Genome-wide association studies in cancer
Douglas F. Easton1,?and Rosalind A. Eeles2,3,4
1Cancer Research UK Genetic Epidemiology Unit, University of Cambridge, Strangeways Research Laboratory,
Worts Causeway, Cambridge CB1 8RN, UK,2Translational Cancer Genetics Team, The Institute of Cancer Research,
15 Cotswold Road, Sutton, Surrey SM2 5NG, UK,3The Royal Marsden NHS Foundation Trust, Downs Road, Sutton,
Surrey SM2 5PT, UK and4The Royal Marsden NHS Foundation Trust, Fulham Road, London SW3 6JJ, UK
Received August 27, 2008; Revised and Accepted September 7, 2008
Genome-wide association studies (GWAS) provide a powerful approach to identify common, low-penetrance
disease loci without prior knowledge of location or function. GWAS have been conducted in five of the com-
monest cancer types: breast, prostate, colorectal and lung, and melanoma, and have identified more than 20
novel disease loci, confirming that susceptibility to these diseases is polygenic. Many of these loci were
detected at low power, indicating that many further loci will probably be detected with larger studies. For
the most part, the loci were not previously suspected to be related to carcinogenesis, and point to new dis-
ease mechanisms. The risks conferred by the susceptibility alleles are low, generally 1.3-fold or less. The
combined effects may, however, be sufficiently large to be useful for risk prediction, and targeted screening
and prevention, particularly as more loci are identified.
All common cancer types aggregate in families, with the
disease being typically 2–4-fold more common in the first
degree relatives of cases of the same type than in the
general population (1,2). Twin studies suggest that this famil-
ial clustering is likely to be largely genetic (3), but for the
most part the underlying genetic loci are not known. Some
of the familial risk can be explained by rare mutations in
high-penetrance genes, first identified in the 1990s, of which
the most important are BRCA1 and BRCA2 for breast and
ovarian cancer, mismatch repair genes for colorectal and
endometrial cancer and CDKN2A for melanoma (4–9).
These mutations, however, explain only a small fraction of
the familial risk. The subsequent failure of genetic linkage
studies to identify further susceptibility genes suggests that
most familial clustering of cancer is due to a combination of
multiple lower penetrance alleles.
Association studies, involving direct testing of genetic poly-
morphisms in large series of cases versus controls, provide a
powerful approach to identify lower penetrance alleles that
cannot be detected by genetic linkage studies, and over the
past decade many groups have tried this approach. Since
early technologies were limited to studying one or a few poly-
morphisms at a time, these studies had to focus on particular
genes or pathways. Typically, studies have concentrated on
candidate genes or pathways suspected to be important in car-
cinogenesis, such as DNA repair, carcinogen metabolism,
cell cycle control and hormone synthesis. They initially
concentrated on polymorphisms (usually single nucleotide
polymorphisms or SNPs) thought to be functionally important.
Gradually, these studies were extended to sets of tagged SNPs
correlated with all known common variants across a gene.
However, despite the fact that many genes have been
studied in association studies, very few well-validated associ-
ations have emerged from this approach [the clear examples
perhaps being NAT2 in bladder cancer and CASP8 D302H in
breast cancer (10,11)]. Additional susceptibility genes in
which rare coding variants are associated with a moderate
cancer risk have, however, emerged through candidate gene
resequencing, including ATM, CHEK2, BRIP1, PALB2 in
breast cancer, and MYH in colorectal cancer (12–17).
More recently, genome-wide association studies (GWAS)
have emerged as a powerful new approach to identifying sus-
ceptibility loci. By utilizing genotyping platforms that can
type hundreds of thousands of SNPs simultaneously, it is poss-
ible to conduct association studies using sets of SNPs that tag
most known common variants in the genome, and hence scan
for associations without prior knowledge of function or pos-
ition (18). Over the past 3 years, results from GWAS have
been published for each of the four commonest cancers in
Western populations: breast, prostate, lung and colorectal,
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Human Molecular Genetics, 2008, Vol. 17, Review Issue 2
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and for malignant melanoma, each reporting well-validated
novel associations. In total, these scans have identified more
than 20 new cancer susceptibility loci. Additional scans are
ongoing in many other cancer types, including cancers of
the haemapoietic system, pancreas, bladder, kidney, testis
and ovary. As a result, one can confidently predict that the
number of cancer susceptibility loci is likely to rise rapidly
over the next 1–2 years.
Results from four GWAS have been published previously.
Easton et al. (19) studied 390 familial breast cancer cases
and 364 controls, using a genome-wide array of over
200 000 SNPs designed by Perlegen Sciences; 12 711 SNPs,
selected on the basis of evidence of association in the genome-
wide scan, were then tested for association in a further 3990
cases and 3916 controls. Finally, 30 SNPs showing evidence
of association after the first two stages were then subjected
to further evaluation in 21 860 cases and 22 578 controls, orig-
inating from 22 studies as part of the international consortium
(BCAC). After these three stages, SNPs in five loci were
associated with disease risk at a ‘genome-wide’ level of sig-
nificanceof association(P , 1027
Cochran-Armitage trend test) that provides strong evidence
of a genuine association (Table 1). Four of these loci
contain known genes. The most strongly associated SNP was
in intron 2 of the FGFR2 gene, a receptor tyrosine kinase
that is amplified and overexpressed in 5–10% of breast
tumours (20,21). The locus on 16q contains a gene TNRC9
and a hypothetical gene LOC643714. The function of
TNRC9 is unknown but the presence of an HMG box motif
suggests that it might act as a transcription factor. The 5q
locus includes MAP3K1, a gene involved in signal transduc-
tion but not previously known to be involved in cancer, and
two other genes MGC33648 and MIER3. The 11p region con-
tains LSP1, an F-actin bundling cytoskeletal protein expressed
in hematopoietic and endothelial cells. Evidence of association
was also found with a SNP in the neighbouring H19 gene, an
imprinted maternally expressed untranslated mRNA closely
involved in regulation of IGF2. The fifth locus was located
in 8q24, in an interval of .110 kb that contains no known
genes. This region also contains loci associated with prostate
cancer and colorectal cancer (Fig. 1; see below).
The CGEMS group detected the association of FGFR2 in a
second genome scan (22). Fine-scale mapping indicates that
this association is likely to be due to one of six variants in
intron 2 (19). These variants are associated with FGFR2
expression in normal breast tissue, and two of the variants
interrupt active transcription factor-binding sites, indicating a
likely biological mechanism (23).
Additional loci were found by the deCode group on 2q and
later on 5p, in a scan of ?1000 unselected breast cancer cases
and the Illumina 317k panel (24,25). The 5p locus includes
MRPS30, a gene involved in apoptosis, which is also close to
FGF10. The 2q region contains no known genes. A further
locus on 6q was identified by Gold et al. (26) based on a scan
of 249 familial Ashkenazi Jewish breast cancer cases. This
region contains two potential candidate genes, ECHDC1 and
RNF146, but the association has yet to be replicated.
Several of the breast cancer loci appear to be associated
with specific subtypes of the disease. In particular, the
FGFR2 association is strongly associated with oestrogen
receptor positive (ERþve) breast cancer, the type of disease
responsive to hormone therapies such as tamoxifen. There is
little association with ER-ve disease (27). Associations with
the MAP3K1, 8q, 2q and 5p loci also appear to be stronger
for ERþve disease. In contrast, the TNRC9 SNP is associated
with both ERþve and ER-ve disease. These observations
show interesting parallels with analyses in BRCA1 and
BRCA2 carriers by the CIMBA consortium (28). These ana-
lyses showed that the FGFR2, TNRC9 and MAP3K1 SNPs
elevate the breast cancer risk in BRCA2 mutation carriers
above that due to the BRCA2 mutation, with a similar relative
risk to that seen in the general population. In contrast, only the
TNRC9 SNP was associated with an elevated breast cancer
risk in BRCA1 mutation carriers. This is consistent with the
observations that breast cancer in BRCA1 carriers is almost
invariably ER-ve, whereas BRCA2 cancers are similar in
subtype distribution to the general population, with a prepon-
derance of ERþve disease (29).
Prostate cancer has been the most productive cancer in terms
of susceptibility loci identified through GWAS, with at least
15 loci identified to date. The first and most important
region to emerge was 8q24. This region first emerged
through linkage studies by the deCode group, followed up
by association analyses (30), and separately through admixture
mapping in African Americans (31). At least three distinct loci
in separate linkage disequilibrium (LD) blocks are present on
8q24 (Fig. 1), all of which have been confirmed in subsequent
GWAS (32–35). Analyses by Haiman et al. (36) identified at
least seven or more independent risk alleles in these blocks,
though fine mapping will required to determine the true
number of ‘causal’ loci. Intriguingly, the susceptibility
alleles at all these loci are commoner in African (Yoruban)
and African Americans, and thus explain at least in part the
higher frequency of the disease in African populations.
The three loci are all distinct from the breast cancer locus in
the same region, which falls in a separate LD block. However,
in one of the three loci, the most significant SNP has also
emerged from GWAS in colorectal cancer, conferring a
similar odds ratio to that for prostate cancer (see below).
This SNP has also been shown to be associated with ovarian
cancer (37). Recent resequencing results indicate that the
SNP rs6983267 is only highly correlated with one other
SNP; this, together with a high degree of conservation,
suggests that this SNP may be causal but this remains to be
proven (38). All these loci fall in a 1.2-Mb region that contains
no known genes. The closest distal gene is the oncogene
c-MYC, leading to suggestions that the susceptibility results
from long range control of myc expression, but this is yet to
be confirmed through expression studies.
Subsequent analyses of GWAS data by the deCode group,
based on a scan of 1500 men with prostate cancer and the
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Illumina 317k array, identified two further loci on 17q. One of
these maps to the HNF1B (TCF2) gene, a gene mutated in
maturity-onset diabetes. The susceptibility allele for prostate
cancer at this locus appears to be protective for type 2 dia-
betes, raising an intriguing possibility that cancer loci more
generally may be related to diabetes or other metabolic
Figure 1. Schematic of the 8q24 region [adapted from (37)].
Table 1. Cancer susceptibility loci identified through GWAS
Per allele ORb
7 ? 10220
3 ? 10211
3 ? 1028
2 ? 10276
3 ? 1029
8 ? 1029
3 ? 1028
6 ? 10210
3 ? 10211
9 ? 10213
3 ? 10215
2 ? 1029
3 ? 10210
9 ? 10229
3 ? 1028
2 ? 10212
2 ? 10218
2 ? 1029
3 ? 10211
4 ? 10214
3 ? 10218
3 ? 10213
6 ? 10210
5 ? 10220
rs1015362/ rs4911414 Haplotype
aReported risk allele frequency in Europeans.
bEstimated per allele odds ratio from the largest available study.
cP for trend, from the first study reporting the replication (not necessarily the current combined evidence).
dStudies first reporting the association.
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disease (39). A second, independent association at HNF1B
26 kb telomeric to the first has recently been found (40).
In the largest study to date, Eeles et al. (34) conducted a
GWAS using 2000 diagnosed prostate cancer cases below the
age of 60 years or with a family history of the disease, and
2000 controls selected for low prostate specific antigen (PSA)
(,0.5 ng/ml), based on the Illumina 550k array. Regions sig-
nificant at P , 1026were followed up in an additional 4000
cases and 4000 controls from the UK and Australia. Using this
strategy, they identified seven novel loci on chromosomes 3,
6, 7, 10, 11, 19 and X (Table 1). A second study by the
CGEMS group, based on 1172 cases from the United States,
also identified the same SNP on chromosome 10 (near MSMB)
and an association on 11q with a SNP closely linked to that
found by Eeles et al. (34). In addition, they identified associ-
ations with SNPs in JAZF1, a transcriptional repressor of
NR2C2 and another locus on chromosome 10 containing the
CTBP2 gene (35). The deCode group have also reported an
association with the same region on X, and found an additional
locus on 2p (41). Aside from the 8q loci, the strongest associ-
start site of MSMB. MSMB codes for PSP94, a member of the
cells of the prostate and secreted into seminal plasma. The
association on chromosome 19 is with an SNP rs2735839
lying downstream of KLK3, the gene coding for PSA, and
upstream of KLK2, coding for another protein secreted by the
prostate, hK2. The association on chromosome 7 is within
LMTK2, a kinase not previously related to cancer. In contrast,
the associations on chromosome 3 and 11 appear to be in gene
poor regions. To date, no clear evidence of subtype specificity
(e.g. with disease aggressiveness) has emerged but more
detailed analyses of disease subtypes are needed.
Five predisposition loci for colorectal cancer have emerged
through GWAS (Table 1). The principal analyses have been
based on two studies from the UK, each of about 1000
cases, one in Scotland based on early onset disease and a
second based on cases with a family history. The first of the
loci found, on 8q24, is identical to one of the loci identified
for prostate cancer, with the same SNP (rs6983267) conferring
a similar odds ratio for both diseases (42,43). Additional loci
have subsequently emerged on 18q21, 15q, 10p14, 11q23
and 8q23.3 (44–47). Several of these loci appear also to
confer susceptibility to colorectal adenomas. There is some
evidence for a stronger association with rectal and colon
cancers for the loci on 11q23, 18q21 and 10p14, an interesting
contrast with the mismatch repair genes (45,46).
Two GWAS for lung cancer have been published to date
(48,49). Both find the same locus on 15q25, suggesting that
this is the most important susceptibility locus for this
disease. This locus contains the nicotinic acetylcholine recep-
tor subunit genes CHRNA3 and CHRNA5, suggesting that sus-
ceptibility may be mediated through smoking behaviour.
Interestingly, in a separate GWAS, Thorgeirsson et al. (50)
report that the same locus is associated with smoking preva-
lence, and suggest that the lung cancer association may be
related to inability to quit smoking. However, both Amos
et al. (48) and Hung et al. (49) report that the association
with lung cancer risk remains after adjusting for smoking.
This suggests that other mechanisms may also be relevant,
but it may also reflect the inability of recorded smoking his-
tories in epidemiological studies to fully adjust for smoking
To date, one melanoma GWAS has been published, based on
an analysis of pooled DNA from 864 cases and 864 controls
(51). This study identified one confirmed locus on 20q. In
addition, several loci associated with eye, hair and skin
colour, or tanning response, known risk factors for melanoma,
have been identified through GWAS (52–54). At least two of
these two loci have been showed to be clearly associated with
the risk of melanoma and basal cell skin cancer (55).
To date, GWAS in five cancer types have identified 28 disease
loci, confirming the polygenic nature of these diseases. Of the
diseases studied, prostate cancer has been the most productive.
To an extent, this may be due to differences in the study
power. However, it may also be related to differences in the
degree of tumour heterogeneity between different cancer
types. Alternatively, it may reflect some fundamental differ-
ences in the pathogenesis of the different cancers. Given that
other cancer types show similar degrees of familial aggrega-
tion, it is highly likely that loci for many other cancers will
be identified through GWAS, given sufficiently large studies.
At this early stage in the GWAS era, it is impossible to draw
strong conclusions about the biological pathways involved. Of
the loci identified so far, most are within blocks containing
genes. Regions such as 8q and 11q for prostate cancer,
however, contain no known genes. To our knowledge, with
the exception of KLK3, none of the genes had previously
been studied in candidate gene association studies, emphasiz-
ing that the candidate gene approach was severely limited by
our incomplete knowledge of the underlying biology. In par-
ticular, none are involved in DNA repair, the major mechan-
ism underlying high-penetrance susceptibility genes. Only
the CHRNA3/5 locus associated with lung cancer, and the
skin pigmentation loci associated with skin cancer, clearly
reflects an environmental risk factor. On the other hand,
several of the loci contain genes (FGFR2, MSMB) that were,
in retrospect, highly plausible candidates. These results, there-
fore, open up exciting new avenues of basic research. Some of
the genes (such LMTK2 for prostate cancer) may also offer
potentially attractive therapeutic targets.
In only one instance, that between FGFR2 and breast
cancer, has the association been narrowed to a limited
number of likely causal variants. Resequencing and conserva-
tion suggest that the SNP rs6983267 on 8q24 associated with
multiple cancers is causal, and the SNP rs10993994 in MSMB
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associated with prostate cancer may well turn out to be causal,
given its position and the strength of the association. Substan-
tial resequencing and fine-mapping efforts will be required to
establish the causal variants for the other loci (Table 1). From
the few loci examined so far, one might speculate that many of
the associations are driven by regulation of gene expression
(either through disruption of promoter sequences or through
more distant control) in signalling pathways related to cell
We can make somewhat more definite statements regarding
the genetic epidemiology. All the SNPs identified to date
confer a modest risk, with all but one of the per allele odds
ratios being ,1.5. All the loci exhibit a dosage effect, with
the excess risk to homozygotes of the risk allele usually
being approximately twice the heterozygote risk. Given the
size of the studies that have been conducted, and the coverage
of the platforms, it is likely that there are few if any loci that
have stronger effects (although the ‘causal’ variant will confer
higher risks than the associated markers, and in some cases
this difference could be substantial). Thus, the strongest loci
identified so far (e.g. the 8q and MSMB loci for prostate
cancer, FGFR2 and TNCR9 for breast cancer) are probably
the strongest loci with common susceptibility variants for
these cancers. On the other hand, some of the loci identified
have odds ratios of 1.1 or less. The current generation of
studies had low power to detect these loci, indicating that
they were found by good fortune. Thus, it is likely that
many more of these weaker loci exist. This is consistent
with the fact that the current sets of loci explain only a
small fraction of the overall familial risk of these cancers
(?5% for breast cancer, 15% for prostate cancer and 4% for
colorectal cancer). A new generation of larger studies, com-
bined analysis across multiple scans, and replication in tens
of thousands of cases will be able to identify many more of
these loci, and such studies are possible at least for the four
commonest cancer types.
So far, only one locus (rs6983267 on 8q24, found through
scans in both prostate and colorectal cancer) has emerged as
being associated with more than one cancer type. This is con-
sistent with the epidemiological observation that most familial
risk of cancer is site specific. The 8q region is perhaps the
most intriguing and important to emerge from cancer
GWAS. Given the absence of known genes in any of the
8q24LD blocks, the associations presumably reflect long-term
range regulation of other genes. In particular, the rs6983267
SNP associated with multiple cancers lies in a highly con-
served segment that has strong regulatory potential and also
contains a putative enhancer (38). However, other mechan-
isms, e.g. effects on DNA structure, might also be involved.
It will be interesting to see if additional regions predisposing
to multiple cancers emerge.
The use of these new susceptibility markers for risk predic-
tion has been much discussed. Individually, the associations
are clearly too weak to be useful on an individual basis.
However, to the extent that they have been studied, the
effects of the different loci appear to combine multiplicatively,
thus generating a risk profile that is approximately log-normal.
Zheng et al. (56) have estimated in a Swedish case–control
study that, based on the five known loci on 8q24 and 17q,
the risk of prostate cancer varies by approximately 4-fold,
rising to 9-fold if family history is also taken into account.
However, this is more limited than it sounds, because the pro-
portion of individuals with all or none of the risk alleles is
minute. Even incorporating all the known risk SNP alleles,
and assuming a multiplicative model, the risk predictive
power is still limited: the top 1% of the population has a
risk that is ?3-fold for prostate cancer and 2-fold for breast
cancer when compared with the mean population risk (57).
However, there is potential for the predictive power to
improve substantially as more variants are found (56,57).
This may in turn have important implications for provision
of cancer screening, e.g. to determine who should have PSA
testing and/or prostate biopsy, colonoscopy, or MRI screening
for breast cancer.
D.F.E. is a Principal Research Fellow of Cancer Research UK.
R.A.E. is funded by HEFCE and the Institute of Cancer
Research. R.A.E. acknowledges research support from the
Biomedical Centre at the Institute of Cancer Research and
the Royal Marsden NHS Foundation Trust.
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