University of Southern California Dissertations and Theses

Screening and association testing of coding variation in steroid hormone coactivator and corepressor genes in relationship with breast cancer risk in multiple populations

(USC Thesis Other)

Screening and association testing of coding variation in steroid hormone coactivator and corepressor genes in relationship with breast cancer risk in multiple populations

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content
SCREENING AND ASSOCIATION TESTING OF CODING VARIATION IN
STEROID HORMONE COACTIVATOR AND COREPRESSOR GENES IN
RELATIONSHIP WITH BREAST CANCER RISK IN MULTIPLE POPULATIONS

by

Rachel Rose Garcia

A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

May 2009

Copyright 2009 Rachel Rose Garcia

ii

Table of Contents

List of Tables iii

Abstract iv

Introduction 1

Materials and Methods 4
Study Population 4
Polymorphism Discovery and Laboratory Methods 5
Statistical Analyses 8

Results 9
Discovery of Coding Variation 9
Allelic Associations with Breast Cancer Risk 17

Discussion 23

References 27

iii
List of Tables

Table 1: Candidate Estrogen Receptor Coactivator and
Corepressor-related Genes 3

Table 2: The Range of Minor Allele Frequency and Association
with Breast Cancer Risk of Each Variant 10

Table 3: Ethnic Specific Minor Allele Frequencies, Hardy-
Weinberg Equilibrium, and Association of Each
Variant with Breast Cancer Risk 12

Table 4: Descriptive Characteristics Among Breast Cancer Cases
and Controls in the Multiethnic Cohort 18

Table 5: Effect Modification by Body Mass Index (kg/m
2
) 20

Table 6: Effect Modification by Age at Menarche (years) 21

Table 7: Effect Modification by Hormone Therapy Among
Postmenopausal Women 22

iv
Abstract

Increased levels of steroid hormones have been implicated in the etiology of
breast cancer. In a multiethnic panel we sequenced coding regions of 17 candidate
coactivator and corepressor genes thought to interact with one’s susceptibility to steroid
hormone levels. Using unconditional logistic regression we evaluated whether non-
synonymous single nucleotide polymorphisms (SNPs) in these 17 genes were associated
with breast cancer risk. While we did not find more than expected statistically significant
associations of these SNPs with breast cancer risk in our combined, ethnic-specific, and
phenotypic analyses, further studies should still be done to reproduce our findings as well
as hopefully provide greater insight to the association of the rare SNPs and breast cancer
risk. Survival analyses and mutational molecular sensitivity studies of the amino acid
changes imposed by these SNPs, by measuring response to various breast cancer
treatments, might also prove to be useful for improving phenotypic patient treatment.

1
Introduction

Various polymorphisms/mutations have been shown to be associated with one’s
susceptibility to breast cancer. However, no more than 25% of familial breast cancers are
associated with such genes (for example BRCA1 and BRCA2). Although common
genetic polymorphisms may play a vital role in the development of breast cancer,
candidate gene studies have produced little or conflicting results of functionally relevant
missense polymorphisms and their association with breast cancer risk. Many techniques
have been used to assess genetic variation in candidate genes and breast cancer risk, such
as haplotyping, genome wide scans, and sequencing.
Previous studies in steroid hormone carcinogenesis of breast cancer have shown
that the risk of breast cancer is correlated with lifetime exposure to endogenous and
exogenous steroid hormones (Henderson et al). Continuous high levels of estrogens are
thought to enhance cell proliferation in breast tissue leading to an increase in possible
mutations. However, circulating steroid hormone levels seem to only account for a small
percentage of differences in breast cancer risk. Inherited polymorphisms and acquired
somatic mutations in steroid hormone coactivator and corepresssor genes combined with
one’s endogenous steroid hormone levels may play important roles in steroid hormone
carcinogenesis. Identifying genetic polymorphisms associated with breast cancer risk
could provide a useful breast cancer risk screening panel allowing for earlier preventive
measures as well as aid in the identification of new molecular targets for therapeutic
intervention.
In our present study, we systematically screened the coding exons of steroid
hormone receptor coactivator and corepressor genes in an attempt to identify potentially

2
functional polymorphisms that may serve as genetic markers of breast cancer risk. We
targeted 17 candidate genes (Table 1) suggested to influence transcriptional activation by
steroid hormone receptors (PGR, ERα, ERβ) through direct binding to these receptors or
through interactions with other well characterized coactivator/corepressor protein
complexes (EA1 Binding Protein p300 [EP300], cyclin D1 [CCND1], non-metastatic
cells 1, protein (NM23A) [NME1], nuclear receptor coactivator 1 [NCOA1], nuclear
receptor coactivator 2 [NCOA2], nuclear receptor coactivator 3 [NCOA3], SWI/SNF
related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2
[SMARCA2], SWI/SNF related, matrix associated, actin dependent regulator of
chromatin, subfamily a, member 4 [SMARCA4], coactivator-associated arginine
methyltransferase 1 [CARM1], forkhead box A1 [FOXA1], N-methylpurine-DNA
glycosylase [MPG], nuclear receptor co-repressor 1 [NCOR1], nuclear receptor co-
repressor 2 [NCOR2], calcium binding and coiled-coil domain 1 [CALCOCO1], protein
arginine methyltransferase 1 [PRMT1], peroxisome proliferator-activated receptor
binding protein [PPARBP], CREB binding protein [CREBBP]).

3
Table 1: Candidate Estrogen Receptor Coactivator and Corepressor-related Genes
Gene
Sequenced
Gene Name Chromosome NM Number
Total
Exons (Coding)
EP300 EA1 Binding Protein p300 22q13 NM_001429 31(31)
CCND1 cyclin D1 11q13 NM_053056 5(5)
NME1 non-metastatic cells 1, protein (NM23A) 17q21 NM_000269 5(4)
NCOA1 nuclear receptor coactivator 1 2p23 NM_003743 21(19)
NCOA2 nuclear receptor coactivator 2 8q13 NM_006540 23(21)
NCOA3 nuclear receptor coactivator 3 20q13 NM_181659 23(21)
SMARCA2
SWI/SNF related, matrix associated, actin dependent regulator of
chromatin, subfamily a, member 2
9p24 NM_003070 34(33)
SMARCA4
SWI/SNF related, matrix associated, actin dependent regulator of
chromatin, subfamily a, member 4
19p13 NM_003072 35(34)
CARM1 coactivator-associated arginine methyltransferase 1 19p13 NM_199141 16(16)
FOXA1 forkhead box A1 14q21 NM_004496 2(2)
MPG N-methylpurine-DNA glycosylase 16p13 NM_002434 5(4)
NCOR1 nuclear receptor co-repressor 1 17p11-p12 NM_006311 46(45)
NCOR2 nuclear receptor co-repressor 2 12q24 NM_006312 48(47)
CALCOCO1 calcium binding and coiled-coil domain 1 12q13 NM_020898 15(14)
PRMT1 protein arginine methyltransferase 1 19q13 NM_001536 11(11)
PPARBP peroxisome proliferator-activated receptor binding protein 17q12 NM_004774 17(17)
CREBBP CREB binding protein 16p13 NM_004380 31(31)

4

Materials and Methods

Study Population: The Multiethnic Cohort Study (MEC) was initiated between
1993 and 1996 and was developed as a population-based prospective cohort of European
Americans and many underrepresented minorities to study diet and cancer. Subjects were
sampled from various ethnic groups – African-Americans and Latinos from California
(mainly Los Angeles) and Native Hawaiians, Japanese-Americans, and European
Americans in Hawaii – as well as diverse socioeconomic strata (Kolonel et. al.). State
driver’s license files in Hawaii and California were used to obtain study subjects. These
files included a majority of the residents in each area, an array of socioeconomic status,
as well as information on age and sex. Additionally, in Hawaii, state voter’s registration
files were used to obtain names that were not included in the state driver’s license files –
this was especially the case for Japanese-American women. In California, Health Care
Financing Administration (HCFA) files were used to obtain additional African-American
subjects.
Once the study population was assembled mailings of a pre-validated 26-page
self-administered baseline questionnaire were sent out to those 45 to 75 years of age. The
questionnaire obtained general demographic information such as gender, age, ethnicity,
prior medical conditions, family history of various cancers, dietary exposures, smoking,
alcoholic beverage consumption, physical activity, body mass index (BMI), and for
women specifically reproductive history and hormone use. In the cohort, incident cancer
cases are identified annually through cohort linkage to population-based cancer
Surveillance, Epidemiology, and End Results (SEER) registries in Hawaii and Los
Angeles county as well as to the California state cancer registry. Information on stage of

5

disease at the time of diagnosis as well as estrogen and progesterone receptor status were
also obtained through these registries.
Blood sample collection in the MEC began in 1994 and was targeted at incident
breast cancer cases as well as a random sample of study participants who served as
controls for genetic analyses. In this study of breast cancer, incident cases are defined as
those diagnosed with invasive breast cancer after enrollment through December 31, 2002.
Cases are between 45 to 81 years of age and consist primarily of postmenopausal women.
Cases excluded women with a previous diagnosis of breast cancer identified by SEER or
self-report. Controls are women without a breast cancer diagnosis through December 31,
2002. The controls were frequency matched to cases based on their age at blood draw,
ethnicity, and the case’s age at diagnosis in 5 year intervals. This nested case-control
study of breast cancer consists of 1,612 invasive breast cancer cases and 1,961 controls
(n, cases / n, controls: African Americans, 345/426; Native Hawaiians, 108/290; Japanese
Americans, 425/419; Latinas 334/386; European Americans, 400/440).
Polymorphism Discovery and Laboratory Methods: The study focused on
identifying and the association testing of variants with minor allele frequencies as low as
approximately 1% overall or in any one of the five racial/ethnic populations in the MEC.
Polymorphism discovery was performed by sequencing the coding exons and splice-site
regions of candidate genes in a multiethnic panel of 95 women with advanced breast
cancer from the MEC, comprised of 19 subjects of each ethnic group. Advanced breast
cancer is defined as invasive/non-localized cancer with SEER stage ≥2. Advanced cases
were targeted for sequencing to increase the likelihood of detecting variants that would
be biologically associated with breast cancer. This panel was selected to have ≥85%

6

power to detect a potentially functional variant of approximately 5% frequency (2 of 38
chromosomes) in any one population or an overall frequency of approximately 1% (2 of
190 chromosomes).
DNA was extracted from the buffy coat fractions of the blood for all case-control
samples in this multiethnic panel. DNA extraction used the uniform method for the
Qiagen QiaAMP Blood Kit (Valencia, CA). All DNA samples were whole-genome
amplified (WGA) by Molecular Staging Incorporated following their regular protocol
(New Haven, CT).
Putative functional variants (i.e. missense, nonsense or splice site SNPs) observed
>1 individual (a minimum of 2 out of the 190 chromosomes) were targeted for
association testing. For variants that were observed in only one individual, we sequenced
an additional 95 cases of that racial/ethnic population. This extra sequencing was
performed to determine whether the variant is extremely rare and/or may have been
introduced during the WGA process which has an error rate of 9.5 x 10
-6
(Paez et al). The
variants confirmed by the additional ethnic-specific sequencing, variants observed in ≥1
of the 190 chromosomes and ≥2 out of 228 chromosomes (approximately 1%), were also
further examined in relationship with breast cancer risk.
Bi-directional sequencing was performed on the ABI 3730xl DNA Analyzer
(Applied Biosystems, Foster City, CA). Gene and exon specific PCR primers were
obtained from NCBI Probe Database
(http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=search&db=probe) when available, and
PCR conditions are according to the VariantSEQr Resequencing System protocol
(Applied Biosystems, Foster City, CA). In the event that PCR primers were not available

7

(13 exons out of 353 targeted exons) they were designed using a primer design webpage
(http://seq.yeastgenome.org/cgi-bin/web-primer) with at least 50 bases upstream and
downstream from the targeted exon. In the instance that an exon exceeded approximately
550 bases or sequencing did not yield analyzable results, internal primers were designed.
Custom PCR primer conditions varied by annealing temperature as well as the number of
PCR cycles. Sequencing primers were typically universal primers obtained from ABI or
internal primers, as mentioned previously. Sequencing purification was performed using
DyeDX 96 columns (Qiagen, Valencia, CA) following their standard protocol.
PolyPhred was used for analyzing sequence traces and variation discovery
(http://droog.mbt.washington.edu/PolyPhred.html). All chromatograms were assembled
into contigs and variant sites were inspected in Consed
(http://bozeman.mbt.washington.edu/consed/consed.html). NCBI sequences (HG build
18) were used as the reference sequences when examining traces and to identify variants.
The Phred quality score, the log-transformed error probability (Q=-10 log
10
(P
e
)) (Ewing
et al), of each trace was examined from 10 bases 5′ through 10 bases 3′ of each exon and
calculated for each gene. Of the 17 candidate genes, we successfully sequenced 346 of
355 coding exons (97.5%), 327 exons in both the forward and reverse directions, but only
19 exons in 1 direction. On average, each amplicon (consisting of at least part of an exon)
was successfully sequenced in 94 of the 95 sequencing subjects. The average Phred
quality score was determined to be 46.6 for all exons sequenced. Variants were
confirmed and recorded only if present in the forward and reverse sequences. Each
variant was confirmed by two observers.

8

Genotyping of all common coding variants, in this nested breast cancer case-
control study was performed using the allelic discrimination assay. TaqMan genotyping
primers and probes were ordered through ABI using Assays-by-Design. A working assay
could not be designed for 5 common variants found via sequencing, thus we were unable
to genotype them (Gly227Glu in FOXA1; Tyr19Cys, Pro975Ser, and Pro2008Ser in
NCOR2; Gly1416Ala in SMARCA2). However, we were able to determine that
Pro2008Ser in NCOR2 was in perfect linkage disequilibrium (LD) with Ala2007Thr in
NCOR2 by sequencing 69-84 control subjects from each racial/ethnic group. Duplicate
quality controls were included for all genotyping assays on and across plates to assess the
reliability and reproducibility of each genotyped assay (average concordance was 99.8%).
Statistical Analyses: Hardy-Weinberg Equilibrium (HWE) testing was
performed using the standard χ
2
method for each SNP. Unconditional logistic regression
was used to assess the association of each variant with breast cancer risk. Co-dominant
effects of each variant were examined in both ethnic-specific and ethnic-pooled analyses,
treating the common (i.e. homozygous wildtype) genotype as the reference group.
Heterogeneity of effects across populations was tested using an interaction term
consisting of ethnicity and variant. We also examined effect heterogeneity by breast
cancer phenotypes (e.g. stage and estrogen receptor [ER] status). These analyses were
performed using the standard case-control approach, limiting the cases to those with a
specific phenotype (i.e. ER+ cases) and all controls. We also examined effect
modification by established breast cancer risk factors that are associated with steroid
hormone exposure: body mass index (kg/m
2
) among postmenopausal women, use of
hormone therapy, and age at menarche. Tests for interactions were performed using the

9

Likelihood Ratio Test. All statistical analyses were done using SAS version 9.0 (Cary,
NC).
Results
Discovery of Coding Variation: Sequencing the coding regions of our 17
candidate genes yielded 43 non-synonymous SNPs, 1 synonymous SNP at a splice site, 1
insertion/deletion, and 3 poly-glutamine repeat polymorphisms. The 43 non-synonymous
SNPs had frequencies ≥ 1% in at least one ethnic group and 19 of these 43 non-
synonymous SNPs as well as the insertion/deletion were novel. As mentioned previously
only 38 of these 43 non-synonymous SNPs had working genotype assays. Breast cancer
risk associations, minor allele frequencies, and Hardy-Weinberg Equilibrium are shown
in Table 2 and more extensively in Table 3. All genotyped variants were found to be in
Hardy-Weinberg Equilibrium among controls (p>0.01) in at least 4 of the 5 ethnic
groups. The 3 poly-glutamine repeat polymorphisms indentified via sequencing were
found in exon 20 of NCOA3, exon 15 of NCOR2, and exon 4 of SMARCA4 however
genotyping assays were not preformed.

10
Table 2: The Range of Minor Allele Frequency and Association with Breast Cancer Risk of Each Variant
Gene/Variant MAF Range
Cases and
Controls
Pooled
1,612 cases/
1,961 controls
OR (95% CI)*
ER+
1024 cases
OR (95% CI)*
ER-
274 cases
OR (95% CI)*
Localized
1187 cases
OR (95% CI)*
Regional/
Metastatic
420 cases
OR (95% CI)*
EP300
Ser507Gly 0-0.02 0.99(0.54-1.81) 0.74(0.35-1.54) 2.59(1.14-5.93) 0.98(0.51-1.88) 1.03(0.38-2.78)
Ile997Val 0.023-0.483 0.92(0.81-1.04) 0.84(0.73-0.98) 1.12(0.88-1.41) 0.94(0.82-1.08) 0.88(0.72-1.07)
Pro1986Leu 0-0.009 0.94(0.22-3.99) 1.21(0.28-5.15) - 0.72(0.14-3.83) 1.50(0.17-13.43)
Gln2223Pro 0-0.039 1.04(0.70-1.53) 0.99(0.63-1.56) 0.96(0.45-2.03) 0.95(0.61-1.47) 1.32(0.75-2.30)
CCND1
Pro241Pro 0.244-0.539 1.07(0.97-1.19) 1.12(1.00-1.26) 1.05(0.86-1.27) 1.08(0.96-1.20) 1.07(0.91-1.25)
NCOA1
Pro1272Ser 0-0.028 1.12(0.70-1.81) 0.99(0.56-1.75) 1.17(0.49-2.78) 1.33(0.81-2.19) 0.62(0.24-1.59)
NCOA2
Ala407Ser 0-0.009 2.08(0.67-6.43) 1.64(0.39-6.97) 2.99(0.70-12.88) 1.19(0.28-5.01) 3.66(1.02-13.09)
Asn1212Ser 0-0.013 1.12(0.47-2.67) 1.29(0.47-3.54) 0.50(0.06-3.91) 0.95(0.35-2.60) 1.59(0.49-5.14)
Met1282Ile 0.019-0.142 1.07(0.88-1.30) 1.04(0.83-1.30) 0.99(0.66-1.48) 1.10(0.89-1.36) 0.95(0.68-1.33)
NCOA3
Arg218Cys 0-0.079 0.87(0.68-1.12) 0.92(0.68-1.24) 0.79(0.48-1.29) 0.77(0.58-1.04) 1.14(0.80-1.64)
Met391Val 0-0.016 0.89(0.39-2.04) 1.33(0.52-3.37) 0.35(0.05-2.72) 0.91(0.36-2.29) 0.82(0.23-2.94)
Pro559Ser 0-0.038 1.15(0.69-1.92) 1.46(0.80-2.64) 0.84(0.32-2.23) 1.37(0.79-2.36) 0.66(0.25-1.74)
Gln586His 0.020-0.086 0.99(0.80-1.23) 0.95(0.75-1.22) 1.19(0.81-1.74) 1.06(0.85-1.34) 0.85(0.60-1.22)
Ser662Phe 0-0.001 - - - - -
SMARCA2
Asp1546Glu 0.129-0.273 1.06(0.94-1.20) 1.11(0.97-1.28) 0.94(0.74-1.19) 1.07(0.93-1.22) 1.07(0.88-1.30)
FOXA1
Ala83Thr 0.118-0.580 0.98(0.88-1.09) 0.98(0.86-1.11) 1.01(0.82-1.24) 0.93(0.82-1.05) 1.12(0.94-1.33)
Ser448Asn 0-0.077 1.09(0.84-1.42) 1.29(0.97-1.73) 0.58(0.30-1.09) 0.99(0.74-1.33) 1.38(0.95-2.01)
MPG
Val242Leu 0-0.003 1.52(0.34-6.88) 3.00(0.65-13.90) - 0.60(0.06-5.84) 3.99(0.78-20.34)
NCOR1
Val1996/1997 del 0-0.028 0.96(0.52-1.77) 0.64(0.26-1.56) 1.11(0.43-2.90) 0.85(0.41-1.73) 1.26(0.54-2.92)

11
Table 2: The Range of Minor Allele Frequency and Association with Breast Cancer Risk of Each Variant (continued)
Gene/Variant MAF Range
Cases and
Controls
Pooled
1,612 cases/
1,961 controls
OR (95% CI)*
ER+
1024 cases
OR (95% CI)*
ER-
274 cases
OR (95% CI)*
Localized
1187 cases
OR (95% CI)*
Regional/
Metastatic
420 cases
OR (95% CI)*
NCOR2
Thr35Met 0-0.025 0.18(0.02-1.38) 0.22(0.03-1.70) - - 0.75(0.09-5.97)
His52Arg 0-0.042 2.46(0.81-7.42) 2.80(0.89-8.75) 2.28(0.56-9.33) 1.38(0.34-5.57) 4.02(1.23-13.11)
Gly783Glu 0-0.131 1.18(0.99-1.41) 1.20(0.98-1.48) 1.16(0.83-1.62) 1.22(1.00-1.48) 1.06(0.80-1.42)
Lys980Thr 0-0.022 1.36(0.68-2.71) 1.22(0.57-2.64) 3.48(1.20-10.03) 1.48(0.71-3.07) 1.17(0.33-4.13)
Ala995Gly 0-0.078 0.95(0.66-1.36) 1.01(0.65-1.58) 1.24(0.72-2.13) 0.99(0.66-1.47) 0.83(0.47-1.47)
Ser1525Thr 0-0.028 0.91(0.51-1.62) 0.86(0.40-1.82) 1.03(0.41-2.60) 0.81(0.42-1.58) 1.13(0.49-2.60)
Ala1706Thr 0.060-0.209 0.98(0.85-1.12) 1.00(0.86-1.17) 0.96(0.74-1.24) 0.95(0.82-1.11) 1.01(0.82-1.25)
Ala2007Thr 0.012-0.049 1.01(0.78-1.30) 0.92(0.68-1.25) 1.03(0.64-1.66) 1.04(0.79-1.37) 0.96(0.63-1.45)
Ala2011Val 0-0.014 1.19(0.50-2.84) 1.62(0.64-4.12) 0.60(0.08-4.76) 0.91(0.31-2.70) 1.84(0.61-5.58)
Thr2216Pro 0-0.028 1.05(0.56-1.95) 1.16(0.56-2.39) 1.11(0.37-3.31) 1.13(0.58-2.21) 0.86(0.29-2.54)
Ser2311Gly 0.005-0.068 0.92(0.63-1.32) 0.89(0.59-1.35) 0.74(0.32-1.70) 1.07(0.73-1.57) 0.47(0.21-1.08)
Ala2496Thr 0-0.079 0.95(0.66-1.36) 1.03(0.70-1.52) 0.58(0.23-1.42) 1.11(0.77-1.61) 0.45(0.19-1.02)
CALCOCO1
Arg12His 0-0.012 2.38(1.03-5.49) 2.93(1.20-7.15) 0.79(0.10-6.37) 1.91(0.75-4.83) 3.27(1.13-9.45)
Arg393Lys 0.128-0.365 0.99(0.84-1.17) 0.95(0.79-1.15) 1.16(0.86-1.57) 0.96(0.80-1.16) 1.06(0.81-1.38)
Ala527Thr 0-0.015 0.92(0.40-2.09) 0.90(0.32-2.55) - 1.21(0.52-2.84) 0.28(0.04-2.16)
CALCOCO1
Gly561Val 0-0.011 1.16(0.57-2.39) 1.01(0.42-2.41) 0.88(0.20-3.92) 1.26(0.58-2.77) 1.06(0.35-3.25)
Thr639Pro 0-0.067 0.95(0.64-1.41) 1.07(0.66-1.72) 0.69(0.32-1.50) 0.84(0.53-1.33) 1.27(0.72-2.22)
CREBBP
Pro858Ser 0-0.006 0.70(0.17-2.98) 0.90(0.17-4.69) 1.17(0.13-10.31) 0.67(0.13-3.48) 0.90(0.10-7.87)
Thr910Ala 0-0.005 1.00(0.24-4.10) 1.52(0.37-6.31) - 1.08(0.24-4.97) 1.06(0.12-9.68)
Val992Ile 0-0.038 1.13(0.67-1.91) 0.66(0.31-1.42) 2.40(1.20-4.81) 1.17(0.66-2.08) 1.08(0.48-2.41)
Gly2229Ser 0-0.015 1.72(0.72-4.14) 1.87(0.68-5.12) 1.19(0.25-5.65) 1.62(0.61-4.27) 1.86(0.56-6.21)
*Adjusted for age and ethnicity.
-Can not calculate OR (95% CI) due to small numbers of cases or controls.

12
Table 3: Ethnic Specific Minor Allele Frequencies, Hardy Weinberg Equilibrium, and Associations of Each Variant with Breast Cancer Risk
Gene/
Variant/
RS#

African
Americans
Cases/Controls
345/426
Native Hawaiians
Cases/Controls
108/290
Japanese
Americans
Cases/Controls
425/419
Latinas
Cases/Controls
334/386
European
Americans
Cases/Controls
400/440
P
Het

EP300
Ser507Gly HWE -/- 0.99/0.99 0.12/0.91 0.99/0.99 -/-
MAF 0/0 0.009/0.005 0.020/0.020 0.002/0.004 0/0
OR(95%CI) - 1.73(0.28-10.56) 1.01(0.52-1.97) 0.40(0.04-3.86) - 0.6051
Ile997Val HWE 0.99/0.71 0.99/0.98 0.82/0.89 0.23/0.58 0.93/0.96
rs20551 MAF 0.105/0.092 0.103/0.151 0.029/0.023 0.462/0.483 0.291/0.324
OR(95%CI) 1.15(0.83-1.61) 0.63(0.39-1.04) 1.30(0.70-2.40) 0.93(0.76-1.13) 0.85(0.69-1.05) 0.2350
Pro1986Leu HWE -/- 0.99/0.99 0.99/0.99 -/- -/-
MAF 0/0 0.009/0.007 0.001/0.002 0/0 0/0
OR(95%CI) - 1.42(0.25-8.02) 0.47(0.04-5.22) - - 0.4483
Gln2223Pro HWE 0.98/0.98 0.99/0.99 0.99/- 0.96/0.94 0.87/0.82
rs1046088 MAF 0.010/0.009 0.005/0.009 0.001/0 0.015/0.018 0.039/0.036
OR(95%CI) 1.12(0.40-3.12) 0.51(0.06-4.43) - 0.84(0.37-1.93) 1.12(0.67-1.86) 0.9438
CCND1
Pro241Pro HWE 0.27/0.78 0.29/0.99 0.17/0.85 0.78/0.74 0.09/0.98
rs603965 MAF 0.248/0.244 0.525/0.539 0.487/0.480 0.401/0.384 0.516/0.473
OR(95%CI) 1.02(0.80-1.28) 0.95(0.70-1.30) 1.02(0.84-1.24) 1.07(0.86-1.33) 1.24(1.01-1.53) 0.6658
NCOA1
Pro1272Ser HWE 0.99/0.99 0.99/0.98 -/- 0.99/0.98 0.43/0.21
rs1804645 MAF 0.004/0.005 0.009/0.010 0/0 0.009/0.010 0.028/0.022
OR(95%CI) 0.97(0.22-4.39) 0.84(0.16-4.27) - 0.87(0.30-2.54) 1.31(0.71-2.41) 0.9269
NCOA2
Ala407Ser HWE 0.99/0.99 -/- -/- 0.99/0.99 -/-
MAF 0.009/0.005 0/0 0/0 0.003/0.001 0/0
OR(95%CI) 1.99(0.55-7.17) - - 2.41(0.22-26.80) - 0.9207
Asn1212Ser HWE 0.97/0.997 -/- -/- 0.99/0.99 -/-
MAF 0.013/0.012 0/0 0/0 0.001/0.001 0/0
OR(95%CI) 1.11(0.45-2.77) - - 1.19(0.07-19.21) - 0.9192

13
Table 3: Ethnic Specific Minor Allele Frequencies, Hardy Weinberg Equilibrium, and Associations of Each Variant with Breast Cancer Risk
(continued)
Gene/
Variant/
RS#

African
Americans
Cases/Controls
345/426
Native Hawaiians
Cases/Controls
108/290
Japanese
Americans
Cases/Controls
425/419
Latinas
Cases/Controls
334/386
European
Americans
Cases/Controls
400/440
P
Het

NCOA2
Met1282Ile HWE 0.66/0.92 0.99/0.41 0.88/0.68 0.61/0.99 0.98/<0.001
rs2228591 MAF 0.035/0.019 0.140/0.142 0.062/0.067 0.052/0.052 0.067/0.061
OR(95%CI) 1.92(1.02-3.64) 1.02(0.64-1.62) 0.91(0.62-1.35) 0.98(0.61-1.59) 1.09(0.75-1.59) 0.4191
NCOA3
Arg218Cys HWE 0.82/0.91 0.98/0.94 -/- 0.35/0.88 0.001/0.59
rs6094752 MAF 0.072/0.079 0.019/0.021 0/0 0.028/0.040 0.045/0.048
OR(95%CI) 0.90(0.61-1.33) 0.84(0.26-2.67) - 0.70(0.40-1.24) 0.95(0.62-1.48) 0.8492
Met391Val HWE 0.96/0.94 -/- -/- -/- -/-
MAF 0.015/0.016 0/0 0/0 0/0 0/0
OR(95%CI) 0.89(0.39-2.04) - - - - -
Pro559Ser HWE 0.77/0.74 -/0.99 -/- 0.99/- 0.99/-
rs2230781 MAF 0.038/0.037 0/0.003 0/0 0.003/0 0.003/0
OR(95%CI) 1.04(0.61-1.80) - - - - 1.0000
Gln586His HWE 0.91/0.92 <0.001/0.68 0.93/0.63 0.60/0.43 0.98/0.21
rs2230782 MAF 0.023/0.020 0.024/0.049 0.041/0.045 0.086/0.063 0.075/0.086
OR(95%CI) 1.17(0.58-2.35) 0.45(0.17-1.18) 0.90(0.56-1.45) 1.45(0.96-2.18) 0.87(0.61-1.24) 0.1460
Ser662Phe HWE 0.99/- -/- 0.99/- -/- -/-
MAF 0.001/0 0/0 0.001/0 0/0 0/0
OR(95%CI) - - - - - -
SMARCA2
Asp1546Glu HWE 0.37/0.98 0.99/0.63 0.73/0.02 0.99/0.73 0.95/0.89
rs2296212 MAF 0.273/0.242 0.163/0.160 0.179/0.180 0.209/0.201 0.131/0.129
OR(95%CI) 1.18(0.93-1.48) 1.05(0.68-1.64) 0.99(0.77-1.28) 1.05(0.81-1.36) 1.01(0.75-1.36) 0.8789
FOXA1
Ala83Thr HWE 0.66/0.33 0.25/0.31 0.97/0.60 0.94/0.99 0.95/0.86
rs7144658 MAF 0.521/0.58 0.159/0.188 0.127/0.118 0.349/0.333 0.430/0.392
OR(95%CI) 0.77(0.64-0.97) 0.82(0.54-1.23) 1.07(0.80-1.44) 1.07(0.85-1.34) 1.13(0.92-1.39) 0.0691

14
Table 3: Ethnic Specific Minor Allele Frequencies, Hardy Weinberg Equilibrium, and Associations of Each Variant with Breast Cancer Risk
(continued)
Gene/
Variant/
RS#

African
Americans
Cases/Controls
345/426
Native Hawaiians
Cases/Controls
108/290
Japanese
Americans
Cases/Controls
425/419
Latinas
Cases/Controls
334/386
European
Americans
Cases/Controls
400/440
P
Het

FOXA1
Ser448Asn HWE 0.97/0.35 0.97/0.93 -/0.99 <0.001/0.37 0.90/0.06
rs33984772 MAF 0.014/0.025 0.023/0.023 0/0.002 0.050/0.046 0.077/0.057
OR(95%CI) 0.54(0.25-1.18) 1.01(0.35-2.92) - 1.08(0.68-1.72) 1.39(0.95-2.05) 0.3808
MPG
Val242Leu HWE 0.99/0.99 -/- 0.99/- -/0.99 0.99/-
MAF 0.003/0.002 0/0 0.001/0 0/0.001 0.001/0
OR(95%CI) 1.25(0.17-9.02) - - - - -
NCOR1
Val1996/ HWE 0.90/0.51 -/- -/- 0.99/- -/-
1997 del MAF 0.024/0.028 0/0 0/0 0.003/0 0/0
OR(95%CI) 0.86(0.45-1.61) - - - - 0.9686
NCOR2
Thr35Met HWE -/- 0.99/0.91 -/- -/- -/0.99
MAF 0/0 0.005/0.025 0/0 0/0 0/0.001
OR(95%CI) - 0.20(0.03-1.53) - - - 0.9729
His52Arg HWE 0.002/0.35 <0.001/- -/- -/- 0.99/-
MAF 0.042/0.026 0.010/0 0/0 0/0 0.001/0
OR(95%CI) 2.00(0.64-6.21) - - - - -
Gly783Glu HWE 0.03/0.80 0.03/0.81 -/0.99 0.22/0.67 0.58/0.84
rs7978237 MAF 0.094/0.071 0.062/0.037 0/0.001 0.131/0.125 0.131/0.122
OR(95%CI) 1.32(0.92-1.89) 1.68(0.84-3.36) - 1.05(0.77-1.44) 1.15(0.86-1.55) 0.7417
Lys980Thr HWE -/- -/0.97 0.90/0.98 -/- -/-
MAF 0/0 0/0.014 0.022/0.011 0/0 0/0
OR(95%CI) - - 2.02(0.90-4.56) - - 0.9610
Ala995Gly HWE 0.99/0.96 -/- -/- 0.99/0.99 -/0.99
rs11057592 MAF 0.075/0.078 0/0 0/0 0.009/0.008 0/0.003
OR(95%CI) 0.98(0.67-1.43) - - 1.13(0.36-3.56) - 0.9584

15
Table 3: Ethnic Specific Minor Allele Frequencies, Hardy Weinberg Equilibrium, and Associations of Each Variant with Breast Cancer Risk
(continued)
Gene/
Variant/
RS#

African
Americans
Cases/Controls
345/426
Native Hawaiians
Cases/Controls
108/290
Japanese
Americans
Cases/Controls
425/419
Latinas
Cases/Controls
334/386
European
Americans
Cases/Controls
400/440
P
Het

NCOR2
Ser1525Thr HWE 0.17/0.51 -/0.99 -/- 0.99/0.99 -/-
MAF 0.024/0.028 0/0.002 0/0 0.005/0.003 0/0
OR(95%CI) 0.86(0.46-1.60) - - 1.70(0.28-10.27) - 0.7522
Ala1706Thr HWE 0.45/0.99 <0.001/0.11 0.28/0.009 0.04/0.39 0.60/0.93
rs2229840 MAF 0.120/0.120 0.102/0.060 0.141/0.116 0.168/0.209 0.148/0.160
OR(95%CI) 1.00(0.73-1.37) 1.56(0.93-2.63) 1.22(0.93-1.61) 0.78(0.60-1.01) 0.88(0.67-1.16) 0.0582
Ala2007Thr HWE 0.79/0.99 0.99/0.98 0.99/0.58 0.37/0.77 0.95/0.65
rs22272777 MAF 0.036/0.045 0.014/0.012 0.047/0.049 0.030/0.035 0.044/0.031
OR(95%CI) 0.82(0.48-1.39) 1.11(0.28-4.38) 0.95(0.60-1.50) 0.83(0.46-1.51) 1.52(0.90-2.55) 0.5701
Ala2011Val HWE <0.001/0.98 -/0.99 -/- -/- -/-
MAF 0.014/0.011 0/0.002 0/0 0/0 0/0
OR(95%CI) 1.27(0.52-3.10) - - - - -
Thr2216Pro HWE 0.31/0.90 -/0.99 -/- -/0.99 -/0.99
rs1472840 MAF 0.028/0.022 0/0.005 0/0 0/0.001 0/0.001
OR(95%CI) 1.28(0.66-2.46) - - - - -
Ser2311Gly HWE 0.99/0.98 0.66/0.55 0.83/0.19 0.99/0.99 0.99/0.99
rs2228587 MAF 0.006/0.009 0.068/0.060 0.029/0.039 0.005/0.001 0.006/0.005
OR(95%CI) 0.64(0.19-2.16) 1.10(0.58-2.10) 0.76(0.45-1.29) 3.54(0.37-34.18) 1.25(0.33-4.82) 0.5939
Ala2496Thr HWE 0.99/0.006 0.88/0.64 0.86/0.86 <0.001/- 0.99/0.99
MAF 0.008/0.014 0.079/0.062 0.027/0.038 0.005/0 0.008/0.003
OR(95%CI) 0.58(0.21-1.59) 1.25(0.68-2.28) 0.71(0.41-1.24) - 1.76(0.44-7.15) 0.4167
CALCOCO1
Arg12His HWE 0.98/0.99 0.99/0.99 -/- 0.99/0.99 0.99/0.99
MAF 0.012/0.005 0.009/0.002 0/0 0.002/0.001 0.006/0.003
OR(95%CI) 2.65(0.79-8.94) 5.60(0.48-64.92) - 1.09(0.07-17.58) 1.76(0.41-7.62) 0.8623
Arg393Lys HWE 0.24/0.96 0.99/0.15 0.22/0.99 0.88/0.79 0.65/0.37
rs3741659 MAF 0.128/0.140 0.276/0.246 0.338/0.365 0.149/0.179 0.147/0.143
OR(95%CI) 1.10(0.69-1.76) 1.44(0.87-2.38) 0.95(0.76-1.17) 0.81(0.48-1.35) 1.00(0.55-1.83) 0.5487

16
Table 3: Ethnic Specific Minor Allele Frequencies, Hardy Weinberg Equilibrium, and Associations of Each Variant with Breast Cancer Risk
(continued)
Gene/
Variant/
RS#

African
Americans
Cases/Controls
345/426
Native Hawaiians
Cases/Controls
108/290
Japanese
Americans
Cases/Controls
425/419
Latinas
Cases/Controls
334/386
European
Americans
Cases/Controls
400/440
P
Het

CALCOCO1
Ala527Thr HWE 0.96/0.96 -/- -/- -/0.99 -/-
MAF 0.015/0.014 0/0 0/0 0/0.003 0/0
OR(95%CI) 1.08(0.46-2.54) - - - - -
Gly561Val HWE 0.99/0.99 -/0.99 -/- 0.99/0.99 0.99/098
rs34229062 MAF 0.007/0.005 0/0.003 0/0 0.008/0.001 0.006/0.011
OR(95%CI) 1.61(0.43-6.05) - - 5.93(0.69-51.06) 0.60(0.20-1.84) 0.2952
Thr639Pro HWE 0.44/0.79 -/0.98 -/- 0.99/0.99 -/-
rs34281379 MAF 0.065/0.067 0/0.010 0/0 0.006/0.003 0/0
OR(95%CI) 0.97(0.64-1.48) - - 2.14(0.39-11.89) - 0.7007
Pro858Ser HWE 0.99/0.99 -/- -/- 0.99/- -/-
MAF 0.003/0.006 0/0 0/0 0.002/0 0/0
OR(95%CI) 0.47(0.09-2.43) - - - - -
CREBBP
Thr910Ala HWE -/0.99 -/- -/0.99 -/- 0.99/0.99
MAF 0/0.001 0/0 0/0.001 0/0 0.005/0.002
OR(95%CI) - - - - 1.93(0.34-10.86) -
Val992Ile HWE 0.77/0.74 -/- -/- 0.99/- 0.99/0.99
MAF 0.038/0.036 0/0 0/0 0.003/0 0.001/0.001
OR(95%CI) 1.05(0.61-1.81) - - - 0.97(0.06-15.74) 0.9992
Gly2229Ser HWE 0.96/0.98 -/- -/- -/- 0.99/-
MAF 0.015/0.011 0/0 0/0 0/0 0.003/0
OR(95%CI) 1.42(0.57-3.53) - - - - -
HWE is defined as Hardy-Weinberg Equilibrium
MAF is defined as Minor Allele Frequency
-Can not be calculated due no data or small numbers.

17
Allelic Associations with Breast Cancer Risk: Among all women in our nested
breast cancer case-control study the median age was 65 years of age for cases and 63
years of age for controls. The median age for cases and controls within each ethnic group
was also similar (Table 4). Known breast cancer risk factors for each ethnic group were
all as expected (Table 4). Briefly, among postmenopausal women, cases were more likely
to be heavier as well as use hormone therapy compared to controls. Among all women in
this study cases had an earlier age at menarche, tended to have fewer children, and were
more likely to have a family history of breast cancer compared to controls (Table 4).
Ethnic-pooled analyses of the co-dominant effect of each variant and breast
cancer (Table 2) yielded one statistically significant result, namely Arg12His in
CALCOCO1 (OR=2.38; 95% CI, 1.03-5.49). As shown in Table 3 this SNP was rare
among cases and controls in our study population, which could make this more likely to
be a spurious result. However, Arg12His in CALCOCO1 was also found to be
statistically significantly associated with ER+ breast cancer (OR=2.93; 95% CI, 1.20-
7.15) as well as regional/metastatic breast cancer (OR=3.27; 95% CI, 1.13-9.45).
In addition to Arg12His in CALCOCO1 Pro241Pro in CCND1 was marginally
statistically significantly associated with ER+ breast cancer (OR=1.12; 95% CI, 1.00-
12.6). ER- breast cancer analyses yielded associations with Ser507Gly in EP300,
Lys980Thr in NCOR2, and Val992Ile in CREBBP (OR=2.59; 95% CI, 1.14-5.93,
OR=3.48; 95% CI, 1.20-10.03, and OR=2.40; 95% CI, 1.20-4.81, respectively).
Gly783Glu in NCOR2 was found to be statistically significantly associated with localized
breast cancer risk (OR=1.22; 95% CI, 1.00-1.48) and Ala407Ser in NCOA2, His52Arg in

18
Table 4: Descriptive Characteristics Among Breast Cancer Cases and Controls in the Multiethnic Cohort
African
Americans
345/426
Native
Hawaiians
108/290
Japanese
Americans
425/419
Latinas
334/386
European
Americans
400/440
Age (median, years) 66/65 60/58 66/65 64/64 66/61
Menopausal Status (%)
Premenopausal 13/10 19/26 12/20 9/9 7/20
Postmenopausal 58/57 60/54 73/66 66/67 72/63
Unknown 21/27 14/16 10/11 17/18 18/16
Body Mass Index (%)*
<25 18/14 22/21 48/46 20/23 42/33
≥25 40/43 38/33 23/20 46/44 30/29
Ever use of hormone therapy (%)*
Never 29/28 24/20 19/19 29/31 18/21
Ever 28/28 35/33 54/45 36/33 54/41
Years of age at menarche(%)
≤12 38/44 53/59 56/51 47/46 55/49
12+ 45/55 44/40 42/48 51/52 45/51
Age at first birth among parous women (%)
<20 38/46 40/37 7/10 34/38 19/20
21-30 39/34 44/45 63/67 46/47 52/53
31+ 7/5 2/6 11/10 7/4 9/11
Number of children (%)
0 13/13 10/9 16/11 10/8 19/15
1 19/14 6/9 11/10 8/6 12/9
2-3 38/40 43/38 55/60 36/35 49/53
4+ 27/31 42/44 17/18 45/50 19/22
First degree family history of breast cancer
Yes 20/13 18/14 17/11 15/10 16/9
No 80/87 82/86 83/89 85/90 84/91
Advanced breast cancer (%)+
Yes 29 27 21 30 26
No 70 73 79 70 74

19
Table 4: Descriptive Characteristics Among Breast Cancer Cases and Controls in the Multiethnic Cohort (continued)
African
Americans
345/426
Native
Hawaiians
108/290
Japanese
Americans
425/419
Latinas
334/386
European
Americans
400/440
Estrogen receptor status (%)
Positive 50 79 74 53 69
Negative 24 14 13 19 15
Unknown 26 7 13 28 16
*Among postmenopausal women only. Postmenopausal is defined as natural menopause or bilateral oopharectomy.
+Advanced breast cancer is defined as SEER stages 2-7.
NOTE: Not all numbers add up to 100% due to missing values.

20
NCOR2, and Arg12His in CALCOCO1 were statistically significantly associated with
regional/metastatic breast cancer risk (OR=3.66; 95% CI, 1.02-13.09, OR=4.02; 95% CI,
1.23-13.11, OR=3.27; 95% CI, 1.13-9.45, respectively). All displayed in Table 2.
Furthermore we tested for effect modification of body mass index (BMI), age at
menarche, and hormone therapy use. As shown in Table 5 BMI category was found to
statistically significantly modify Asp1546Glu in SMARCA2, Ala83Thr in FOXA1, and
Ser2311Gly in NCOR2 and breast cancer risk (p=0.0225, 0.0075, and 0.0256,
respectively). Table 6 displays that age at menarche marginally statistically significantly
modified the effect of Ala2007Thr in NCOR2 and breast cancer risk (p=0.0657).
Hormone therapy use among postmenopausal women was found to statistically
significantly modify the effect of Arg393Lys in CALCOCO1 and breast cancer risk
(p=0.0471) and marginally statistically significantly modify the effect of Ser448Asn in
FOXA1 and breast cancer risk (p=0.0645) as shown in Table 7.
Table 5: Effect Modification by Body Mass Index (kg/m
2
)
Gene Variant <25
Cases/Controls
743/845
≥25
Cases/Controls
870/1116
P
int

EP300 Ser507Gly 0.94(0.28-1.74) 1.02(0.41-2.57) 0.8780
Ile997Val 0.88(0.72-1.07) 0.94(0.80-1.11) 0.3221
Pro1986Leu 1.90(0.10-34.72) 0.63(0.11-3.61) 0.6281
Gln2223Pro 1.27(0.72-2.26) 0.83(0.48-1.42) 0.3141
CCND1 Pro241Pro 1.03(0.89-1.20) 1.12(0.97-1.28) 0.3038
NCOA1 Pro1272Ser 1.06(0.50-2.24) 1.16(0.62-2.16) 0.8531
NCOA2 Ala407Ser 2.76(0.24-32.19) 2.00(0.55-7.23) 0.8975
Asn1212Ser 1.38(0.30-6.45) 1.02(0.35-2.97) 0.5631
Met1282Ile 1.08(0.80-1.47) 1.05(0.80-1.38) 0.9538
NCOA3 Arg218Cys 0.91(0.60-1.40) 0.81(0.59-1.12) 0.3054
Met391Val 0.54(0.09-3.08) 1.07(0.42-2.76) 0.6042
Pro559Ser 0.54(0.17-1.76) 1.36(0.76-2.43) 0.3876
Gln586His 1.04(0.76-1.44) 0.94(0.70-1.27) 0.6561
Ser662Phe - - -
SMARCA2 Asp1546Glu 0.89(0.74-1.08) 1.21(1.02-1.42) 0.0225
FOXA1 Ala83Thr 1.13(0.95-1.34) 0.89(0.77-1.02) 0.0075
Ser448Asn 1.35(0.89-2.05) 0.96(0.68-1.36) 0.2325

21
Table 5: Effect Modification by Body Mass Index (kg/m
2
) (continued)
Gene Variant <25
Cases/Controls
743/845
≥25
Cases/Controls
870/1116
P
int

MPG Val242Leu 1.89(0.17-21.18) 1.46(0.20-10.48) 0.7244
NCOR1 Val1996/1997 del 0.49(0.13-1.91) 1.14(0.56-2.31) 0.4675
NCOR2 Thr35Met 0.33(0.04-2.77) - -
His52Arg - - -
Gly783Glu 1.16(0.87-1.55) 1.18(0.93-1.49) 0.9268
Lys980Thr 1.67(0.76-3.64) 0.80(0.14-4.49) 0.4704
Ala995Gly 0.94(0.46-1.92) 0.94(0.62-1.43) 0.5791
Ser1525Thr 2.05(0.58-7.27) 0.76(0.38-1.50) 0.1246
Ala1706Thr 0.97(0.79-1.19) 1.00(0.83-1.19) 0.9468
Ala2007Thr 0.86(0.59-1.26) 1.16(0.82-1.63) 0.1942
Ala2011Val - 1.25(0.52-2.99) -
Thr2216Pro 0.86(0.07-10.02) 1.12(0.59-2.14) 0.7930
Ser2311Gly 0.64(0.39-1.05) 1.36(0.78-2.36) 0.0256
Ala2496Thr 0.76(0.47-1.24) 1.16(0.68-1.96) 0.2522
CALCOCO1 Arg12His 2.35(0.68-8.08) 2.44(0.77-7.72) 0.9020
Arg393Lys 0.97(0.77-1.21) 0.99(0.76-1.29) 0.7982
Ala527Thr 4.00(0.40-40.25) 0.70(0.28-1.79) 0.1703
Gly561Val 0.87(0.30-2.51) 1.38(0.51-3.75) 0.6995
Thr639Pro 0.78(0.36-1.71) 1.04(0.65-1.65) 0.7860
CREBBP Pro858Ser 0.31(0.03-3.18) 1.20(0.17-8.59) 0.4438
Thr910Ala - 0.30(0.03-2.72) -
Val992Ile 1.09(0.43-2.80) 1.15(0.61-2.17) 0.8253
Gly2229Ser 1.46(0.31-7.00) 1.88(0.64-5.50) 0.9322
Note: All ORs are adjusted for age and ethnicity.
Table 6: Effect Modification by Age at Menarche (years)
Gene Variant ≤12
Cases/Controls
854/966
12+
Cases/Controls
728/977
P
int

EP300 Ser507Gly 1.31(0.57-3.04) 0.70(0.28-1.74) 0.2311
Ile997Val 0.95(0.79-1.14) 0.91(0.76-1.08) 0.7654
Pro1986Leu 0.90(0.20-4.02) - -
Gln2223Pro 1.10(0.64-1.89) 1.00(0.56-1.79) 0.9065
CCND1 Pro241Pro 1.07(0.93-1.23) 1.0780.93-1.25) 0.8883
NCOA1 Pro1272Ser 0.96(0.48-1.95) 1.16(0.60-2.24) 0.9641
NCOA2 Ala407Ser 3.46(0.69-17.41) 0.93(0.15-5.75) 0.4130
Asn1212Ser 1.04(0.30-3.65) 1.25(0.37-4.18) 0.5043
Met1282Ile 1.07(0.80-1.43) 1.07(0.81-1.41) 0.2759
NCOA3 Arg218Cys 0.98(0.68-1.41) 0.76(0.53-1.09) 0.1557
Met391Val 0.42(0.11-1.65) 1.76(0.57-5.41) 0.1635
Pro559Ser 0.83(0.41-1.69) 1.68(0.79-3.60) 0.4338
Gln586His 0.91(0.67-1.23) 1.12(0.83-1.53) 0.4960
Ser662Phe - - -
SMARCA2 Asp1546Glu 1.01(0.84-1.20) 1.11(0.93-1.33) 0.9177
FOXA1 Ala83Thr 0.95(0.81-1.10) 1.02(0.87-1.20) 0.7579
Ser448Asn 0.86(0.59-1.24) 1.36(0.93-1.98) 0.1262
MPG Val242Leu - - -
NCOR1 Val1996/1997 del 1.60(0.56-4.60) 0.90(0.40-2.02) 0.2419

22
Table 6: Effect Modification by Age at Menarche (years) (continued)
Gene Variant ≤12
Cases/Controls
854/966
12+
Cases/Controls
728/977
P
int

NCOR2 Thr35Met 0.37(0.04-3.15) - -
His52Arg 1.46(0.44-4.86) - -
Gly783Glu 1.11(0.87-1.43) 1.25(0.96-1.63) 0.8856
Lys980Thr 1.22(0.43-3.48) 1.42(0.55-3.71) 0.2724
Ala995Gly 1.03(0.63-1.68) 0.79(0.46-1.36) 0.4523
Ser1525Thr 0.68(0.32-1.43) 1.22(0.47-3.16) 0.7131
Ala1706Thr 1.04(0.86-1.25) 0.92(0.76-1.12) 0.5203
Ala2007Thr 1.29(0.90-1.86) 0.76(0.52-1.10 0.0657
Ala2011Val 1.39(0.41-4.70) 0.99(0.27-3.61) 0.3987
Thr2216Pro 0.78(0.33-1.83) 1.44(0.57-3.62) 0.4395
Ser2311Gly 1.16(0.67-1.99) 0.68(0.40-1.16) 0.1643
Ala2496Thr 1.07(0.61-1.89) 0.84(0.52-1.36) 0.7287
CALCOCO1 Arg12His 1.97(0.62-6.30) 3.15(0.91-10.89) 0.8973
Arg393Lys 0.97(0.76-1.23) 1.02(0.80-1.30) 0.1184
Ala527Thr 0.75(0.28-2.02) 1.15(0.25-5.22) 0.9110
Gly561Val 0.75(0.31-1.78) 2.87(0.70-11.77) 0.0991
Thr639Pro 1.15(0.62-2.16) 0.83(0.48-1.42) 0.0829
CREBBP Pro858Ser 0.94(0.06-15.23) 0.70(0.13-3.91) 0.5594
Thr910Ala 0.93(0.18-4.80) 1.06(0.06-17.68) 0.9665
Val992Ile 0.89(0.39-2.01) 1.53(0.76-3.10) 0.1510
Gly2229Ser 1.87(0.61-5.70) 1.22(0.27-5.60) 0.6803
NOTE: All ORs are adjusted for age and ethnicity.

Table 7: Effect Modification by Hormone Therapy Among Postmenopausal Women
Gene Variant Never
Cases/Controls
373/475
Ever (E or E+P)
Cases/Controls
700/711
P
int

EP300 Ser507Gly 1.31(0.57-3.04) 0.70(0.28-1.74) 0.2311
Ile997Val 0.95(0.79-1.14) 0.91(0.76-1.08) 0.7654
Pro1986Leu 3.52(0.29-42.20) - -
Gln2223Pro 0.93(0.38-2.29) 1.08(0.60-1.92) 0.7124
CCND1 Pro241Pro 1.05(0.85-1.29) 1.12(0.95-1.32) 0.3910
NCOA1 Pro1272Ser 1.15(0.42-3.15) 0.45(0.18-1.14) 0.2556
NCOA2 Ala407Ser 1.33(0.26-6.80) - -
Asn1212Ser 0.95(0.21-4.39) 1.18(0.23-6.12) 0.9614
Met1282Ile 0.94(0.61-1.44) 1.08(0.79-1.47) 0.6783
NCOA3 Arg218Cys 0.72(0.44-1.20) 1.03(0.66-1.59) 0.3426
Met391Val 0.62(0.11-3.49) 1.97(0.31-12.47) 0.4602
Pro559Ser 1.30(0.47-3.60) 0.63(0.24-1.70) 0.2251
Gln586His 0.91(0.57-1.45) 1.03(0.75-1.42) 0.5110
Ser662Phe - - -
SMARCA2 Asp1546Glu 1.02(0.78-1.32) 1.07(0.88-1.31) 0.9899
FOXA1 Ala83Thr 0.96(0.77-1.20) 0.95(0.80-1.13) 0.9820
Ser448Asn 0.64(0.34-1.20) 1.20(0.82-1.76) 0.0645
MPG Val242Leu - 1.35(0.22-8.33) -
NCOR1 Val1996/1997 del 0.74(0.17-3.20) 1.59(0.55-4.63) 0.5461
NCOR2 Thr35Met 0.45(0.05-4.50) - -
His52Arg 1.14(0.28-4.63) - -

23
Table 7: Effect Modification by Hormone Therapy Among Postmenopausal Women (continued)
Gene Variant Never
Cases/Controls
373/475
Ever (E or E+P)
Cases/Controls
700/711
P
int

NCOR2 Gly783Glu 1.46(1.01-2.11) 0.95(0.71-1.28) 0.1238
Lys980Thr 1.78(0.22-14.25) 1.02(0.40-2.61) 0.6488
Ala995Gly 0.96(0.50-1.86) 0.88(0.45-1.74) 0.4930
Ser1525Thr 0.60(0.15-2.46) 2.14(0.83-5.49) 0.2048
Ala1706Thr 0.99(0.75-1.32) 0.96(0.78-1.20) 0.9526
Ala2007Thr 0.88(0.51-1.52) 1.52(1.00-2.31) 0.1181
Ala2011Val 0.45(0.05-4.48) 2.56(0.54-12.16) 0.2999
Thr2216Pro 1.28(0.40-4.11) 1.12(0.33-3.87) 0.6722
Ser2311Gly 0.70(0.31-1.56) 0.89(0.49-1.61) 0.7289
Ala2496Thr 0.89(0.43-1.82) 0.94(0.52-1.68) 0.9519
CALCOCO1 Arg12His 2.95(0.52-16.82) 6.98(1.43-34.11) 0.6546
Arg393Lys 0.68(0.47-1.00) 1.16(0.90-1.50) 0.0471
Ala527Thr 0.32(0.03-2.96) 0.67(0.19-2.38) 0.7370
Gly561Val 2.85(0.69-11.83) 0.52(0.17-1.59) 0.1302
Thr639Pro 0.95(0.44-2.04) 0.97(0.42-2.22) 0.6918
CREBBP Pro858Ser - 3.22(0.32-32.45) -
Thr910Ala - 1.08(0.18-6.59) -
Val992Ile 1.36(0.50-3.71) 1.68(0.63-4.51) 0.9643
Gly2229Ser 1.72(0.37-8.01) 1.73(0.27-11.06) 0.9352
NOTE: All ORs are adjusted for age and ethnicity.
Discussion
There have been few studies to enumerate and test coding alleles for susceptibility
to breast cancer. While common variants contribute to the susceptibility of breast cancer
there is still a large amount of unexplained risk which may be genetic. Since the risk of
breast cancer is related to steroid hormone exposure, any alteration in genes that change
one’s sensitivity to steroid hormones would affect one’s risk of breast cancer. This is
what we examined in the present study by focusing on a strong set of candidate genes that
interact with steroid hormone receptors or coactivator/corepressor protein complexes
which influence transcriptional activation. We screened these genes in multiple
populations in attempt to capture all common alleles and to examine the role of rare
alleles which may be pan-ethnic or ethnic specific. There are many examples that support
the contribution of rare variants and the susceptibility to disease such as the rare variants

24
in BRIP1 and PALB2 for breast cancer (Seal et al and Rahman et al), AXIN1 and hMSH2
for colorectal adenomas (Fearnhead et al) and ABCA1, APOA1, LCAT for low plasma
levels of HDL cholesterol (Cohen et al). Thus the rare variant hypothesis remains an
important hypothesis because it allows investigators to analyze specific aberrations in the
pathway to disease and therefore develop more fine tuned models for disease risk.
In this study our goal was to identify and catalogue potentially functional
polymorphisms in our candidate genes that may serve as genetic markers of breast cancer
risk. Statistically significant associations with breast cancer risk were found in the pooled
analyses for Arg12His in CALCOCO1 (OR=2.38; 95% CI, 1.03-5.49). Furthermore,
Arg12His in CALCOCO1 was statistically significantly associated with ER+ breast
cancer (OR=2.93; 95% CI, 1.20-7.15) and regional/metastatic breast cancer (OR=3.27;
95% CI, 1.13-9.45). Of the forty coding alleles examined in this study, we observed
nominally significant associations with 1 of 40 (2.5%) in the main pooled analysis, and
while some may be of interest, for example the variants in CALCOCO1 which were
associated with more than one breast cancer phenotype and need to be examined in future
studies, they were not unexpected based on the number of tests that were performed
(200).
Previous research has not yielded many results regarding coding SNPs in the
candidate genes we studied and breast cancer risk. The majority of findings and most
recent findings for Pro241Pro of CCND1 have suggested an increased risk of breast
cancer (Onay et al, Yu et al, Shu et al) with a few publications showing a protective
effect (Ceschi et al) or no association (Krippl et al). Burwinkel et al found a statistically
significant protective effect of Gln586His in NCOA3 among a German population while

25
our results found no association. Wiretenberger et al reported no association for either the
A/G or G/G genotype of Ile997Val and the A/C genotype of Gln2223Pro in EP300 (the
C/C genotype was too rare to calculate an effect estimate) where again we found no
association. Conflicting results could be due to differences in sample size (i.e. not enough
power to detect an association in the ethnic stratum) or general characteristics of the
study population (i.e. premenopausal versus postmenopausal study subjects).
Strengths of our current study are that we have an overall large study drawn from
a prospective design. This better enables us to detect a statistically significant association,
should one exist, and to ensure that our controls represent the population giving rise to
the cases. Our sample also has ample power to detect an association between any of these
SNPs and breast cancer risk as well as interactions between known breast cancer risk
factors and these SNPs. We had 80% power across our entire population to detect an
association of 1.80 for MAF equal to 1%. Power was weaker in ethnic-specific analyses.
We has 80% power (for MAF equal to 1%) to detect an association as low as 3.0 in our
African Americans, 4.45 among our Native Hawaiians, 2.90 for our Japanese Americans,
3.10 in our Latinas, and 2.90 among our European Americans. Moreover, our multiethnic
study would allow us to potentially reproduce any association for each SNP within our
own data thus making the association more convincing however the sample size within
each ethnic group would most likely be too small to detect a statistically significant
association.
The limitations of our study are that we are unable to test very rare SNPs because
we would need a much larger sample size in each ethnic group in order to detect and test
associations of rare SNPs with breast cancer risk. In the future hopefully this limitation

26
will be eliminated by projects such as the 1000 Genomes Project however additional
studies that target specific phenotypes (i.e. ER+ disease) may be needed due to the
possibility that some SNPs might only be associated with certain phenotypes.
Furthermore, we did not study the poly-glutamine repeats found in NCOA3, NCOR2, and
SMARCA4 however the repeat found in NCOA3 has previously been studied and no
association with breast cancer risk has been reported. There are no publications to date
regarding the poly-glutamine repeats in NCOR2 and SMARCA4 and breast cancer risk.
Although our project yielded few interesting results other researchers should still
consider testing the association of these SNPs and other phenotypes not studied here.
Other things to consider are the associations of these SNPs with response to treatments
and survival of breast cancer as well as gene-gene interactions.

27
References
Burwinkel et al. Association of NCOA3 polymorphisms with breast cancer risk. Clin
Cancer Res 2005, 11(6):2169-2174.

Cohen et al. Multiple Rare Alleles Contribue to Low Plasma Levels of HDL Cholesterol.
Science 2004, 305:869-872.

Ceschi et al. The effect of cyclin D1 (CCND1) G870A-polymorphism on breast cancer
risk is modified by oxidative stress among Chinese women in Singapore.
Carcinogenesis 2005, 26(8);1457-1464.

Ewing et al. Base-calling of automated sequencer traces using phred. II. Error
probabilities. Genome Res 1998, 8(3):186-194.

Fearnhead et al. Multiple rare variants in different genes account for multifactorial
inherited susceptibility to colorectal adenomas. Proc Natl Acad Sci U S A 2004,
101(45):15992-15997.

Henderson et al. Hormonal Carcinogenesis. Carcinogenesis 2000, 21(3);427-433.

Kolonel et al. A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics.
Am J Epidemiol 2000, 151(4):346-357.

Krippl et al. The 870G>A polymorphism of the cyclin D1 gene is not associated with
breast cancer. Breast Cancer Res Treat 2003, 82(3):165-168.

Onay et al. Combined effect of CCND1 and COMT polymorphisms and increased breast
cancer risk. BMC Cancer 2008, 8:6.

Paez et al. Genome coverage and sequence fidelity of phi29 polymerase-based multiple
strand displacement whole genome amplification. Nucleic Acids Res 2004,
32(9):e71.

Rahman et al. PALB2, which encodes a BRCA2-interacting protein, is a breast cancer
susceptibility gene. Nat Genet 2007, 39(2):165-167.

Seal et al. Truncating mutations in the Fanconi anemia J gene BRIP1 are low-penetrance
breast cancer susceptibility alleles. Nat Genet 2006, 38(11):1239-1241.

Shu et al. Association of cyclin D1 genotype with breast cancer risk and survival. Cancer
Epidemiol Biomarkers Prev 2005, 14(1):91-97.

28
Wirtenberger et al. Associatoins of genetic variants in the estrogen receptor coactivators
PPARGC1A, PPARGC1B, and EP300 with familial breast cancer.
Carcinogenesis 2006, 27(11):2201-2208.

Yu et al. Tumor susceptibility and prognosis of breast cancer associated with the G870A
polymorphism of CCND1. Breast Cancer Res Treat 2008, 107(1):95-102.

Abstract (if available)

Abstract Increased levels of steroid hormones have been implicated in the etiology of breast cancer. In a multiethnic panel we sequenced coding regions of 17 candidate coactivator and corepressor genes thought to interact with one's susceptibility to steroid hormone levels. Using unconditional logistic regression we evaluated whether nonsynonymous single nucleotide polymorphisms (SNPs) in these 17 genes were associated with breast cancer risk. While we did not find more than expected statistically significant associations of these SNPs with breast cancer risk in our combined, ethnic-specific, and phenotypic analyses, further studies should still be done to reproduce our findings as well as hopefully provide greater insight to the association of the rare SNPs and breast cancer risk. Survival analyses and mutational molecular sensitivity studies of the amino acid changes imposed by these SNPs, by measuring response to various breast cancer treatments, might also prove to be useful for improving phenotypic patient treatment.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Genes and hormonal factors involved in the development or recurrence of breast cancer

PDF

Pathogenic variants in cancer predisposition genes and risk of non-breast multiple primary cancers in breast cancer patients

PDF

Examining the relationship between common genetic variation, type 2 diabetes and prostate cancer risk in the multiethnic cohort

PDF

The effects of hormonal exposures on ovarian and breast cancer risk

PDF

The effects of tobacco exposure on hormone levels and breast cancer risk among young women

PDF

Air pollution and breast cancer survival in California teachers: using address histories and individual-level data

PDF

Diet quality and pancreatic cancer incidence in the multiethnic cohort

PDF

Associations between isoflavone soy protein (ISP) supplementation and breast cancer in postmenopausal women in the Women’s Isoflavone Soy Health (WISH) clinical trial

PDF

Predictive factors of breast cancer survival: a population-based study

PDF

Utility of polygenic risk score with biomarkers and lifestyle factors in the multiethnic cohort study

PDF

Using genetic ancestry to improve between-population transferability of a prostate cancer polygenic risk score

PDF

Differential role of two coactivators, CCAR1 and CARM1, for dysregulated beta-catenin activity in colorectal cancer cell growth and gene expression

PDF

Meat intake, polymorphisms in the NER and MMR pathways and colorectal cancer risk

PDF

The role of pesticide exposure in breast cancer

PDF

Arm lymphedema in a multi-ethnic cohort of female breast cancer survivors

PDF

Genetic variation in the base excision repair pathway, environmental risk factors and colorectal adenoma risk

PDF

Breast epithelial cell type specific enhancers and functional annotation of breast cancer risk loci

PDF

The role of heritability and genetic variation in cancer and cancer survival

PDF

An analysis of disease-free survival and overall survival in inflammatory breast cancer

PDF

Genes and environment in prostate cancer risk and prognosis

Asset Metadata

Creator Garcia, Rachel Rose (author)

Core Title Screening and association testing of coding variation in steroid hormone coactivator and corepressor genes in relationship with breast cancer risk in multiple populations

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics

Publication Date 02/23/2009

Defense Date 01/07/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag breast cancer,Epidemiology,genetic epidemiology,MEC,multiethnic cohort,OAI-PMH Harvest

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Haiman, Christopher A. (committee chair), Stallcup, Michael R. (committee member), Stram, Daniel O. (committee member)

Creator Email garcia.rachel@gmail.com,rachel.criswell@vanderbilt.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1978

Unique identifier UC1280985

Identifier etd-Garcia-2591 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-145074 (legacy record id),usctheses-m1978 (legacy record id)

Legacy Identifier etd-Garcia-2591.pdf

Dmrecord 145074

Document Type Thesis

Rights Garcia, Rachel Rose

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu