BMC Medical Genetics

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Open AccessA genome-wide association study of breast and prostate cancer in
the NHLBI's Framingham Heart Study
Joanne M Murabito*1,2, Carol L Rosenberg2, Daniel Finger2,
Bernard E Kreger1,2, Daniel Levy1,3, Greta Lee Splansky1, Karen Antman2 andShih-Jen Hwang1,3
Address: 1The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Section of General Internal Medicine
and the Sections of Hematology/Oncology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA and 3National
Heart, Lung, and Blood Institute, Bethesda, MD, USA
Email: Joanne M Murabito* - murabito@bu.edu; Carol L Rosenberg - crosenbe@bu.edu; Daniel Finger - Daniel.finger@bmc.org;
Bernard E Kreger - Bernard.kreger@bmc.org; Daniel Levy - levyd@nhlbi.nih.gov; Greta Lee Splansky - gretalee@bu.edu;
Karen Antman - kha4@bu.edu; Shih-Jen Hwang - hwangs2@nhlbi.nih.gov* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S6

doi:10.1186/1471-2350-8-S1-S6This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S6© 2007 Murabito et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Breast and prostate cancer are two commonly diagnosed cancers in the United States. Prior worksuggests that cancer causing genes and cancer susceptibility genes can be identified.
Methods: We conducted a genome-wide association study (Affymetrix 100K SNP GeneChip) of cancer in thecommunity-based Framingham Heart Study. We report on 2 cancer traits – prostate cancer and breast cancer –in up to 1335 participants from 330 families (54% women, mean entry age 33 years). Multivariable-adjusted
residuals, computed using Cox proportional hazards models, were tested for association with qualifying SNPs (70,987 autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001)
using generalized estimating equations (GEE) models and family based association tests (FBAT).
Results: There were 58 women with breast cancer and 59 men with prostate cancer. No SNP associations
attained genome-wide significance. The top SNP associations in GEE models for each trait were as follows: breast
cancer, rs2075555, p = 8.0 × 10-8 in COL1A1; and prostate cancer, rs9311171, p = 1.75 × 10-6 in CTDSPL. In analysisof selected candidate cancer susceptibility genes, two MSR1 SNPs (rs9325782, GEE p = 0.008 and rs2410373,
FBAT p = 0.021) were associated with prostate cancer and three ERBB4 SNPs (rs905883 GEE p = 0.0002,
rs7564590 GEE p = 0.003, rs7558615 GEE p = 0.0078) were associated with breast cancer. The previously
reported risk SNP for prostate cancer, rs1447295, was not included on the 100K chip. Results of cancer
phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/
gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: Although no association attained genome-wide significance, several interesting associations
emerged for breast and prostate cancer. These findings can serve as a resource for replication in otherpopulations to identify novel biologic pathways contributing to cancer susceptibility.

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Background
Breast and prostate cancer are the most frequently diagnosed cancers in women and men respectively with over
200,000 cases each of new breast and prostate cancer estimated for 2006 in the United States [1]. Furthermore,
prostate cancer is the second leading cause of cancerrelated deaths in men and breast cancer is the second leading cause of cancer-related deaths in women. Family history is a well established risk factor for both breast andprostate cancer providing evidence for underlying genetic
factors contributing to cancer occurrence. Accumulating
research has identified a number of candidate genes andbiologic pathways associated with increased susceptibility
to cancer. However, even the most penetrant mutations,such as in BRCA1 and BRCA2, account for only 5–10% ofcases and are present in <1% of the general population.
Genome-wide association studies (GWAS) provide a comprehensive approach to identification of genetic variants
associated with cancer risk unconstrained by existingknowledge and may permit detection of common genetic
variants each with small associated cancer risk but great
public health impact. Reports from two recent GWAS
demonstrated the importance of this approach with thediscovery of novel loci for breast cancer susceptibility[2,3]. Four SNPs in the FGFR2 gene were strongly associated with breast cancer and the association was confirmed
in a sample of cases and controls derived from three additional studies [3].
We used the Framingham Heart Study (FHS) Affymetrix
100K SNP genotyping resource for GWAS of breast andprostate cancer phenotypes. The FHS offers the advantage
of a prospective longitudinal family-based community
sample with participants who have been well-characterized throughout adulthood with respect to risk factors anddiseases, including cancer. We report results of two complementary strategies to identify genome-wide associations with cancer phenotypes: 1) a simple low p-value
SNP ranking strategy; and 2) 100K SNP associations
within candidate genes and regions previously reported to
be associated with these cancers in humans.

Methods
Study sample
The genotyped study sample comprised 1345 Original
cohort (n = 258) and Offspring (n = 1087) participantsfrom the 330 largest FHS families. The Overview [4] provides further details of this sample. There were 250 participants in the sample with cancer (excluding nonmelanoma skin cancer) including 58 women with breast
cancer, and 59 men with prostate cancer. The Boston University Medical Center Institutional Review Board
approved the examination content of Original Cohort
and Offspring examinations. All participants provided

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written informed consent including consent for genetic
studies.
Cancer phenotype definitions and residual creation
The 5209 Original Cohort participants have been examined biennially since study inception in 1948 and the5124 Offspring Cohort participants (children of the Original Cohort and spouses of the children) have been examined approximately every 4 years since enrollment in
1971. Cancer cases were identified at routine examinations or by health-history updates for participants who
did not attend an examination. Medical records werereviewed by two independent reviewers (BEK, GLS). The
vast majority of cancers were confirmed by pathology
reports; <3.4% of cancer cases were based on death certificate or clinical diagnosis alone. The 1976 World HealthOrganization ICD-O coding was used to classify all primary cancers. Hence, topography, location (subdivisionof site), histology or morphology (cell histopathology),
behavior (degree of malignancy), and grade (histologicalgrading & differentiation) were recorded along with dateof diagnosis. Cancer cases reviewed through December
31, 2005 were included in this study. The proportion of
women and men in the study sample with breast (8%)
and prostate cancer (9%) respectively was similar to thatin the full FHS sample.

Cox proportional hazards models were used to generatemartingale residuals using the PHREG procedure in SAS
to perform the regression analysis of time from study
entry to cancer diagnosis or last contact free of cancer.
Breast cancer was examined in women only and models
were cohort-specific and adjusted for 1) age at entry and2) age, parity, and body mass index at study entry. For
prostate cancer, in men only, models were cohort-specificand adjusted for age at entry.
Genotyping
Affymetrix 100K SNP GeneChip genotyping and the
Marshfield STR genotyping performed by the Mammalian
Genotyping Service http://research.marshfieldclinic.org/
genetics are described in the Overview [4]. SNPs were
excluded if minor allele frequency <0.10 (n = 38062);
genotypic call rate <0.80 (n = 2346); Hardy Weinberg
equilibrium test p < 0.001 (n = 1595). There were 70,987
autosomal SNPs available for analysis after the exclusions.
Statistical Analysis
The statistical methods for genome-wide association analyses are described in detail in the Overview [4]. While
there are various suggested methods for interpretation of
genome-wide significance, we chose to use a conservative(p < 0.05/10-6 = 5 × 10-8) threshold to define genomewide significance for this report.

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Association
All cancer residual traits listed in Table 1 were computed
using Cox proportional hazards models. The full set of
FHS participants with the phenotype were used to create
the residuals. The residuals were used to test for association between the genotyped subset of participants and the
SNPs using family-based association test (FBAT) and generalized estimating equation (GEE) models. FBAT analyses were restricted to at least 10 informative families. The
GEE tests tended to give an excess of very small p-values
over what would be expected (see Overview [4]).

top 500 SNP list. Using this method, 1487 SNPs in FHS
100K (including SNPs with MAF < 0.1) correlated with the
CGEMS top 500 SNPs related to a known gene.
SNPs were annotated with the UCSC genome browser
tables using the May 2004 assembly http://
genome.ucsc.edu/[7,8]. All genes within 60 kb of the top
ranked SNPs were identified. The physical location of the
SNPs was based on Build 35 of Genome for this report;
however, the 100K web browser was based on Build 36.

Results
SNP prioritization
We used several strategies to prioritize SNPs associatedwith cancer traits. First, we used an untargeted approach
whereby SNP associations were ranked according to thestrength of the p-value for each trait. Next we identified
candidate genes reported to be associated with each cancer trait from review of the literature. Candidate genes
were selected by searching PubMed (using susceptibility,
gene, cancer and (breast or prostate) as keywords, lastaccessed 08-15-06), and the Entrez Gene and Online
Mendelian Inheritance in Man resources, as well as recent
text books. All available 100K SNPs in or near the a priori
selected candidate genes were investigated for association
with cancer traits. Finally for prostate cancer, we also
examined SNP associations in the region on chromosome
8 (8q24) previously reported to be associated with prostate cancer in Icelandic families and confirmed in threecase-control series [5] and African American men [6]. Further, for prostate cancer we examined the overlap in SNP
associations in our study and the top 500 ranked SNPs
from the Cancer Genetics Markers of Susceptibility(CGEMS) project sponsored by the National Cancer Institute. Because CGEMS used an Illumina platform for genotyping and the genotyping used in this study was
performed with an Affymetrix platform, the gene_symbolfrom the UCSC annotation was used to link with CGEMS

The cancer phenotypes available in the FHS 100K SNP
resource, including details of the sample size, number ofcancer events, and covariate adjustment for each trait arelisted in Table 1. In this report, we consider only two phenotypes: breast cancer in women (multivariable-adjusted)and prostate cancer in men. Among participants in the
100K sample the mean age at breast cancer diagnosis was
59 years (range 35 to 83 years) in Offspring Cohort
women and 70 years (range 35 to 97 years) in Original
Cohort women; the mean age at prostate cancer diagnosis
was 66 years (range 43 to 85 years) in Offspring Cohort
men and 76 years (range 53 to 95 years) in Original
Cohort men.
For each of the cancer phenotypes, Table 2 provides the
top 15 SNPs ranked in order by lowest p-value for the GEE
models and for the FBAT models (all SNP associations canbe viewed on the web) [9]. None of the SNP associations
achieved genome-wide significance (p < 5 × 10-8) [4].
However, for prostate cancer, the top SNP in GEE models,
rs9311171, is in CTDSPL (CTD {carboxy-terminal
domain, RNA polymerase II, polypeptide A} small phosphatase-like), a gene that may play a role in tumor suppression [10].

Table 1: Cancer Phenotypes for the Framingham Heart Study 100K Analyses

Phenotype*

TraitsNumber ofparticipants

EventsAdjustment (database phenotype variable name)

All Cancer* (excluding
non-melanoma skin cancer)

2

1335

250

Breast Cancer (women)

2

723

58

Prostate Cancer (men)

1

617

59

Cohort- and sex-specific
1. age at study entry (allcancer1)
2. multivariable adjusted for age, body mass index, and cigarette smoking atstudy entry (allcancer2)
Cohort-specific
1. age at study entry (breastcancer1)
2. multivariable adjusted for age, parity, and body mass index at study entry(breastcancer2)
Cohort-specific
1. age at study entry (prostatecancer1)

Residuals from these models were used as traits to test for association with SNP genotypes. All residuals created using Cox Proportional Hazards
regression. Cancers ascertained through December 31, 2005
*All SNP associations for all phenotypes in the table including all cancer are available on the web http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007

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There were several additional associations not listed inTable 2 that were of interest. For prostate cancer, in GEEmodels rs906304 (rank 27, p = 0.000067), is in NCOR2
also known as SMRT. SMRT levels have been reported tobe elevated in prostate cancer cells, and result in suppression of anti-proliferative target gene actions for the vitamin D receptor [11,12]. In FBAT models, for prostatecancer SNP rs255561 (rank 17, p = 0.00039), is near
XRCC4, a gene that plays a role in DNA repair andrs1897676 (rank 50, p = 0.0012), is in PTPRD. Protein
tyrosine phosphatases are signaling molecules involved in
the regulation of a variety of cellular processes including
cell growth, differentiation, mitotic cycle, and oncogenictransformation [13]. For breast cancer in GEE models
rs4146372 (rank 31, p = 0.00007), is near RAB21, SNP
rs9307561 (rank 40, p = 0.0001), is near FAT4, andrs10512849 (rank 46, p = 0.00014), is in FGF10. These
genes appear to play biologic roles in a variety of processes
including tumor growth and suppression [13,14]. In FBAT
models, for breast cancer rs2836391 (rank 46, p =0.0012), is in ERG, an oncogene important in the development of prostate cancer [15,16].
Our second strategy was to identify from the literature
candidate genes implicated in breast and prostate cancer
susceptibility (see Additional data file 1). For prostate cancer, we identified 63 candidate genes. Twenty of these candidate genes had from 1 to 20 SNPs on the 100K chip
whereas the remaining genes had no SNP coverage on thechip. For breast cancer, 75 potential candidate genes wereidentified, 28 of these genes had between 1 and 35 SNPs
on the 100K chip and the remaining candidate genes were
not covered on the chip. Two SNPs in MSR1 (rs9325782,
GEE p = 0.008 and rs2410373, FBAT p = 0.021) were associated with prostate cancer and three SNPs in ERBB4(rs905883 GEE p = 0.0002, rs7564590 GEE p = 0.003,
rs7558615 GEE p = 0.0078) were associated with breast
cancer (Table 3). For prostate cancer, a region on chromosome 8q24 was recently reported to be associated withprostate cancer risk in Icelandic men and confirmed inthree case-control series of men of European ancestry andAfrican American men [5,6]. There were a total of 64 SNPs
on the 100K chip in this 8q24 region (128 to 129.3 Mb
interval). However, the reported risk SNP, rs1447295, was
not included on the 100K chip and none of the 64 available SNPs were in linkage disequilibrium with the risk
SNP. Five other SNPs in this region were associated withprostate cancer with a GEE or FBAT p-value < 0.01 (Table
4).
The National Cancer Institute commenced the CGEMS[17] initiative to conduct genome-wide association studies to identify genetic factors related to prostate and breast
cancer. We examined overlap between the top 500 ranked
SNPs for prostate cancer in CGEMS phase 1a [18] and the

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results of the FHS 100K GWAS analysis for prostate cancer. The physical position of the SNP was used to detect
overlapping associations and the results are shown in
Table 5. Of note, many of the associations in Table 5 arein SNPs with very low minor allele frequencies and theresults are presented according to minor allele frequency.
WWOX gene, a tumor suppressor gene, that has beenreported to play a role in prostate cancer [19], showed evidence of association (rs3751832, p = 0.0009) in our study
sample.

Discussion
Breast and prostate cancer are the two most frequently
diagnosed cancers in the United States and result in substantial morbidity and mortality [1]. A number of breastand prostate cancer susceptibility genes and chromosomal regions have been identified [2,3,5,20-34]. However, currently known genes account for only a fraction of
the familial aggregation of breast cancer [25] and few
prostate cancer susceptibility genes have even been identified. Risk for these cancers is likely mediated through variation in many genes, each conferring a relatively small
risk for the disease. Genome-wide association studies provide an opportunity to discover novel genes and pathways
that play a causal role in cancer occurrence and in turn
may lead to new therapies for the prevention and treatment of cancer. Finding genetic associations with breastand prostate cancer risk that are robust across multiple
studies may facilitate the identification of high risk individuals who can be targeted for early screening and preventive interventions.
We report GWAS results for breast cancer and prostatecancer phenotypes in a community-based sample ofadults from two generations of the same families.
Although none of the SNP associations achieved genomewide significance in GEE or FBAT models, this resource
has the potential to detect novel cancer susceptibility
genes and to explore the relevance of promising candidate
gene associations to human cancer. Our results can becompared to those from other genome-wide association
studies such as the National Cancer Institute's CGEMS[17]. Although the two studies used different genotyping
platforms limiting overlap in the SNPs examined, we wereable to determine the physical position of the SNPs. Using
this strategy, SNPs in the ERRB4 gene (CGEMS DSSNP_ID
rs2371438 and FHS 100K SNP rs10497958) were associated with prostate cancer. ErbB proteins are widely
expressed in prostate cells [35] and may play a role intumor development, growth and progression in humanprostate cancer [36,37]. We also examined the 8q24
region previously associated with prostate cancer risk.
CGEMS investigators recently reported a second independent risk SNP (rs6983267) within the 8q24 strongly
associated with prostate cancer [34]. The Affymetrix 100K

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Table 2: Cancer Phenotypes for FHS 100K Project: Results of Association Analyses*

2a. GEE results ranked by p-value
SNP

Chr

Physical Position

GEE p-value

FBAT p-value

Gene region(within 60 kb)

17
5
12
18
13
7
2
21
15
5
7
5
1
3
11

45,629,290
163,821,858
72,276,104
51,815,280
94,672,957
66,503,417
59,410,998
30,099,382
46,687,878
163,822,784
25,846,789
146,185,979
229,808,093
178,681,862
27,946,695

8.3 × 10-8
5.0 × 10-7
6.6 × 10-7
9.66 × 10-7
1.9 × 10-6
2.7 × 10-6
4.7 × 10-6
6.0 × 10-6
6.0 × 10-6
1.2 × 10-5
1.3 × 10-5
1.7 × 10-5
1.9 × 10-5
2.8 × 10-5
3.0 × 10-5

0.157
0.114
0.052
0.287
0.072
0.590
0.077
0.064
0.742
0.058
0.245
0.112
0.470
0.189
0.066

COL1A1

3
13
6
5
3
8
3
14
3
11
10
5
2
3
11

37,971,481
102,726,008
51,774,590
83,021,495
146,656,486
111,210,785
175,020,343
29,676,177
146,619,475
15,789,323
127,209,365
119,954,204
128,292,061
174,963,409
15,,790,399

1.8 × 10-6
1.8 × 10-6
3.0 × 10-6
3.1 × 10-6
5.1 × 10-6
1.3 × 10-5
1.8 × 10-5
1.9 × 10-5
2.1 × 10-5
2.2 × 10-5
2.4 × 10-5
2.8 × 10-5
3.0 × 10-5
3.4 × 10-5
3.7 × 10-5

0.392
0.071
0.285
0.305
0.002
0.002
0.317
0.019
0.002
0.196
0.028
0.165
0.786
0.201
0.223

Chr

Physical Position

GEE p-value

FBAT p-value

5
4
4
2
20
4
9
16
1
4
7
9

180,307,504
137,449,995
109,823,951
122,873,276
59,025,355
90,503,827
101,538,523
7,368,424
229,359,076
84,822,792
145,895,727
91,348,121

0.010
0.008
0.001
0.027
0.132
0.159
0.807
0.475
0.418
0.954
0.085
0.017

8.4 × 10-5
9.5 × 10-5
1.2 × 10-4
1.9 × 10-4
2.0 × 10-4
2.2 × 10-4
2.3 × 10-4
2.9 × 10-4
3.1 × 10-4
3.7 × 10-4
3.9 × 10-4
4.5 × 10-4

BREAST CANCER
rs2075555
rs6556756
rs1154865
rs1978503
rs1926657
rs10263639
rs10490113
rs458685
rs1876206
rs9314033
rs10486490
rs9325024
rs1294255
rs10513754
rs10501093

ABCC4

GRIK1
FBN1

PPP2R2B

KIF18APROSTATE CANCER
rs9311171
rs1529276
rs10498792
rs4466137
rs345013
rs10505137
rs1873038
rs8019932
rs344985
rs1920676
rs10511206
rs1037569
rs2109312
rs1352416
rs1440714

CTDSPLPKHD1
HAPLN1

NLGN1

WDR33
NLGN1

2b. FBAT results ranked by p-value
SNP

Gene Region(within 60 kb)

BREAST CANCER
rs7711990
rs1451125
rs4266352
rs6720918
rs6101183
rs9307064
rs10512287
rs7190881
rs2224402
rs10516690
rs10487920
rs6479347

GRIN3A
C1orf57
CNTNAP2

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Table 2: Cancer Phenotypes for FHS 100K Project: Results of Association Analyses* (Continued)

rs1924587
rs2059273
rs2822669

13
16
21

96,195,380
5,991,144
14,752,449

0.020
0.005
0.029

4.6 × 10-4
4.9 × 10-4
5.7 × 10-4

HS6ST3
A2BP1
SAMSN1

6
1
9
1
1
17
8
5
4
4
20
5
8
1
5

56,402,241
170,274,521
10,930,127
234,250,153
176,253,589
53,562,730
3,020,222
82,386,566
137,063,555
167,317,424
37,450,516
68,064,834
16,758,362
28,993,331
101,539,543

0.109
0.007
0.633
0.159
0.005
0.552
0.054
0.040
0.010
0.049
0.057
0.657
0.057
0.239
0.051

3.4 × 10-5
4.0 × 10-5
6.6 × 10-5
1.1 × 10-4
1.4 × 10-4
3.2 × 10-4
3.6 × 10-4
3.9 × 10-4
3.9 × 10-4
4.2 × 10-4
4.4 × 10-4
4.6 × 10-4
5.1 × 10-4
5.3 × 10-4
5.4 × 10-4

DST
SLC9A11

PROSTATE CANCER
rs657057
rs6691482
rs4378061
rs2056387
rs2274626
rs181247
rs7827348
rs255561
rs1504858
rs954709
rs2179443
rs35239
rs477516
rs595725
rs10515322

RYR2
NPHS2
OR4D1|OR4D2
XRCC4

OPRD1
SLCO4C1*Autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001, and ≥10 informative families for
FBAT. The physical location of the SNPs was based on Build 35 of Genome; however, the 100K web browser was based on Build 36

GeneChip did not include either of the previously
reported risk SNPs; however, we did identify five other
SNPs in this region associated with prostate cancer. The
underlying biologic mechanism mediating prostate cancer risk associated with the SNPs and chromosomal region
remains unknown. A two-stage approach, genome-wide
association followed by selective genotyping of SNPs withsuggestive evidence of association, may provide an efficient strategy for pursuing initial genome-wide results[2,38,39].

and used the residual traits for the subset of individuals
genotyped using the 100K Affymetrix GeneChip to test for
association with the SNPs in linear regression models.
Residual traits from Cox models typically are not ideally
distributed for linear regression models, but our adjustment method using the full Framingham sample precludes the testing of SNP associations with cancer traits
using Cox models. Second, we had a small number of cancer events (250 all cancer cases, 58 breast cancer cases and
59 prostate cancer cases) limiting our ability to detect SNP
associations. In a recent small GWAS of age-related macular degeneration that included 96 cases and 50 controls,
an association with the CFH gene was identified [40] andconfirmed in larger studies [41-43]. However, in thatreport, individuals homozygous for the CFH risk allele
had a sevenfold increased likelihood of age-related macular degeneration [40]. It is very unlikely that common
genetic variants for cancer phenotypes will confer a risk
for cancer susceptibility of that magnitude. For example,
the odds ratio associated with the risk marker identified

Several important limitations merit comment. First, thisstudy used cancer cases identified through surveillance ofa multigenerational community-based sample. The
enrollment and examination of Original Cohort and Offspring Cohort participants began years before DNA collection occurred. Thus, a survival bias may have been
introduced. Our cases may be comprised of early-staged
and less lethal cancers. To address this potential bias, we
adjusted for covariates using the full Framingham sample,

Table 3: All SNP Associations within Selected Breast and Prostate Candidate Genes (up to 60 kb)

Associations within Candidate Genes Selected by Cancer Type: FBAT or GEE p-value < 0.01
Cancer Trait

Gene

SNP

Chr

PhysicalPosition

GEE p-value

FBAT p-value

Breast cancer
Breast cancer
Breast cancer
Prostate cancer
Prostate cancer

ERBB4
ERBB4
ERBB4
MSR1
MSR1

rs905883
rs7564590
rs7558615
rs9325782
rs2410373

2
2
2
8
8

213,013,976
213,213,406
212,771,655
16,134,844
15,968,877

2.0 × 10-4
0.00320.008
8.2 × 10-40.014

0.85170.25670.84570.34480.02051

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Table 4: SNPs in the Chromosome 8q24 region Associated with Prostate Cancer:GEE or FBAT p-value < 0.01

SNP

Physical Position

Distance from
reported marker

MAF

GEE p-value

rs7001069
rs10505483
rs1562871
rs10505474
rs10505506

128179828
128194377
128470954
128486686
129114473-253268-23871937858
53590
6813770.01880.02230.16960.406
0.3275

3.0 × 10-5
4.0 × 10-5
0.166180.00995
0.00829

for prostate cancer in region 8q24 was 1.72 in the combined Icelandic sample [5]. Furthermore, the associations
between prostate cancer and the SNPs with low minor
allele frequency (Table 5) are likely to be false positive
associations given the small number of prostate cancer
cases in our sample. Third, the 100K Affymetrix GeneChip
provides limited coverage of the genome; many of our apriori candidate genes did not have any SNP coverage on
the chip and coverage of some candidate genes that were
present on the chip was suboptimal. Importantly, the replicated risk SNP, rs1447295, for prostate cancer [5] was
not included on the chip. NHLBI has committed funds fora 550 K genome-wide scan on all FHS participants. This
will enable us to confirm our initial 100K SNP associations in a larger sample with a greater number of cancer
cases and with denser coverage of the genome. We did not

FBAT p-value

0.393220.32697
0.003280.01210.92941

examine epistasis or gene-environment interactions
which may modify the associations noted in this study.
Lastly, most of our associations are likely to be due tochance. Replication studies are needed to determine if anyof the results we report are indicative of true associations.
It is important that our data be used in conjunction withdata from other samples given the high probability offalse positive associations.

Conclusion
In summary, the untargeted genome-wide approach todetect genetic associations for cancer traits provides anopportunity to identify novel biologic pathways related tocancer occurrence and to direct future study of candidate
genes that hold the most promise for relevance to cancer
risk in humans. Enhancing our understanding of theTable 5: Prostate Cancer SNP Associations Common to Both CGEMS Top 500 Ranked SNPs and FHS 100K SNPs
CGEMS Phase 1a
DBSNP_ID

Chr

Physical Position

FHS 100KCGEMS p-value

CGEMS
Rank

100K SNP

PhysicalPosition

GEE p-value

FBAT p-value

MAF

NearestGene

0.00024100
0.00024100
0.00041100
0.00041800
0.00092200
0.00111000
0.00127400
0.00128500
0.00144900
0.00154600
0.00158700

85
85
137
141
295
350
395
401
445
468
478

rs3794889
rs4801149
rs2834645
rs10492797
rs4418248
rs986831
rs254315
rs10497958
rs10505624
rs10515347
rs10512920

51,212,935
51,214,227
35,109,556
24,122,453
3,194,181
40,594,892
82,313,913
212,771,465
135,607,862
102,950,833
7,455,561

0.017
0.044
0.009
0.036
1.8 × 10-4
0.009
7.1 × 10-4
0.055
4.9 × 10-4
0.006
0.009

0.799
0.983
0.293
0.407
0.056
0.094
0.011
0.047
0.007
0.062
0.647

0.224
0.221
0.206
0.175
0.109
0.407
0.141
0.164
0.147
0.354
0.244

TCF4
TCF4
RUNX1
PRKCB1
SDK1
PTPRT
CDH13
ERBB4
ZFAT1
NUDT12ADCY20.00055700
0.00057000
0.00057000
0.00060200
0.00061900
0.00064700
0.00065000
0.00079900

181
184
184
193
196
209
211
257

rs10486031
rs10520247
rs10520246
rs10520880
rs10513681
rs7989050
rs10487577
rs10514443

14,258,703
79,693,452
79,693,324
22,634,003
171,624,010
97,680,299
2,583,719
77,353,386

1.7 × 10-5
2.8 × 10-5
1.5 × 10-4
8.5 × 10-5
7.8 × 10-7
6.0 × 10-6
2.0 × 10-7
9.3 × 10-4

0.759
0.240
0.198
0.356
0.402
0.258
N/A
N/A

0.032
0.065
0.050
0.065
0.025
0.014
0.012
0.008

DGKB
CTNNA2CTNNA2
CDH12
CLDN11
FARP1
GNA12
WWOX

Minor Allele Frequency ≥10%
rs3017183
rs3017183
rs2253319
rs392715
rs10256504
rs6102912
rs4782742
rs2371438
rs6577648
rs9327886
rs6555491

18
18
21
16
7
20
16
2
8
5
5

51,319,241
51,319,241
35,109,916
24,023,323
3,516,796
40,636,349
81,573,151
212,791,037
135,555,688
102,931,646
7,799,792

Minor Allele Frequency <10%rs38276rs216666
rs216666
rs1261256
rs7610584
rs7329659
rs208354
rs3751832

7
2
2
5
3
13
7
16

14,078,240
80,745,015
80,745,015
22,490,853
171,635,885
97,872,564
2,554,104
77,771,773

CGEMS = Cancer Genetics Markers of Susceptibility; MAF = minor allele frequency; N/A= not available, less than 10 informative families for FBATPage 7 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S6

mechanisms responsible for cancer susceptibility may inturn identify novel strategies for early detection, prevention, and treatment of breast and prostate cancers. These
data serve as a resource for replication in other population-based samples.

http://www.biomedcentral.com/1471-2350/8/S1/S6

References
1.
2.

Abbreviations
CGEMS = cancer genetics markers of susceptibility; FBAT
= family-based association test; FHS = Framingham Heart
Study; GEE = generalized estimating equations; GWAS =genome-wide association study; ICD-O = internationalclassification of diseases for oncology; MAF = minor allele
frequency; SNP = single nucleotide polymorphism.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
All authors have made substantial contributions to conception and design or acquisition of phenotypic data.
JMM, SJW, DF, DL, CLR contributed to the analysis andinterpretation of data. JMM, CLR, DL, DF, and SJW havebeen involved in drafting the manuscript or revising it critically for important intellectual content and all authors
have read and approved the final manuscript.

Additional material
Additional file 1
List of selected candidate genes for breast and prostate cancer.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S6-S1.doc]

3.

Acknowledgements
We thank Drs Emelia J. Benjamin and Martin G. Larson for their participation in the Framingham Heart Study Cancer Phenotype Working Groupand for their contribution to the conception, design and interpretation of
the data and comments on the drafting of the manuscript.

4.

We thank the FHS participants for their ongoing participation and dedication to the study making this work possible.
NHLBI's Framingham Heart Study is supported by contract number N01HC-25195. FHS 100K analyses were conducted using the Boston University. Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR
(National Center for Research Resources) Shared Instrumentation grant
1S10RR163736-01A1 http://www.bu.edu/dbin/sph/departments/biostatis
tics/linga_publications.php.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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