Institute of Pathology, Medical Faculty of the University of Bonn,
Bonn, Germany
The GENICA Network
Institute for Prevention and Occupational Medicine of the
German Social Accident Insurance, Institute of the Ruhr
University Bochum (IPA), Bochum, Germany
The GENICA Network
Molecular Genetics of Breast Cancer, Deutsches
Krebsforschungszentrum (DKFZ), Heidelberg, Germany
J. Chang-Claude
Division of Cancer Epidemiology, German Cancer Research
Center, Heidelberg, Germany
Hum Genet
1 3
2012 release) and an in-house genotyping panel that con-
tained 133 additional SNPs from the October 2010 1000
Genomes Project data release, genotyped in 15,044 sam-
ples from the SEARCH and CCHS BCAC studies (Bojesen
et al. 2013). After filtering for SNP frequency (MAF
≥0.02; 887 SNPs excluded) and imputation QC (info score
≥0.8; 394 further SNPs excluded), we included 396 SNPs
in the association analyses, comprising 113 genotyped
and 283 imputed. SNPs with MAF <0.02 were excluded
because we would not have statistical power to detect asso-
ciations with rare SNPs. We used a stringent cutoff for
the imputation information score to reduce the chance of
spurious associations caused by imputation artefacts. The
IMPUTEv2 “leave-out” internal concordance check gave
98.2 % concordance at SNPs with r2 ≥ 0.8 for SNPs on
the 1000 Genomes reference panel but not on the additional
in-house panel, and 99.2 % for those SNPs also on the in-
house reference panel.
Statistical analysis
Associations between each SNP and endometrial cancer
were estimated using unconditional logistic regression with
a per-allele (1df) model, based on the expected genotype
dosages for the imputed SNPs. Analyses were adjusted for
strata (6 of the 8 strata were defined by country, whilst the
large UK dataset was divided into ‘SEARCH’ and ‘other
UK’) and for the first 10 principal components of the
genomic kinship matrix, based on 37,000 uncorrelated
SNPs (r2 < 0.1), including ~1,000 selected as ancestry
informative markers, using an in-house C++ programme
incorporating the Intel MKL libraries for eigenvectors
(http://ccge.medschl.cam.ac.uk/software/). One princi-
pal component was derived specifically for the Leuven
(LES/LMBC) studies, for which there was substantial infla-
tion not accounted for by the other principal components.
Inflation of the test statistic (λ) was estimated by divid-
ing the 45th centile of the test statistic by the 45th cen-
tile of a 1df χ2 distribution based on 43,233 uncorrelated
(r2 < 0.1) SNPs selected for the iCOGS array by consor-
tia other than ECAC. This was converted to an equivalent
inflation for a study with 1,000 cases and 1,000 controls
(λ1,000) by adjusting for effective sample size,
where ncasek and nctrlk are the numbers of cases and con-
trols in strata k.
A ‘global’ test using the admixture maximum likelihood
method [AML (Tyrer et al. 2006)] was performed against
the null hypothesis that none of the genotyped SNPs within
the region are associated with endometrial cancer, with the
alternative hypothesis that at least one of the SNPs is asso-
ciated, based on 10,000 permutations. The test was per-
formed for 55 of the 113 genotyped SNPs, selected such
that none of the SNPs had a pairwise r2 ≥ 0.5 with another
SNP in the test.
1,000 =1+
500
(
−
1
)
k
1
ncasek+1
nctrlk
A. Rudolph
Department of Cancer Epidemiology/Clinical Cancer Registry
and Institute for Medical Biometrics and Epidemiology,
University Clinic Hamburg-Eppendorf, Hamburg, Germany
F. J. Couch · J. M. Cunningham
Department of Laboratory Medicine and Pathology, Mayo Clinic,
Rochester, MN, USA
F. J. Couch · J. M. Cunningham
Department of Health Science Research, Mayo Clinic, Rochester,
MN, USA
G. G. Giles · F. Bruinsma
Cancer Epidemiology Centre, Cancer Council Victoria,
Melbourne, Australia
G. G. Giles
Department of Epidemiology and Preventive Medicine, Monash
University, Melbourne, Australia
B. L. Fridley
Department of Biostatistics, University of Kansas Medical
Center, Kansas City, KS, USA
A.-L. Børresen-Dale · V. N. Kristensen
Department of Genetics, Institute for Cancer Research, The
Norwegian Radium Hospital, Oslo, Norway
A.-L. Børresen-Dale · V. N. Kristensen
Faculty of Medicine, The K.G. Jebsen Center for Breast Cancer
Research, Institute for Clinical Medicine, University of Oslo,
Oslo, Norway
V. N. Kristensen
Division of Medicine, Department of Clinical Molecular
Oncology, Akershus University Hospital, Ahus, Norway
A. Cox
Department of Oncology, Sheffield Cancer Research Centre,
University of Sheffield, Sheffield, UK
A. J. Swerdlow
Division of Genetics and Epidemiology, Institute of Cancer
Research, London, UK
A. J. Swerdlow · N. Orr
Division of Breast Cancer Research, Institute of Cancer
Research, London, UK
R. P. Weber
Department of Community and Family Medicine, Duke
University School of Medicine, Durham, NC, USA
Z. Chen
Division of Population Sciences, Department of Cancer
Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
Hum Genet
1 3
To determine independently associated SNPs, we used
forward stepwise logistic regression based on all SNPs with
P < 0.05 in the single-SNP analysis; at each stage, the most
significant SNP was potentially eligible for inclusion in the
final model if it was significant at P < 0.01 after adjustment
for other SNPs. Given the strong prior evidence of can-
cer associations with this region, this is a candidate gene
study, and hence the very stringent significance thresholds
required for a GWAS are not applicable here. The 396
SNPs in the analysis can be pairwise-tagged by 68 tagging
SNPs at r2 ≥ 0.5, hence the number of strictly independent
tests is closer to 68 than to 396 (and could be considered
to be even lower) which would give a Bonferroni-corrected
significance threshold of around 0.05/68 = 7.4 × 10−4. An
additional logistic regression was performed including all
SNPs retained in the step-wise process. Backwards logistic
regression was also performed. A secondary analysis was
performed in which the most significant independent SNPs
from the main analysis were tested for associations specifi-
cally with endometrioid and non-endometrioid histology
endometrial cancer, and in a case-only comparison of endo-
metrioid and non-endometrioid cases. Pairwise linkage dis-
equilibrium r2 measures were calculated from the iCOGS
samples.
As an alternative to the frequentist stepwise variable
selection procedure we also used a Bayesian-inspired
penalized maximum likelihood approach which simulta-
neously analyses all genotyped and imputed SNPs in the
region to identify the optimal subset for disease prediction
[HyperLasso (Hoggart et al. 2008)]. We used the normal
exponential gamma distribution (NEG) shrinkage prior
with shape parameter 1.0, as recommended by Vignal et al.
(2011). To obtain a SNP-wise type I error of 0.001, we used
a penalty (lambda) of 110, estimated based on 100 permu-
tations under the null for different values of lambdas.
The Tagger package (de Bakker et al. 2005) was used to
identify independent tagging SNPs for the AML analysis.
All analyses were conducted using R, including the Gen-
ABEL and SNPMatrix packages (Aulchenko et al. 2007;
Clayton and Leung 2007), apart from the HyperLasso anal-
ysis (Hoggart et al. 2008) and the AML testing (Tyrer et al.
2006). All statistical tests were 2-sided.
SNP annotation
We annotated all SNPs that had moderate to high LD with
the three risk alleles identified in our study using Galaxy
(Giardine et al. 2005) and the UCSC genome browser.
To do so, we followed the annotation scheme described
recently by Carvajal-Carmona et al. (2011).
Gene expression analysis
A literature search to identify all published microar-
ray studies investigating endometrial cancer was per-
formed and datasets accessed directly from the author
(Moreno-Bueno et al. 2003), publication supplementary
data (Risinger et al. 2003; Saidi et al. 2004) or the NCBI
Gene Expression Omnibus database [GEO; http://www.
ncbi.nlm.nih.gov/geo/; (Day et al. 2011) (GSE17025),
(Mhawech-Fauceglia et al. 2010) (GSE23518), (Salvesen
et al. 2009) (GSE14860)]. Additional microarray data
were downloaded from the Expression Project for Oncol-
ogy (expO) study via GEO (GSE2190) and TCGA (Kan-
doth et al. 2013) via the TCGA data portal (http://tcga-
data.nci.nih.gov/tcga/tcgaHome2.jsp). TERT expression
was interrogated by the platforms used in all eight data-
sets, whilst CLPTM1L was able to be interrogated by
five datasets [(Day et al. 2011; Kandoth et al. 2013;
Mhawech-Fauceglia et al. 2010; Salvesen et al. 2009)
and expO].
All datasets were log transformed (by taking the loga-
rithmic values of the signals to the base of two) and median
centred per array. The change in expression level of TERT
and CLPTM1L between non-endometrioid and endome-
trioid endometrial cancer for each individual study was
expressed as an effect size, a unit-free standardized mean
difference between groups. Gene expression results were
then combined using the t-based modelling approach (Choi
et al. 2003) using the meta-package in R. Meta-analysis
was performed using a random effects model to account for
between-study heterogeneity.
Fig. 1 Association between SNPs in the 5p15 region and endome-
trial cancer. SNPs in SNP sets 1–3 are shown by circles, squares and
triangles, respectively, with the filled symbols denoting the most sig-
nificant SNP in that set. Only SNPs with MAF >0.02 and imputation
information score >0.8 are shown
Hum Genet
1 3
Level 3 (processed) RNASeqV2 normalized expression
values for TCGA endometrial cancer samples were down-
loaded from the TCGA data portal. Differences in TERT
and CLPTM1L expression between cancer vs normal tissue
and endometrioid vs non-endometrioid endometrial cancer
tissue was assessed by Mann–Whitney U test using IBM
SPSS Statistics (version 22).
eQTL analysis
Level 2 (preprocessed) germline GWAS data from endo-
metrial cancer patients was downloaded from the TCGA
data portal and QC performed. SNPs were excluded for call
rate <95 %, MAF <1 % or deviations from HWE signifi-
cant at 10−4. Samples were excluded for low overall call
rate (<95 %), heterozygosity >3 standard deviations from
the mean, inconclusive sex status (X-chromosome homozy-
gosity rate between 0.2 and 0.8), or samples >6 standard
deviations from the mean scores for principal compo-
nent 1 or 2, calculated using CEU individuals in HapMap
(http://hapmap.ncbi.nlm.nih.gov/). For duplicate samples
or samples identified as close relatives by IBS probabilities
>0.85, the sample with the lower call rate was excluded.
RNA-Seq Zscores and GISTIC copy number calls for
TCGA endometrial cancer samples were obtained via the
cBio Portal for Cancer Genomics (http://www.cbioportal.
org/public-portal/index.do). There were 192 TCGA sam-
ples with both genotype and gene expression data avail-
able for analysis. The association of SNPs in the TERT–
CLPTM1L gene region (chr5:1,200,000–1,400,000) with
TERT and CLPTM1 expression was assessed using PLINK,
adjusting for copy number.
Results
We performed high-density genotyping and genotype
imputation for variants in the 5p15 TERT–CLPTM1L
region to examine genetic associations with endometrial
cancer risk. For this purpose, we used a custom-designed
Illumina iSelect ~200,000 SNP array (“iCOGS”), which
included 118 successfully genotyped SNPs (after standard
QC exclusions) spanning a 200 kb region (chr5:1,200,000–
1,400,000), to genotype 4,401 endometrial cancer cases
from 11 centres participating in the Endometrial Cancer
Association Consortium (ECAC) and 28,758 control sub-
jects from the Breast Cancer Association Consortium
(BCAC) and the Ovarian Cancer Association Consortium
(OCAC). All subjects were of European ancestry (Sup-
plementary Table 1). We then imputed the genotypes of
untyped SNPs using 1000 Genomes project data (April
2012 release) as a reference. After excluding SNPs with
an imputation information score <0.8 or minor allele fre-
quency <0.02, 113 genotyped and 283 imputed SNPs
were included in the analyses. There was no evidence of
genomic inflation (λ1,000 = 1.012, based on 43,233 uncor-
related iCOGS SNPs separate from those presented here).
First, a ‘global’ test using the admixture maximum like-
lihood method (AML) (Tyrer et al. 2006) against the null
hypothesis that none of the genotyped SNPs within the
TERT–CLPTM1L region are associated with endometrial
cancer provided significant evidence that at least one SNP
is associated (P = 0.0001).
Single-SNP association testing identified 61 out of 396
SNPs with P values <0.05, compared with <20 expected by
chance (Fig. 1; Supplementary Table 2). Forward stepwise
logistic regression based on these 61 SNPs identified three
imputed SNPs (rs7705526, rs13174814 and rs62329728)
that each showed evidence of being independently asso-
ciated with disease (P = 7.7 × 10−5, 4.9 × 10−6 and
2.2 × 10−5; conditioning on the other SNPs in the model
P = 9.7 × 10−3, 1.7 × 10−4 and 1.8 × 10−4, respectively;
Table 1). The three SNPs had high imputation informa-
tion scores (0.89, 0.98 and 0.82, respectively). Backward
stepwise regression did not improve the model. The link-
age disequilibrium (LD) between these three SNPs is weak
(maximum pairwise r2 = 0.047; Table 1), which further
suggests that they represent independent risk factors for
endometrial cancer. Although rs7705526 did not reach the
approximate Bonferroni-corrected significance threshold
(7.4 × 10−4; see “Materials and methods”), it was retained
in the model because of its individual significance and the
Table 1 The 3 SNPs showing independent associations with endometrial cancer
Unconditional analyses were adjusted for study strata (see “Materials and methods”) and for the first ten principal components. The Conditional
Analysis model was also adjusted for the above variables and contained all 3 listed SNPs. Odds ratios (OR) are for allele A1
SNP Position
(bld 37)
A1/A2 Frequency
of A1
Imputation
information
score
Unconditional analysis Conditional analysis r2 with
rs7705526
r2 with
rs13174814
OR (95 % CI) P value OR (95 % CI) P value
rs7705526 1,285,974 C/A 0.33 0.89 1.11 (1.06, 1.17) 7.7E−05 1.08 (1.02, 1.14) 9.7E−03
rs13174814 1,299,859 G/C 0.25 0.98 0.87 (0.82, 0.93) 4.9E−06 0.89 (0.84, 0.95) 1.7E−04 0.047
rs62329728 1,356,890 G/A 0.06 0.82 1.27 (1.14, 1.43) 2.2E−05 1.24 (1.11, 1.39) 1.8E−04 0.024 <0.001
Hum Genet
1 3
strong prior evidence supporting a role for this particular
SNP in hormonal cancers (Bojesen et al. 2013).
Whilst the three SNPs in Table 1 were the most signifi-
cant in the forward logistic regression, each SNP should
be considered as a tagging or representative SNP for a set
of SNPs, sometimes referred to as an association “peak”.
For each of the three SNPs, Supplementary Table 3 lists all
other SNPs in the analysis which were in LD (r2 > 0.2) with
that SNP, and which have likelihood ratios of <100:1 rela-
tive to the most significant SNP for that set. The SNP sets
harbouring rs7705526, rs13174814 and rs62329728 (SNP
sets 1, 2 and 3), respectively, contain 12, 4 and 10 distinct
SNPs, none of which could be excluded as potentially caus-
ative on the basis of statistical analysis. Replacing each of
the three imputed SNPs in Table 1 with a genotyped SNP
from its own SNP set, each SNP set still showed evidence
of association with endometrial cancer in the multi-SNP
model, albeit with slightly weaker significance for two of
the three sets, indicating that the observed effects are not
due to imputation artefacts (Supplementary Table 4).
As an alternative to the frequentist stepwise variable
selection procedure, we also used a Bayesian-inspired
penalized maximum likelihood approach which simulta-
neously analyses all genotyped and imputed SNPs in the
region to identify the optimal subset for disease prediction
[HyperLasso (Hoggart et al. 2008)]. With shrinkage param-
eters fixed to obtain a Type I Error Rate of 0.001, the four
best-fitting models all contained rs13174818 (lead SNP in
SNP set 2), and one of rs7705526, rs33961405, rs7725218
or rs7734992, all of which fall within SNP set 1. This dif-
fers in some respects from the stepwise regression results,
in which rs13174814 and rs62329728 were more signifi-
cant than rs7705526, and provides further support for a role
of SNP set 1 in endometrial cancer.
Of the three SNPs independently associated with endo-
metrial cancer in our study, only one (rs7705526) lies
in an LD region previously associated with cancer risk.
rs7705526 (OR = 1.11, CI = 1.06–1.17, P = 7.7 × 10−5)
is located in the first intron of TERT (chr5:1,285,974, Sup-
plementary Fig. 1a). In the recent COGS study of breast
and ovarian cancer risk and telomere length associated
with SNPs in the TERT region, rs7705526 was classified
as being in what was referred to as “peak 2” (one of two
sets of associated SNPs straddling TERT introns 2–4 in
Fig. 2 Forest plot showing
the differential expression of
a TERT and b CLPTM1L by
endometrial cancer histological
subtype using collated datasets
of endometrial cancer micro-
array gene expression. The
solid vertical line represents
no change in gene expression
between the two histological
subtypes and the dashed line
indicates the overall standard-
ized mean difference (SMD)
in expression across all studies
analysed. SMD is a unit-free
measurement of gene expres-
sion. A positive SMD value
represents increased gene
expression in non-endometrioid
endometrial cancer (NEEC)
compared with endometrioid
endometrial cancer (EEC).
Heterogeneity P value was
calculated by Q-statistic
Hum Genet
1 3
that study), and was associated with longer telomeres in
blood cells and with increased risks of breast cancer (oes-
trogen receptor negative and positive subtypes) and ovar-
ian cancer (serous low-malignant potential and serous
invasive epithelial) (Bojesen et al. 2013; Pharoah et al.
2013). rs7705526 is in high LD with prostate cancer SNP
rs7725218 (r2 = 0.87) (Kote-Jarai et al. 2013), and also in
moderate LD with SNPs in “peak 3” of the COGS study,
e.g., r2 = 0.36 with rs10069690, which is particularly
associated with oestrogen receptor negative breast cancer
and with both subtypes of ovarian cancer (Supplemen-
tary Table 5) (Bojesen et al. 2013; Pharoah et al. 2013).
rs7705526 is also in LD with rs7726159 and rs2736100
(r2 = 0.95 and 0.53, respectively, Supplementary Table 5),
which are reported to be associated with multiple cancers
including lung, ovarian, testicular, pancreatic and prostate
cancers and glioma. Therefore, rs7705526 lies in a complex
risk haplotype that is now associated with risks of at least
eight different types of cancers.
The two remaining SNP sets identified as independently
associated with endometrial cancer risk in our study (repre-
sented by rs13174814 and rs62329728) have not, to the best
of our knowledge, been previously associated with cancer
(Supplementary Table 5), and therefore represent novel risk
Fig. 3 Boxplots of endometrial tissue normalized gene expression
levels using RNASeq data generated by The Cancer Genome Atlas.
Boxplots depict the median and first and third quartiles. a TERT
expression in endometrioid endometrial cancer (EEC) and non-endo-
metrioid endometrial cancer (NEEC) tissue samples. b CLPTM1L
expression in EEC and NEEC tissue samples. c TERT expression
in endometrial cancer and normal endometrial tissue. d CLPTM1L
expression in endometrial cancer and normal endometrial tissue
Hum Genet
1 3
variants in the region. rs13174814 (OR = 0.87, CI = 0.82–
0.93, P = 4.9 × 10−6) maps to the TERT promoter (chr5:
1,299,859 and ~4.7 Kb from the 5′ UTR), a region that
has been previously associated with the risk of testicu-
lar [rs4635969 (Turnbull et al. 2010)], lung [rs4975616
(Landi et al. 2009; Wang et al. 2008)], prostate [rs7712562,
rs2853669, rs2736107 and rs13190087 (Kote-Jarai et al.
2013)] and breast cancers [rs2853669, rs2736108 and
rs2736107 (Bojesen et al. 2013)]. However, the previ-
ously reported cancer-associated variants show only weak
LD with rs13174814 (r2 < 0.07 for all comparisons) (Sup-
plementary Table 5), suggesting that this SNP represents
a novel risk variant for cancer in the promoter region of
TERT. The other SNP independently associated with endo-
metrial cancer, rs62329728 (OR = 1.27, CI = 1.14–1.43,
P = 2.2 × 10−5), maps to a non-coding region ~12 kb
upstream of the 5′ UTR of CLPTM1L (Supplementary
Fig. 1c). To the best of our knowledge, rs62329728 is not
correlated with any published cancer SNP (r2 < 0.05), and
thus represents a new cancer risk allele in the CLPTM1L
region.
rs13174814 and rs62329728 showed similar associa-
tions for endometrioid and the more aggressive non-endo-
metrioid histology endometrial cancers (Supplementary
Table 6). Although rs7705526 was not significantly asso-
ciated with non-endometrioid cancers, the number of non-
endometrioid cancers (n = 757) was far smaller than the
number of endometrioid cancers (n = 3,535), and the case-
only endometrioid vs non-endometrioid analyses did not
show any significant differences (P > 0.05).
To identify possible mechanistic associations between
TERT, CLPTM1L and endometrial cancer, we searched for
information on endometrial gene expression and somatic
variation in publically available datasets. Specifically, we
looked at eight microarray datasets that have compared
gene expression levels in endometrioid and non-endome-
trioid cancer (Fig. 2) and RNASeq data from The Cancer
Genome Atlas (TCGA, Fig. 3). Analysis of microarray
data found that TERT was overexpressed in non-endome-
trioid cancer (P = 0.0015, Fig. 2a), however, this was not
observed in the larger TCGA RNASeq dataset (P = 1.0,
Fig. 3a). Increased expression of CLPTM1L in non-
endometrioid cancer was seen across five of the microar-
ray datasets that also interrogated CLPTM1L expression
(P < 0.0001, Fig. 2b), with a similar result also found by the
TCGA RNASeq analysis (P = 4.1 × 10−8, Fig. 3b). Using
TCGA RNASeq data we found significantly increased
expression of both TERT (Fig. 3c) and CLPTM1L (Fig. 3d)
in endometrial cancer tissue compared with normal tissue
(TERT P = 1.5 × 10−18, CLPTM1L P = 1.5 × 10−19).
TCGA endometrial cancer data analysis (http://www.cbio
portal.org/public-portal/index.do) shows that the 5p15.33
region containing both TERT and CLPTM1L is significantly
amplified in ~3 % of cases (Gistic Q value <0.00011, not
shown), whilst TERT and CLPTM1L mutations have been
identified in a small fraction of endometrial tumours (Kan-
doth et al. 2013).
We then assessed association between SNPs in the
region and TERT and CLPTM1L expression. Our most
strongly associated risk variants were not genotyped by
the TCGA genotyping platform (Affymetrix 6.0) and it
was not possible to impute these SNPs with a satisfactory
degree of accuracy (imputation information scores of 0.41,
0.35 and 0.45 for rs7705526, rs13174814 and rs62329728,
respectively) based on this genotyping. Other variants in
the region were assessed for association with expression
of TERT (Supplementary Table 7) or CLPTM1L (Supple-
mentary Table 8): the best TERT eQTL (P = 0.009) was
for rs2853668 (endometrial cancer risk P = 7.2 × 10−4;
Supplementary Table 2) located 166 bp from rs13174814
(r2 = 0.10) in the TERT promoter; the best CLPTM1L
eQTL (P = 0.06) was observed for rs2736100 (endome-
trial cancer risk P = 8.6 × 10−4; Supplementary Table 2),
located 542 bp from rs7705526 (r2 = 0.53). The TCGA
genotyping array provided reasonable tags for rs7705526
(best tag rs2736100 with r2 = 0.53), but not for rs62329728
(best tag rs246992, r2 = 0.09) or rs1317814 (best tag
rs246995, r2 = 0.13).
Discussion
Using high-density genotyping, imputation, a ‘global’ like-
lihood test and multi-SNP logistic regression analyses, we
have shown for the first time that genetic variants in the
TERT–CLPTM1L region are associated with the risk of
endometrial cancer, and provide evidence that this region
contains three independent risk SNPs for this cancer. One
previous study has reported a nominally significant asso-
ciation between a SNP in the TERT region (rs2736122)
and endometrial cancer (reported P = 0.03) (Prescott et al.
2010), but this SNP was not significant in our larger anal-
ysis (P = 0.85; Supplementary Table 5), whilst a recent
multi-cancer study of nearly 2,000 5p15.33 SNPs did not
report an association with endometrial cancer (Wang et al.
2014). Only one of the endometrial cancer risk variants
identified in our study (rs7705526) lies in an LD region that
has been previously associated with other cancer types.
To date, GWAS for endometrial cancer have convinc-
ingly identified evidence for endometrial cancer risk asso-
ciation at the HNF1B locus (Spurdle et al. 2011; Setiawan
et al. 2012; Painter et al. 2014), the risk allele of which
(rs4430796A) maps to a region that has also been associ-
ated with the risk of ovarian and prostate cancers (Gud-
mundsson et al. 2007; Shen et al. 2013; Thomas et al.
2008). In the candidate study of the 5p15 multi-cancer
Hum Genet
1 3
region presented here, we have identified up to three new
independent endometrial cancer risk variants within a
locus already associated with multiple cancers, potentially
accounting for ~0.5 % of the excess familial relative risk
of endometrial cancer. A similar candidate region approach
has been used successfully to demonstrate associations
between variation at the 8q24 multi-cancer region and thy-
roid cancer, another understudied malignancy (Jones et al.
2012). We thus propose that future studies on the role of
additional multi-cancer regions, such as 1q32/MDM4,
4q24/TET2, 8q24, 10p12/MLT10, 14q24/RAD51B8 or
19q13/MERIT40 (Sakoda et al. 2013), are worthwhile
endeavours for cancers that are relatively understudied,
including endometrial cancer.
Among the list of 41 TERT SNPs for which we were
able to identify a previous report of a significant associa-
tion with cancer in a European ancestry population (Sup-
plementary Table 5), only those SNPs which are in LD with
rs7705526 showed even nominally significant associations
with endometrial cancer (with the exceptions of P = 0.032
for rs402710 and P = 0.041 for rs13172201), and none
remained significant after conditioning on rs7705526.
This suggests that we identified one SNP from a haplotype
which is associated with endometrial cancer and also with
multiple other types of cancer, and two mutually independ-
ent SNPs which are associated with endometrial cancer but
do not lie in haplotypes previously reported to be associ-
ated with any other type of cancer. However, this does not
exclude the possibility that these novel endometrial cancer
SNPs are also multi-cancer variants. The 5p15.33 region
has complex LD patterns and is poorly tagged by many
GWAS genotyping panels. As a comparison, we exam-
ined the SNP coverage of this region in a set of 5,180 con-
trol subjects genotyped using the Illumina Infinium 1.2M
GWAS array as part of the Wellcome Trust Case Control
Consortium (2007), for which missing genotypes were
imputed using the same method and reference panel as in
our main study. Of the 799 SNPs with MAF >0.02, the
median imputation information score in the iCOGS set was
0.80 compared with 0.21 in the 1.2M GWAS set, and 87 %
of SNPs had an information score of at least 0.4 in the
iCOGS set compared to just 26 % of SNPs reaching this
threshold in the GWAS set (Supplementary Fig. 2; Supple-
mentary Table 2). These findings emphasize the value of
targeted, dense genotyping as a complementary approach
to standard GWAS. The imputation information score for
rs7705526 (the only one of our associated SNPs previously
associated with other cancer types) was 0.55 in the GWAS
set, whilst the GWAS information scores for rs13174814
and rs62329728 were just 0.43 and 0.12, respectively. Thus,
the use of a deliberately dense panel of local SNPs, such as
that used in this study, may reveal associations between the
novel endometrial cancer risk SNPs and other cancers.
Fine-mapping genomic regions which potentially contain
multiple causal variants is a relatively new area of research,
and generally accepted thresholds for claiming the statisti-
cal significance of variants do not yet exist. An appropriate
threshold for a given region can depend on the number of
SNPs tested, the extent of LD in the region, the frequencies
of the variants and the prior evidence for association. Some
authors have suggested using Bayesian inference as an
alternative to frequentist P value-based methods. Here, we
performed one such Bayesian-inspired method, the Hyper-
Lasso (Hoggart et al. 2008), which also found associations
with SNP sets 1 and 2, but reported no further associated
SNPs. The results of this alternative method increase our
confidence in the associations between endometrial can-
cer and SNP sets 1 and 2, while direct genotyping of large
case–control studies will help towards resolving the disa-
greement between statistical methods regarding the asso-
ciations with SNP set 3. The use of imputed genotypes in
our analysis allowed us to examine a broader group of SNPs
than would have been possible in an analysis restricted to
SNPs that had been genotyped. Genotyping cases and con-
trols using the same array, thorough pre-imputation quality
control, excluding rarer SNPs and restricting the analysis
to SNPs with high imputation information scores (>0.8)
should have reduced imputation errors and minimized the
chance of false-positive associations (Marchini and Howie
2010). Nevertheless, it will be informative to replicate the
analysis using direct genotyping in independent samples.
Two of the endometrial cancer risk SNPs identified in
this study are in or near the TERT gene. The risk allele at
rs7705526 has been shown to result in increased TERT pro-
moter activity in luciferase reporter assays conducted in
ER-negative breast, ER-positive breast and ovarian cancer
cell lines (Bojesen et al. 2013), and was reported to be asso-
ciated with TERT transcript levels in benign prostate tissue
(Kote-Jarai et al. 2013). Data from ENCODE show that
rs13174814 and another SNP in LD with it, rs13174919,
map to a 400 bp region (chr5:1,299,601–1,300,000) identi-
fied as an insulator in embryonic stem cells, although an
insulator function has yet to be experimentally validated in
this or other cell lines. Interestingly, there are also a number
of chromatin interactions, indicative of regulatory poten-
tial in the region of the most likely causal SNPs for this
SNP set in two cancer cell lines (MCF7 and K562) (Sup-
plementary Fig. 1b). Furthermore, our search for functional
effects in RegulomeDB (Boyle et al. 2012) and HaploReg
(Ward and Kellis 2012) suggests that rs13174814 affects
the binding of both RAD21 and CTCF. Previous studies
have shown that both RAD21 and CTCF are deregulated
or aberrantly expressed in endometrial cancer (Hoivik et al.
2014; Supernat et al. 2012). Interestingly, CTCF appears to
be a target for slippage mutations in endometrial cancers
with microsatellite instability (Zighelboim et al. 2014).
Hum Genet
1 3
The third endometrial cancer risk SNP identified in this
study is in the upstream/promoter region of CLPTM1L,
~60 kb away from TERT, and which also harbours sev-
eral cancer risk alleles, mostly for non-hormone-related
malignancies such as lung, bladder and pancreatic cancers
(Haiman et al. 2011; Kote-Jarai et al. 2011, 2013; McKay
et al. 2008; Petersen et al. 2010; Rafnar et al. 2009; Shete
et al. 2009; Stacey et al. 2009; Turnbull et al. 2010; Wang
et al. 2014). The evidence for an involvement of CLPTM1L
in tumorigenesis is, however, more limited. One study
has linked CLPTM1L expression with cisplatin resistance
in an ovarian cancer cell line (Yamamoto et al. 2001) and
more recently, CLPTM1L was shown to promote growth
and enhance chromosomal instability in pancreatic cancer
cell lines (Jia et al. 2014). Although yet to be functionally
characterized, rs62329728 is in LD (r2 > 0.8) with addi-
tional SNPs across the TERT–CLPTM1L region which are
located within areas of open chromatin, transcription fac-
tor binding or chromatin interactions in multiple ENCODE
cell lines including the Ishikawa endometrial cancer cell
line (Supplementary Fig. 1c), and hence may have regula-
tory potential.
Our analysis of microarray datasets suggested dif-
ferences in CLPTM1L expression between endometrial
tumour histological subtypes, and increased expression of
both TERT and CLPTM1L between endometrial tumour
and normal tissue. Further, a role for TERT is indicated by
eQTL analyses, in that endometrial cancer risk-associated
SNPs were associated with expression of TERT in endo-
metrial tumour tissue. These results have highlighted a new
region of the TERT promoter worthy of functional inves-
tigation, and, importantly, implicate CLPTM1L expression
in the aetiology of endometrial cancer. As such, these find-
ings will expand biological studies of the TERT/CLPTM1L
region in this and other hormone-driven cancers. A pos-
sibility that should be examined in future studies is the
existence of long-range regulatory elements in this region
and their effects on TERT, and whether the prioritized risk-
associated variants play a role in CLPTM1L regulation.
In summary, we have used an informed candidate
approach to identify a novel endometrial cancer risk
locus. Importantly, our study highlights the value of using
the information generated by GWAS to guide candidate
gene/SNP approaches, particularly for those cancer types
that have been relatively understudied using the GWAS
approach, such as endometrial cancer. Unlike previous
studies in hormone-related malignancies (breast, ovarian
and prostate), which only found risk variants in or near
TERT, our study found evidence of risk variants in and
near TERT and also near CLPTM1L. Future studies should
investigate the functional effects of prioritized risk-asso-
ciated variants on CLPTM1L and/or TERT in endometrial
cancer and other cancer models. Furthermore, additional
studies, ideally using re-sequencing, should be carried
out to uncover possible additional low frequency causal
variants.
Acknowledgments The authors thank the many individuals who
participated in this study and the numerous institutions and their staff
who have supported recruitment, detailed in full in the Supplemen-
tary Notes. Fine-mapping analysis was supported by NHMRC Pro-
ject Grant (ID#1031333) to ABS, DFE and AMD. LGC-C receives
funding from the University of California Davis, The V Foundation
for Cancer Research, and The National Institute On Aging (award
number P30AG043097) and The National Cancer Institute (award
number K12CA138464) of the National Institutes of Health. The
content is solely the responsibility of the authors and does not neces-
sarily represent the official views of the National Institutes of Health.
ABS is supported by the National Health and Medical Research
Council (NHMRC) Fellowship scheme. D. F. E. is a Principal
Research Fellow of Cancer Research UK. A. M. D. is supported by
the Joseph Mitchell Trust. I. T. is supported by Cancer Research UK
and the Oxford Comprehensive Biomedical Research Centre. P. A. F.
was partly funded by the Dr. Mildred Scheel Stiftung of the Deutsche
Krebshilfe (German Cancer Aid). FA is senior clinical researcher for
the Research Fund Flanders (F.W.O.). Funding for the iCOGS infra-
structure came from: the European Community’s Seventh Frame-
work Programme under Grant agreement no 223175 (HEALTH-
F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118,
C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384,
C5047/A15007, C5047/A10692), the National Institutes of Health
(CA128978) and Post-Cancer GWAS initiative (1U19 CA148537,
1U19 CA148065 and 1U19 CA148112—the GAME-ON initiative),
the Department of Defence (W81XWH-10-1-0341), the Canadian
Institutes of Health Research (CIHR) for the CIHR Team in Familial
Risks of Breast Cancer, Komen Foundation for the Cure, the Breast
Cancer Research Foundation, and the Ovarian Cancer Research
Fund. NCI CA 15083 (Mayo Clinic CCSG) supported the genotyp-
ing carried out by the Genotyping Core laboratory. ANECS recruit-
ment was supported by Project Grants from the National Health and
Medical Research Council of Australia (ID#339435), The Cancer
Council Queensland (ID#4196615) and Cancer Council Tasmania
(ID#403031 and ID#457636). SEARCH recruitment was funded
by a programme Grant from Cancer Research UK (C490/A10124).
Case genotyping was supported by the National Health and Medi-
cal Research Council (ID#1031333). NSECG was supported princi-
pally by Cancer Research UK and by funds from the Oxford Com-
prehensive Biomedical Research Centre, with core infrastructure
support to the Wellcome Trust Centre for Human Genetics, Oxford
provided by Grant 075491/Z/04. The Bavarian Endometrial Cancer
Study (BECS) was partly funded by the ELAN fund of the Univer-
sity of Erlangen. The Leuven Endometrium Study (LES) was sup-
ported by the Verelst Foundation for endometrial cancer. The Mayo
Endometrial Cancer Study (MECS) and Mayo controls (MAY) were
supported by Grants from the National Cancer Institute of United
States Public Health Service (R01 CA122443, P30 CA15083, P50
CA136393, and GAME-ON the NCI Cancer Post-GWAS Initiative
U19 CA148112), the Fred C and Katherine B Andersen Foundation,
the Mayo Foundation, and the Ovarian Cancer Research Fund with
support of the Smith family, in memory of Kathryn Sladek Smith.
MoMaTEC received financial support from a Helse Vest Grant, the
University of Bergen, Melzer Foundation, The Norwegian Cancer
Society (Harald Andersens legat), The Research Council of Nor-
way and Haukeland University Hospital. The Newcastle Endome-
trial Cancer Study (NECS) acknowledges contributions from the
University of Newcastle, The NBN Children’s Cancer Research
Group, Ms Jennie Thomas and the Hunter Medical Research Insti-
tute. RENDOCAS was supported through the regional agreement
Hum Genet
1 3
on medical training and clinical research (ALF) between Stockholm
County Council and Karolinska Institutet (numbers: 20110222,
20110483, 20110141 and DF 07015), The Swedish Labor Market
Insurance (number 100069) and The Swedish Cancer Society (num-
ber 11 0439). The Cancer Hormone Replacement Epidemiology in
Sweden Study (CAHRES, formerly called The Singapore and Swed-
ish Breast/Endometrial Cancer Study; SASBAC) was supported by
funding from the Agency for Science, Technology and Research of
Singapore (A*STAR), the US National Institute of Health (NIH) and
the Susan G. Komen Breast Cancer Foundation. The Shanghai Endo-
metrial Cancer Genetic Study (SECGS) was supported by Grants
from the National Cancer Institute of United States Public Health
Service (RO1 CA 092585 and R01 CA90899, R01 CA64277). The
Breast Cancer Association Consortium (BCAC) is funded by Cancer
Research UK (C1287/A10118, C1287/A12014). The Ovarian Cancer
Association Consortium (OCAC) is supported by a grant from the
Ovarian Cancer Research Fund thanks to donations by the family
and friends of Kathryn Sladek Smith (PPD/RPCI.07), and the UK
National Institute for Health Research Biomedical Research Centres
at the University of Cambridge. Additional funding for individual
control groups is detailed in the Supplementary Text.
Conflict of interest The authors declare that they have no conflicts
of interest.
Ethical standards We declare that all the experiments presented
in this manuscript comply with the current laws of the countries in
which they were performed. Informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution License which permits any use, distribu-
tion, and reproduction in any medium, provided the original author(s)
and the source are credited.
References
Aulchenko YS, Ripke S, Isaacs A, van Duijn CM (2007) GenABEL:
an R library for genome-wide association analysis. Bioinformat-
ics 23:1294–1296. doi:10.1093/bioinformatics/btm108
Beral V, Bull D, Reeves G (2005) Endometrial cancer and hor-
mone-replacement therapy in the Million Women Study. Lancet
365:1543–1551. doi:10.1016/S0140-6736(05)66455-0
Bojesen SE, Pooley KA, Johnatty SE, Beesley J, Michailidou K, Tyrer
JP, Edwards SL, Pickett HA, Shen HC, Smart CE et al (2013)
Multiple independent variants at the TERT locus are associated
with telomere length and risks of breast and ovarian cancer. Nat
Genet 45:371–384, 384e371–372. doi:10.1038/ng.2566
Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski
M, Karczewski KJ, Park J, Hitz BC, Weng S et al (2012) Anno-
tation of functional variation in personal genomes using Regu-
lomeDB. Genome Res 22:1790–1797. doi:10.1101/gr.137323.112
Briggs S, Tomlinson I (2013) Germline and somatic polymerase
epsilon and delta mutations define a new class of hypermu-
tated colorectal and endometrial cancers. J Pathol 230:148–153.
doi:10.1002/path.4185
Carvajal-Carmona LG, Cazier JB, Jones AM, Howarth K, Broderick
P, Pittman A, Dobbins S, Tenesa A, Farrington S, Prendergast J
et al (2011) Fine-mapping of colorectal cancer susceptibility
loci at 8q23.3, 16q22.1 and 19q13.11: refinement of associa-
tion signals and use of in silico analysis to suggest functional
variation and unexpected candidate target genes. Hum Mol Genet
20:2879–2888. doi:10.1093/hmg/ddr190
Choi JK, Yu U, Kim S, Yoo OJ (2003) Combining multiple microar-
ray studies and modeling interstudy variation. Bioinformatics
19(Suppl 1):i84–i90
Clayton D, Leung HT (2007) An R package for analysis of
whole-genome association studies. Hum Hered 64:45–51.
doi:10.1159/000101422
Couch FJ, Wang X, McGuffog L, Lee A, Olswold C, Kuchenbae-
cker KB, Soucy P, Fredericksen Z, Barrowdale D, Dennis J et al
(2013) Genome-wide association study in BRCA1 mutation carri-
ers identifies novel loci associated with breast and ovarian cancer
risk. PLoS Genet 9:e1003212. doi:10.1371/journal.pgen.1003212
Day RS, McDade KK, Chandran UR, Lisovich A, Conrads TP, Hood
BL, Kolli VS, Kirchner D, Litzi T, Maxwell GL (2011) Identi-
fier mapping performance for integrating transcriptomics and
proteomics experimental results. BMC Bioinform 12:213.
doi:10.1186/1471-2105-12-213
de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D
(2005) Efficiency and power in genetic association studies. Nat
Genet 37:1217–1223. doi:10.1038/ng1669
Fearon ER (1997) Human cancer syndromes: clues to the origin and
nature of cancer. Science 278:1043–1050
Fisher B, Costantino JP, Wickerham DL, Cecchini RS, Cronin WM,
Robidoux A, Bevers TB, Kavanah MT, Atkins JN, Margolese RG
et al (2005) Tamoxifen for the prevention of breast cancer: current
status of the National Surgical Adjuvant Breast and Bowel Project
P-1 study. J Natl Cancer Inst 97:1652–1662. doi:10.1093/jnci/dji372
Genomes Project C, Abecasis GR, Auton A, Brooks LD, DePristo
MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean
GA (2012) An integrated map of genetic variation from 1,092
human genomes. Nature 491:56–65. doi:10.1038/nature11632
Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P,
Zhang Y, Blankenberg D, Albert I, Taylor J et al (2005) Galaxy:
a platform for interactive large-scale genome analysis. Genome
Res 15:1451–1455. doi:10.1101/gr.4086505
Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thor-
leifsson G, Manolescu A, Rafnar T, Gudbjartsson D, Agnarsson
BA, Baker A et al (2007) Two variants on chromosome 17 confer
prostate cancer risk, and the one in TCF2 protects against type 2
diabetes. Nat Genet 39:977–983. doi:10.1038/ng2062
Haiman CA, Chen GK, Vachon CM, Canzian F, Dunning A, Mil-
likan RC, Wang X, Ademuyiwa F, Ahmed S, Ambrosone CB et al
(2011) A common variant at the TERT–CLPTM1L locus is asso-
ciated with estrogen receptor-negative breast cancer. Nat Genet
43:1210–1214. doi:10.1038/ng.985
Hemminki K, Rawal R, Chen B, Bermejo JL (2004) Genetic epide-
miology of cancer: from families to heritable genes International
journal of cancer. J Int Cancer 111:944–950. doi:10.1002/ijc.20355
Hoggart CJ, Whittaker JC, De Iorio M, Balding DJ (2008) Simulta-
neous analysis of all SNPs in genome-wide and re-sequencing
association studies. PLoS Genet 4:e1000130. doi:10.1371/
journal.pgen.1000130
Hoivik EA, Kusonmano K, Halle MK, Berg A, Wik E, Werner HM,
Petersen K, Oyan AM, Kalland KH, Krakstad C et al (2014)
Hypomethylation of the CTCFL/BORIS promoter and aberrant
expression during endometrial cancer progression suggests a role
as an Epi-driver gene. Oncotarget 5:1052–1061
Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate
genotype imputation method for the next generation of genome-
wide association studies. PLoS Genet 5:e1000529. doi:10.1371/
journal.pgen.1000529
James MA, Vikis HG, Tate E, Rymaszewski AL, You M (2014)
CRR9/CLPTM1L regulates cell survival signaling and is required
for Ras transformation and lung tumorigenesis. Cancer Res
74:1116–1127. doi:10.1158/0008-5472.CAN-13-1617
Jia J, Bosley AD, Thompson A, Hoskins JW, Cheuk A, Collins I,
Parikh H, Xiao Z, Ylaya K, Dzyadyk M et al (2014) CLPTM1L
Hum Genet
1 3
promotes growth and enhances aneuploidy in pancreatic cancer
cells. Cancer Res. doi:10.1158/0008-5472.can-13-3176
Jones AM, Howarth KM, Martin L, Gorman M, Mihai R, Moss L,
Auton A, Lemon C, Mehanna H, Mohan H et al (2012) Thyroid
cancer susceptibility polymorphisms: confirmation of loci on
chromosomes 9q22 and 14q13, validation of a recessive 8q24
locus and failure to replicate a locus on 5q24. J Med Genet
49:158–163. doi:10.1136/jmedgenet-2011-100586
Kaaks R, Lukanova A, Kurzer MS (2002) Obesity, endogenous hor-
mones, and endometrial cancer risk: a synthetic review. Cancer
Epidemiol Biomarkers Prev 11:1531–1543
Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, Rob-
ertson AG, Pashtan I, Shen R, Benz CC et al (2013) Integrated
genomic characterization of endometrial carcinoma. Nature
497:67–73. doi:10.1038/nature12113
Kolquist KA, Ellisen LW, Counter CM, Meyerson M, Tan LK, Wein-
berg RA, Haber DA, Gerald WL (1998) Expression of TERT in
early premalignant lesions and a subset of cells in normal tissues.
Nat Genet 19:182–186. doi:10.1038/554
Kote-Jarai Z, Olama AA, Giles GG, Severi G, Schleutker J, Weis-
cher M, Campa D, Riboli E, Key T, Gronberg H et al (2011)
Seven prostate cancer susceptibility loci identified by a multi-
stage genome-wide association study. Nat Genet 43:785–791.
doi:10.1038/ng.882
Kote-Jarai Z, Saunders EJ, Leongamornlert DA, Tymrakiewicz M,
Dadaev T, Jugurnauth-Little S, Ross-Adams H, Al Olama AA,
Benlloch S, Halim S et al (2013) Fine-mapping identifies mul-
tiple prostate cancer risk loci at 5p15, one of which associ-
ates with TERT expression. Hum Mol Genet 22:2520–2528.
doi:10.1093/hmg/ddt086
Landi MT, Chatterjee N, Yu K, Goldin LR, Goldstein AM, Rotunno
M, Mirabello L, Jacobs K, Wheeler W, Yeager M et al (2009) A
genome-wide association study of lung cancer identifies a region
of chromosome 5p15 associated with risk for adenocarcinoma.
Am J Hum Genet 85:679–691. doi:10.1016/j.ajhg.2009.09.012
Marchini J, Howie B (2010) Genotype imputation for genome-
wide association studies. Nat Rev Genet 11:499–511.
doi:10.1038/nrg2796
McKay JD, Hung RJ, Gaborieau V, Boffetta P, Chabrier A, Byrnes
G, Zaridze D, Mukeria A, Szeszenia-Dabrowska N, Lissowska
J et al (2008) Lung cancer susceptibility locus at 5p15.33. Nat
Genet 40:1404–1406. doi:10.1038/ng.254
Mhawech-Fauceglia P, Wang D, Kesterson J, Clark K, Monhollen
L, Odunsi K, Lele S, Liu S (2010) Microarray analysis reveals
distinct gene expression profiles among different tumor histol-
ogy, stage and disease outcomes in endometrial adenocarcinoma.
PLoS One 5:e15415. doi:10.1371/journal.pone.0015415.s001
Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J,
Milne RL, Schmidt MK, Chang-Claude J, Bojesen SE, Bolla MK
et al (2013) Large-scale genotyping identifies 41 new loci asso-
ciated with breast cancer risk. Nat Genet 45:353–361, 361e351–
352. doi:10.1038/ng.2563
Moreno-Bueno G, Sanchez-Estevez C, Cassia R, Rodriguez-Perales
S, Diaz-Uriarte R, Dominguez O, Hardisson D, Andujar M, Prat
J, Matias-Guiu X et al (2003) Differential gene expression profile
in endometrioid and nonendometrioid endometrial carcinoma:
STK15 is frequently overexpressed and amplified in nonendome-
trioid carcinomas. Cancer Res 63:5697–5702
Painter JN, O’Mara TA, Batra J, Cheng T, Lose FA, Dennis J, Michai-
lidou K, Tyrer JP, Ahmed S, Ferguson K et al (2014) Fine-map-
ping of the HNF1B multicancer locus identifies candidate vari-
ants that mediate endometrial cancer risk. Hum Mol Genet pii:
ddu552 [Epub ahead of print]
Palles C, Cazier JB, Howarth KM, Domingo E, Jones AM, Broderick
P, Kemp Z, Spain SL, Guarino E, Salguero I et al (2013) Ger-
mline mutations affecting the proofreading domains of POLE and
POLD1 predispose to colorectal adenomas and carcinomas. Nat
Genet 45:136–144. doi:10.1038/ng.2503
Petersen GM, Amundadottir L, Fuchs CS, Kraft P, Stolzenberg-
Solomon RZ, Jacobs KB, Arslan AA, Bueno-de-Mesquita HB,
Gallinger S, Gross M et al (2010) A genome-wide association
study identifies pancreatic cancer susceptibility loci on chromo-
somes 13q22.1, 1q32.1 and 5p15.33. Nat Genet 42:224–228.
doi:10.1038/ng.522
Pharoah PD, Tsai YY, Ramus SJ, Phelan CM, Goode EL, Lawrenson
K, Buckley M, Fridley BL, Tyrer JP, Shen H et al (2013) GWAS
meta-analysis and replication identifies three new susceptibility
loci for ovarian cancer. Nat Genet 45:362–370, 370e361–362.
doi:10.1038/ng.2564
Prescott J, McGrath M, Lee IM, Buring JE, De Vivo I (2010) Tel-
omere length and genetic analyses in population-based studies of
endometrial cancer risk. Cancer 116:4275–4282. doi:10.1002/c
ncr.25328
Rafnar T, Sulem P, Stacey SN, Geller F, Gudmundsson J, Sigurdsson
A, Jakobsdottir M, Helgadottir H, Thorlacius S, Aben KK et al
(2009) Sequence variants at the TERT–CLPTM1L locus associ-
ate with many cancer types. Nat Genet 41:221–227. doi:10.1038/
ng.296
Risinger JI, Maxwell GL, Chandramouli GV, Jazaeri A, Aprelikova O,
Patterson T, Berchuck A, Barrett JC (2003) Microarray analysis
reveals distinct gene expression profiles among different histo-
logic types of endometrial cancer. Cancer Res 63:6–11
Saidi SA, Holland CM, Kreil DP, MacKay DJ, Charnock-Jones DS,
Print CG, Smith SK (2004) Independent component analysis of
microarray data in the study of endometrial cancer. Oncogene
23:6677–6683. doi:10.1038/sj.onc.1207562
Sakoda LC, Jorgenson E, Witte JS (2013) Turning of COGS moves
forward findings for hormonally mediated cancers. Nat Genet
45:345–348. doi:10.1038/ng.2587
Salvesen HB, Carter SL, Mannelqvist M, Dutt A, Getz G, Stefans-
son IM, Raeder MB, Sos ML, Engelsen IB, Trovik J et al (2009)
Integrated genomic profiling of endometrial carcinoma asso-
ciates aggressive tumors with indicators of PI3 kinase activa-
tion. Proc Natl Acad Sci USA 106:4834–4839. doi:10.1073/p
nas.0806514106
Setiawan VW, Doherty JA, Shu XO, Akbari MR, Chen C, De Vivo I,
Demichele A, Garcia-Closas M, Goodman MT, Haiman CA et al
(2009) Two estrogen-related variants in CYP19A1 and endome-
trial cancer risk: a pooled analysis in the Epidemiology of Endo-
metrial Cancer Consortium. Cancer Epidemiol Biomarkers Prev
18:242–247. doi:10.1158/1055-9965.EPI-08-0689
Setiawan VW, Haessler J, Schumacher F, Cote ML, Deelman E, Fes-
inmeyer MD, Henderson BE, Jackson RD, Vöckler JS, Wilkens
LR et al (2012) HNF1B and endometrial cancer risk: results
from the PAGE study. PLoS One 7(1):e30390. doi:10.1371/
journal.pone.0030390
Shen H, Fridley BL, Song H, Lawrenson K, Cunningham JM, Ramus
SJ, Cicek MS, Tyrer J, Stram D, Larson MC et al (2013) Epige-
netic analysis leads to identification of HNF1B as a subtype-spe-
cific susceptibility gene for ovarian cancer. Nat Commun 4:1628.
doi:10.1038/ncomms2629
Shen J, Gammon MD, Wu HC, Terry MB, Wang Q, Bradshaw
PT, Teitelbaum SL, Neugut AI, Santella RM (2010) Multi-
ple genetic variants in telomere pathway genes and breast can-
cer risk. Cancer Epidemiol Biomarkers Prev 19:219–228.
doi:10.1158/1055-9965.EPI-09-0771
Shete S, Hosking FJ, Robertson LB, Dobbins SE, Sanson M, Malmer
B, Simon M, Marie Y, Boisselier B, Delattre JY et al (2009)
Genome-wide association study identifies five susceptibility loci
for glioma. Nat Genet 41:899–904. doi:10.1038/ng.407
Spurdle AB, Thompson DJ, Ahmed S, Ferguson K, Healey CS,
O’Mara T, Walker LC, Montgomery SB, Dermitzakis ET,
Hum Genet
1 3
Australian National Endometrial Cancer Study G et al (2011)
Genome-wide association study identifies a common variant
associated with risk of endometrial cancer. Nat Genet 43:451–
454. doi:10.1038/ng.812
Stacey SN, Sulem P, Masson G, Gudjonsson SA, Thorleifsson G,
Jakobsdottir M, Sigurdsson A, Gudbjartsson DF, Sigurgeirsson
B, Benediktsdottir KR et al (2009) New common variants affect-
ing susceptibility to basal cell carcinoma. Nat Genet 41:909–914.
doi:10.1038/ng.412
Supernat A, Lapinska-Szumczyk S, Sawicki S, Wydra D, Biernat W,
Zaczek AJ (2012) Deregulation of RAD21 and RUNX1 expres-
sion in endometrial cancer. Oncol Lett 4:727–732. doi:10.3892
/ol.2012.794
Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, Orr N, Yu K,
Chatterjee N, Welch R, Hutchinson A et al (2008) Multiple loci
identified in a genome-wide association study of prostate cancer.
Nat Genet 40:310–315. doi:10.1038/ng.91
Turnbull C, Rapley EA, Seal S, Pernet D, Renwick A, Hughes D,
Ricketts M, Linger R, Nsengimana J, Deloukas P et al (2010)
Variants near DMRT1, TERT and ATF7IP are associated with
testicular germ cell cancer. Nat Genet 42:604–607. doi:10.1038/
ng.607
Tyrer J, Pharoah PD, Easton DF (2006) The admixture maximum like-
lihood test: a novel experiment-wise test of association between
disease and multiple SNPs. Genet Epidemiol 30:636–643. doi:1
0.1002/gepi.20175
Vignal CM, Bansal AT, Balding DJ (2011) Using penalised logistic
regression to fine map HLA variants for rheumatoid arthritis. Ann
Hum Genet 75:655–664. doi:10.1111/j.1469-1809.2011.00670.x
Wang Y, Broderick P, Webb E, Wu X, Vijayakrishnan J, Matakidou
A, Qureshi M, Dong Q, Gu X, Chen WV et al (2008) Common
5p15.33 and 6p21.33 variants influence lung cancer risk. Nat
Genet 40:1407–1409. doi:10.1038/ng.273
Wang Z, Zhu B, Zhang M, Parikh H, Jia J, Chung CC, Sampson JN,
Hoskins JW, Hutchinson A, Burdette L et al (2014) Imputation
and subset-based association analysis across different cancer
types identifies multiple independent risk loci in the TERT–
CLPTM1L region on chromosome 5p15.33. Hum Mol Genet. doi
:10.1093/hmg/ddu363
Ward LD, Kellis M (2012) HaploReg: a resource for exploring chro-
matin states, conservation, and regulatory motif alterations
within sets of genetically linked variants. Nucleic Acids Res
40:D930–D934. doi:10.1093/nar/gkr917
Wellcome Trust Case Control C (2007) Genome-wide association
study of 14,000 cases of seven common diseases and 3,000
shared controls. Nature 447:661–678. doi:10.1038/nature05911
Yamamoto K, Okamoto A, Isonishi S, Ochiai K, Ohtake Y (2001)
A novel gene, CRR9, which was up-regulated in CDDP-resist-
ant ovarian tumor cell line, was associated with apoptosis. Bio-
chem Biophys Res Commun 280:1148–1154. doi:10.1006/b
brc.2001.4250
Zighelboim I, Mutch DG, Knapp A, Ding L, Xie M, Cohn DE, Good-
fellow PJ (2014) High frequency strand slippage mutations in
CTCF in MSI-positive endometrial cancers. Hum Mutat 35:63–
65. doi:10.1002/humu.22463