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

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

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GENETIC VARIATION IN THE BASE EXCISION REPAIR PATHWAY,

ENVIRONMENTAL RISK FACTORS AND COLORECTAL ADENOMA RISK

by

Roman Corral

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
(MOLECULAR EPIDEMIOLOGY)

August 2012

Copyright: 2012 Roman Corral
ii

DEDICATION
I dedicate this document to my parents, Mr. Roman Corral and Mrs. Yolanda Corral.

iii

ACKNOWLEDGEMENTS
I will be forever grateful to my mentor, Dr. Mariana C. Stern. Her insight, advice,
encouragement and patience have made this thesis possible. I would like to thank my
thesis committee members, Dr. Juan Pablo Lewinger and Dr. Sue Ingles for their advice
and suggestions at various stages of the thesis process. I am also indebted to Dr. Joseph
Hacia for helping me understand the complex processes involved in the regulation of
transcription and translation. I would also like to thank Ahva Shahabi, Amit Joshi and
Chelsea Catsburg. They are not only members of the Stern Lab but they are my friends.
Additionally, I would like to thank my brother Adrian and my sister Laura for their
support.

iv

TABLE OF CONTENTS
DEDICATION ii
ACKNOWLEDGEMENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
ABSTRACT vii
INTRODUCTION 1
METHODS 7
Study Subjects 7
SNP Selection 8
SNP Genotyping 9
Gene Main Effect Analysis 9
TagSNP x Environmental Risk Factor Interactions 12
RESULTS 20
BER Genes and Colorectal Adenoma Risk 20
BER Genes and Colorectal Adenoma Risk Taking Into Account Size
and Location 25
Genetic Variation in BER Genes, Adenoma Risk and Alcohol 28
Genetic Variation in BER Genes, Adenoma Risk and Dietary
Folate Intake 29
Genetic Variation in BER Genes, Adenoma Risk and Smoking 31

DISCUSSION 34
BIBLIOGRAPHY 53

v

LIST OF TABLES

Table 1: Base excision repair pathway genes investigated 15

Table 2: Genes/SNPs included in study and minor allele
frequencies 16

Table 3: Demographics and descriptive characteristics of cases
and controls 21

Table 4: Adenoma SNP associations among the entire study
population and by race/ethnicity 26

Table 5: Significant colorectal adenoma SNP associations by
polyp subsite 27

Table 6: Smoking, alcohol, folate and colorectal adenoma risk
by genotype 33

vi

LIST OF FIGURES

Figure 1: Single SNP analysis of the entire study population 24

vii

ABSTRACT
Three inter-related and consistent factors emerging from the epidemiological
literature for colorectal adenoma are cigarette smoking, alcohol intake, and dietary folate
levels. Oxidative damage caused by these three factors can be repaired through the base
excision repair pathway (BER). The ability to repair such damage may be modified by
common genetic variants in BER pathway genes. In a sigmoidoscopy based study, we
examined associations between 182 halplotype tagging single nucleotide polymorphisms
(SNPs), which captured common genetic variation in 14 BER genes, and colorectal
adenoma risk. Additionally, the interaction effects between SNPs and cigarette smoking,
alcohol intake, and dietary folate levels were examined.
Using logistic regression, per allele odds ratios (ORs) and 95% confidence
intervals (CIs) were estimated for the association between SNPs and colorectal adenoma
risk. Using multinomial logistic regression, per allele ORs and 95% CIs for each SNP
were calculated after stratifying on adenoma location (rectal versus left colon) and
adenoma size (< 1 cm versus ≥ 1 cm).
Significant associations were observed between SNPs in BER genes (FEN1,
NTHL1, APEX1) and colorectal adenoma risk. Significant associations were also
observed between SNPs in the NEIL2 gene and rectal adenoma risk. SNPs in BER genes
modified the effect of smoking (MUTYH), alcohol consumption (LIG3) and dietary folate
(LIG3, XRCC1) on colorectal adenoma risk. The findings support our hypothesis that
genetic variation in DNA repair genes can modify the association of key colorectal
adenoma risk factors. Our findings support a role for oxidative damage induced by these
exposures on colorectal cancer formation.
1

INTRODUCTION
In 2011, colorectal cancer will be diagnosed in approximately 141,210 individuals
and claim approximately 49,380 lives in the United States (ACS 2011). Colorectal cancer
is the third most common cancer diagnosed in men and women and is the third leading
cause of cancer death in the United States (Edwards, Ward et al. 2010; ACS 2011).
While non-Hispanic whites account for approximately 87% of all confirmed colorectal
cancer cases, African-American men and women have the highest incidence and
mortality rates in the United States (Edwards, Ward et al. 2010; ACS 2011). Those over
50 years of age accounted for approximately 90% of confirmed colorectal cancer cases in
the United States (Stewart, Wike et al. 2006; USPSTF 2007; Edwards, Ward et al. 2010).
Approximately 96% of all colorectal cancers are adenocarcinomas, with more than 80%
of colorectal cancers evolving from adenomatous polyps, also called adenomas (Stewart,
Wike et al. 2006; USPSTF 2007). The prevalence of adenomatous polyps is estimated at
25% by the age of 50 and 40% by 60 years of age (Vatn and Stalsberg 1982; Williams,
Balasooriya et al. 1982; Lieberman, Weiss et al. 2000).
Colorectal polyps that develop sporadically are histologically classified as
neoplastic or non-neoplastic. Adenomas are neoplastic polyps, having the potential to
become malignant. Most adenomas may never progress to colorectal cancer, but they are
believed to be precursors of carcinomas via an adenoma-carcinoma sequence. Less than
10% of adenomas progress to adenocarcinomas (Williams and Bedenne 1990; Stewart,
Wike et al. 2006; USPSTF 2007), and approximately 10-15 years are necessary for this
progression to occur (Morson 1974; Winawer, Zauber et al. 1993). Compared to small
(<1 cm) adenomas, larger ones (≥ 1 cm) have a greater potential to grow and progress
2

into colorectal cancer (Hoff, Foerster et al. 1986; Stryker, Wolff et al. 1987; Atkin,
Morson et al. 1992; Hofstad, Vatn et al. 1996). Use of sigmoidoscopy/colonoscopy
screening with polyp removal have significantly reduced both the risk for developing
colorectal cancer (~50%) and the mortality rates (~60%) of colorectal cancer (Atkin,
Morson et al. 1992; Winawer, Zauber et al. 1993; Muller and Sonnenberg 1995; Muller
and Sonnenberg 1995; Selby, Friedman et al. 2001). Three factors have emerged from the
epidemiological literature as inter-related and consistent modifiers of adenoma risk:
cigarette smoking, alcohol intake, and dietary folate levels. We discuss them below,
highlighting their contribution to the accumulation of DNA damage in the colon.
According to IARC, there is now sufficient evidence that tobacco smoking causes
cancer of the colon (Secretan, Straif et al. 2009). A recent systematic review and meta-
analysis provides more evidence that smoking is associated with increased colorectal
cancer risk (Liang, Chen et al. 2009). Over the last 20 years, many epidemiological
studies have consistently reported on cigarette smoking as a risk factor for colorectal
adenoma occurrence (Lee, Neugut et al. 1993; Honjo, Kono et al. 1995; Todoroki, Kono
et al. 1995; Hoshiyama, Kono et al. 2000; Giovannucci 2001; Erhardt, Kreichgauer et al.
2002; Anderson, Attam et al. 2003; Reid, Marshall et al. 2003; Toyomura, Yamaguchi et
al. 2004; Abrams, Terry et al. 2008; Shrubsole, Wu et al. 2008; Anderson, Latreille et al.
2009; Hassan, Pickhardt et al. 2010; Burnett-Hartman, Newcomb et al. 2011; Martinez,
Fernandez-Martos et al. 2011; Shin, Hong et al. 2011). A pooled analysis (Terry, Neugut
et al. 2002) and a meta-analysis of 42 observational studies (Botteri, Iodice et al. 2008)
confirmed this association. Relative to never smokers, current and ever smokers
experienced an approximately 2 fold increased risk of colorectal polyps, with smoking
3

accounting for 20 to 25 percent of colorectal polyps (Botteri, Iodice et al. 2008). An
increase in the number of years of smoking and cigarettes/day has been associated with
an increase in the risk of larger adenomas, as well as multiple adenomas across the
colorectum (Giovannucci, Colditz et al. 1994; Giovannucci, Rimm et al. 1994; Anderson,
Attam et al. 2003; Reid, Marshall et al. 2003; Toyomura, Yamaguchi et al. 2004; Botteri,
Iodice et al. 2008; Shin, Hong et al. 2011).
Cigarette smoke contains reactive oxygen species (ROS) as well as over 60
known carcinogens (Pryor 1997). Many of these, such as polycyclic aromatic
hydrocarbons, nitrosamines, aromatic amines, and heterocyclic amines can induce DNA
bulky adducts which can cause DNA damage if left unrepaired. ROS from smoking are
capable of initiating lipid peroxidation, oxidizing proteins, and damaging DNA directly
(single strand breaks, double strand breaks, abasic sites, and base adduct formation)
(Pryor 1997; Friedberg and ebrary Inc. 2006).
Another contribution of smoking to adenoma risk is through the observed
association between smoking and decreased serum folate, red blood cell folate levels and
circulating maternal folate concentrations (Baron, Sandler et al. 1998; Walmsley, Bates et
al. 1999; McDonald, Perkins et al. 2002; Cogswell, Weisberg et al. 2003; Mannino,
Mulinare et al. 2003; Stark, Pawlosky et al. 2005; Baker, Wheeler et al. 2009). It is
unclear how smoking may directly or indirectly modify folate metabolism or uptake. The
B-vitamin folate is involved in DNA synthesis, replication, methylation and cellular
repair. Folate deficiency has been shown to increase DNA damage in in-vivo and in-vitro
studies using human cells (Blount and Ames 1995; Fenech 2001). Folate deficiency has
also been associated with adenoma risk (Duthie, Narayanan et al. 2004). However, folate
4

supplementation also appears to promote progression for those with established colorectal
neoplasms (Kim 2007; Sanderson, Stone et al. 2007; Mason 2009).
Increasing intake of alcohol has also been shown to reduce folate levels (Seitz,
Simanowski et al. 1990; Baron, Sandler et al. 1998). Increasing alcohol consumption can
decrease levels of glutathione, a cellular antioxidant, in mitochondria, as well as inhibit
its synthesis in the cytosol and transport into the mitochondria (Das and Vasudevan
2007). Decreased immune surveillance (Kune and Vitetta 1992; Poschl and Seitz 2004;
Das and Vasudevan 2007), and changes in bile composition (Kune and Vitetta 1992) are
other undesirable effects of alcohol consumption. The relationship between alcohol
consumption and adenoma risk is inconclusive (Riboli, Cornee et al. 1991; Giovannucci,
Stampfer et al. 1993; Lee, Neugut et al. 1993; Olsen and Kronborg 1993; Sandler, Lyles
et al. 1993; Honjo, Kono et al. 1995; Martinez, McPherson et al. 1995; Longnecker, Chen
et al. 1996; Baron, Sandler et al. 1998; Nagata, Shimizu et al. 1999; Breuer-Katschinski,
Nemes et al. 2000; Erhardt, Kreichgauer et al. 2002; Tiemersma, Wark et al. 2003;
Toyomura, Yamaguchi et al. 2004; Anderson, Alpern et al. 2005; Stern, Siegmund et al.
2006; Austin, Galanko et al. 2008; Shrubsole, Wu et al. 2008). Yet, according to IARC,
there is sufficient evidence that alcohol consumption may cause cancer of the colorectum
(Secretan, Straif et al. 2009). The World Cancer Research Fund/American Institute for
Cancer Research concluded more than 30 g/day of alcohol consumption was a convincing
colorectal risk factor in men and a probable risk factor in women (American Institute for
Cancer Research. and World Cancer Research Fund. 2007).
Alcohol metabolism occurs through three free radical producing pathways that
utilize alcohol dehydrogenase, microsomal ethanol oxidation system and catalase (Das
5

and Vasudevan 2007). The alcohol dehydrogenase pathway can generate acetaldehyde, a
known carcinogen and mutagen, and free radicals in the colon lumen (Kune and Vitetta
1992; Poschl and Seitz 2004). Peroxisomal metabolism, possibly more common among
heavy drinkers, gives rise to acetaldehyde (Das and Vasudevan 2007). Acetaldehyde
directly inhibits O
6
methylguanine-methyl-transferase, an enzyme involved in removal of
alkylating damage (Poschl and Seitz 2004; Seitz, Maurer et al. 2005). Acetaldehyde
directly affects colonic mucosa leading to hyper regeneration of crypt cells (Poschl,
Stickel et al. 2004; Seitz, Maurer et al. 2005). DNA synthesis and repair can be disrupted
by acetaldehyde, which forms DNA adducts, inter-strand cross-links and induces double
strand breaks (Poschl and Seitz 2004; Brooks and Theruvathu 2005; Seitz, Maurer et al.
2005). Reactive oxygen species that accumulate as a by-product of alcohol metabolism
can lead to lipid peroxidation and generation of DNA protein adducts (Seitz, Maurer et al.
2005; Das and Vasudevan 2007). In addition, alcohol metabolism through the
microsomal ethanol oxidation system involves cytochrome p450 enzymes, whose
induction can increases activation of pro-carcinogens into carcinogens as well as generate
reactive oxygen species (Seitz, Simanowski et al. 1990; Kune and Vitetta 1992; Poschl
and Seitz 2004; Seitz, Maurer et al. 2005; Das and Vasudevan 2007). Among the pro-
carcinogens converted are chemicals found in cigarette smoke, such as nitrosamines and
polycyclic hydrocarbons.
Overall, smoking, alcohol consumption and decreased folate availability can
cause oxidative damage and uracil incorporation into DNA. The base excision repair
(BER) pathway repairs abasic sites, base damage, and single-strand breaks. DNA damage
repair is variable in the population. Common genetic variants in genes that play key roles
6

in DNA repair pathways may explain part of this variability. In this study, we examined
potential associations between single nucleotide polymorphisms (SNPs) in genes
involved in BER and adenoma risk, and considered their role as potential modifiers of the
effect of cigarette smoking, alcohol consumption, and dietary folate intake. Using data
and samples from 721 cases and 736 controls from a sigmoidoscopy-based study
conducted in Los Angeles County, we implemented a tagSNP approach to
comprehensively investigate the genetic variation in 14 genes involved in the BER
pathway and colorectal adenoma risk.

7

METHODS
Study Subjects
Study participants were enrolled in a University of Southern California/Kaiser
Permanente study of risk factors for colorectal adenomas. All individuals were examined
by flexible sigmoidoscopy from 1991 to 1993 (phase I) and from 1993 to 1995 (phase II)
at one of two southern California Kaiser Permanente clinics (Bellflower or Sunset). Phase
II subjects were recruited using identical criteria as phase I subjects. Characteristics of
this study have been previously described (Stern, Siegmund et al. 2005; Stern, Siegmund
et al. 2006). Briefly, eligible individuals were members of Kaiser Permanente who spoke
English, were between 50 and 74 years old, and lived in the Los Angeles metropolitan
area. Eligible individuals did not have a history of invasive cancer, inflammatory bowel
disease, familial polyposis, previous bowel surgery, or symptoms suggestive of
gastrointestinal disease. Cases were individuals with a first-time diagnosis of a
histologically confirmed adenoma. Data regarding number, size, and location of
adenomas was obtained from Kaiser Permanente pathology reports. Controls were
selected the remaining eligible individuals who did not present with polyps at
sigmoidoscopy examination and had no past history of histologically confirmed
adenomas. Cases were individually matched to controls by gender, age (within 5 years),
sigmoidoscopy date (within 3 months), and Kaiser Permanente clinic. All subjects signed
a written informed consent approved by the Institutional Review Board, donated a blood
sample, and completed two questionnaires. Interviewers were blinded to the case-control
status of subjects. A risk factor questionnaire was administered during an in-person
interview, on average, five months after sigmoidoscopy examination. This risk factor
8

questionnaire collected data on demographics, smoking history, family history of cancer,
physical activity and other factors described previously (Lin, Probst-Hensch et al. 1995;
Longnecker, Chen et al. 1996; Levine, Lee et al. 2011). A semi-quantitative food
frequency questionnaire was administered which allowed the estimation of nutrient intake
during the year before sigmoidoscopy examination, as previously described (Enger,
Longnecker et al. 1996; Haile, Witte et al. 1997; Lin, Probst-Hensch et al. 1998).

SNP Selection
TagSNPs for each BER gene were selected using Haploview Tagger (Barrett, Fry
et al. 2005) based on the HapMap CEPH (CEU) population using the following criteria:
minor allele frequency (MAF) ≥ 5%, pairwise r
2
≥ 0.95, and a distance from the closest
SNP greater than 60 base pairs on the Illumina platform. Linkage disequilibrium blocks
were defined using data from HapMap data release #16c.1, June 2005, on NCBI B34
assembly, dbSNP b124. For each gene, the 5ʹ′-UTR and 3ʹ′-UTR regions were extended to
include the 5ʹ′-most and 3ʹ′-most SNP within the linkage disequilibrium (LD) block (~10
kb upstream and ~5 kb downstream). In regions of no or low LD, tagSNPs with a MAF ≥
5% at a density of ~ 1 per kb were selected from either HapMap or dbSNP. Finally, non-
synonymous tagSNPs and investigator selected SNPs, regardless of the MAF, were
included. In this analysis, we report our results for 182 tagSNPs across 14 genes that
participate in the BER pathway (Table 1). All 182 tagSNPs included in this study are
listed in Table 2.

9

SNP Genotyping
TagSNPs were genotyped on the Illumina GoldenGate platform (Shen, Fan et al.
2005). A series of quality control checks were implemented based on Illumina metrics,
and tagSNPs were excluded from analysis based on the following criteria: GenTrain
score < 0.4, 10% GC score < 0.25, AB T Dev> .1239, call rate < 0.95, > 2 Mendelian
errors or discordance with HapMap > 3. TagSNPs were also excluded from analysis, if
among intra-plate and inter-plate replicates, there were more than two errors found for
replicate genotypes. Genotype data from 30 CEPH trios (Coriell Cell Repository,
Camden, NJ) was also used to confirm genotyping reliability and reproducibility.
TagSNPs were excluded if more than 3 discordant genotypes were found in comparison
with genotypes from the International HapMap Project (TIH 2005). Among the BER
genes in our study, we required that all 182 tagSNPs have call rates ≥ 0.90 and a p-value
≥ 0.00027 from exact tests (Bonferroni-corrected p-value; α = 0.05/182) when testing
deviations of observed genotype frequencies from those expected, assuming Hardy-
Weinberg equilibrium. All tagSNPs met these two requirements. Additionally, we
required all individuals have overall call rates ≥ 90%. We excluded from analyses 89
individuals with overall call rates less than 90%. After excluding subjects with call rates
less than 90%, we had genotyping results available for 1,368 of 1,457 total subjects in
this study.

Gene Main Effect Analyses
MAF were calculated using genotype data from controls. Deviations of observed
genotype frequencies from those expected when assuming HWE were examined among
10

controls by race/ethnicity using exact tests. We did not observe evidence of statistically
significant deviations of observed from expected values after using a Bonferroni
correction to account for the total number of tagSNPs. Haploview was used to estimate
pairwise linkage disequilibrium between tagSNPs (Barrett, Fry et al. 2005). The square of
the correlation coefficient (R
2
) between markers was used to estimate pairwise linkage
disequilibrium. We used unconditional logistic regression, assuming a log-additive
model, to estimate per allele odds ratios (ORs) and corresponding 95% confidence
intervals (95% CIs) for the association between genotype and adenoma risk. This allowed
us to use all cases regardless of whether a control was available for individual matching.
We have previously reported that similar results can be obtained when using
unconditional logistic regression adjusting for the matching factors as using conditional
logistic regression, with the benefit of using all genotype information in the study
population (Levine, Siegmund et al. 2000; Stern, Siegmund et al. 2005; Stern, Siegmund
et al. 2006). All gene main effect models were adjusted for race (in non-race/ethnic group
specific analysis, Non-Hispanic whites, Latinos, African-American, Asian/Pacific
Islander), study phase (phase I/phase II), age at sigmoidoscopy (continuous), sex, exam
date, and clinic (Bellflower or Sunset). Additional control for the following adenoma risk
factors did not change the results by more than 10%; therefore, these covariates were not
included in final analyses: alcohol intake (g/day), smoking status, dietary folate intake
(mcg/day), body mass index, multivitamin use (yes/no), total caloric intake, total dietary
fiber (g/day).
We used an approach to address issues of multiple comparisons and minimize the
rate of false positives that considered corrections for multiple testing within each gene
11

region as well as across all gene regions investigated in the BER pathway. Specifically,
within each gene region, p-values for each tagSNP obtained under a log-additive model
were corrected for multiple testing taking into account correlated tagSNPs implemented
in the R package P
ACT
(p-value adjusted for correlated tests) (Conneely and Boehnke
2007). Statistical significance for tagSNPs within each gene region was determined at the
α-level of 0.05 for P
ACT
.

We further corrected for overall significance across the 14 gene
regions (p
pathway
) investigated in the BER pathway using a Bonferroni correction of the
P
ACT
corrected p-value. Statistical significance for tagSNPs across all genetic regions was
determined at the α-level of 0.05 for Bonferroni corrected p
pathway
(Storey and Tibshirani
2003). We assessed potential heterogeneity of gene main effects by race by including
interaction variables between genotypes and race in logistic regression models and
comparing to a model without the interaction variable using likelihood ratio tests with 3
degrees of freedom (df).
Multinomial logistic regression was used to examine gene main effects by
adenoma location (rectal versus left colon) and adenoma size (< 1 cm versus ≥ 1 cm) with
respect to the control group. TagSNPs coded assuming a log-additive model were used to
obtain estimates of per allele ORs and corresponding 95% CIs. For each tagSNP, a
likelihood ratio test was calculated to determine the p-value for the heterogeneity of the
odds ratios. Gene-main effect p-values for stratified analyses were corrected using P
ACT
,
as described above for overall gene main effect analyses. For all gene main effect
heterogeneity tests, we report stratified analyses by each of the variables considered when
the P
ACT
< 0.05, irrespective of the heterogeneity test p-value.

12

TagSNP x Environmental Risk Factor Interactions
We investigated if tagSNPs in genes involved in BER could modify the effect of
alcohol use, dietary folate intake, and smoking in the total study population and among
non-Hispanic Whites. Gene-smoking interactions considered the following smoking
variables: never/ever, never/quit/current, years of smoking (three-level variable, using
median among smoking controls as cut point: 0 years, ≤ 26 years, > 26 years), pack-years
smoked (three-level variable, using median among smoking controls as cut point: 0 pack-
years, ≤ 21 pack-years, > 21 pack-years). For gene-alcohol interactions, we considered
the following alcohol variables: never/ever drinker, number of drinks per day (never, ≤ 1
drink/day, > 1 drink/day) and median daily alcohol intake (a three-level variable, using
median intake among drinking controls as a cut point: 0 g/day, ≤ 6 g/day, > 6 g/day). One
alcoholic drink per day was defined as approximately up to 15 grams of ethanol per day.
For dietary folate-gene interactions, we considered the following dietary folate variables:
low/high dietary intake (a dichotomous variable, using the median dietary folate intake
among controls: ≤ 317 mcg/day versus > 317 mcg/day) and low/medium/high dietary
intake (a three level variable, using tertiles of dietary folate intake among controls as cut
points: ≤ 267 mcg/day, > 267-387 mcg/day, ≥ 388 mcg/day).
Analyses of gene by environment interactions were conducted using
unconditional logistic regression models that included product terms between tagSNPs
and each environmental exposure using likelihood ratio tests. These models were
adjusted for race (in non-race/ethnic group specific analysis, non-Hispanic whites,
Latinos, African-American, Asian/Pacific Islander), study phase (phase I/phase II), age at
sigmoidoscopy (continuous), sex, exam date, and clinic (Bellflower or Sunset).
13

Additionally, gene-smoking interactions were adjusted for alcohol and dietary folate
intake. Gene-alcohol interactions were additionally adjusted for smoking and dietary
folate intake. Gene-dietary folate interactions were additionally adjusted for alcohol
intake and smoking. Further adjustment for body mass index, multivitamin use (yes/no),
total caloric intake, or total dietary fiber (g/day) produced almost identical estimates;
therefore we did not keep these variables in the models.
For dichotomous exposures, tests for interaction were conducted by including
product terms between a tagSNP (assuming a log-additive mode of action) and each
dichotomous exposure, using a likelihood ratio test (1df) that compared the model with
the interaction term to a model without the interaction term. For exposures using tertile
variables, tests for interaction were conducted that included product terms between each
tagSNP (assuming a log-additive mode of action) and a three level exposure variable
coded with dummy variables (coded as 0/1/2: using the median levels of exposure among
the controls for each of the levels). Tests of trend across categories of (smoke, alcohol,
dietary folate) exposure were conducted by assigning those median values to every tertile
of exposure and modeling the categories as continuous. To assess interaction significance
for three level variables, we conducted tests for the heterogeneity of trend of the exposure
effect on outcome among subjects with 1 or more copies of the variant allele vs. trend of
the exposure effect among subjects with no copies of the variant allele.
In the gene-environment interaction analysis, we address the multiple
comparisons issue both within each gene region as well as across all gene regions in the
BER pathway by applying two Bonferroni corrections to the crude interaction p-values.
Within each gene region (interaction p
gene
), a Bonferroni correction was applied by
14

multiplying each tagSNP’s crude interaction p-value by the number of tagSNPs in its
respective gene region. Interaction p
gene
< 0.05 were considered statistically significant.
Furthermore, for significance across all 14 gene regions investigated in the BER pathway
(interaction p
pathway
), a Bonferroni correction was applied by multiplying each interaction
p
gene
value by the 14 BER gene regions investigated. Interaction p
pathway
< 0.05 were
considered statistically significant. All tests conducted were two sided and all statistical
analysis were conducted using Stata 11 SE (Stata Corporation, College Station, TX) and
the R programming language (The R Project for Statistical Computing, http://www.r-
project.org).

15

Table 1. Base excision repair pathway genes investigated.
Gene Symbol # tagSNPs Chromosomal location Protein Function
APEX1 13 14q11.2 Endonuclease
FEN1 7 11q12 Endonuclease
LIG1 23 19q13.2-q13.3 Ligase
LIG3 7 17q11.2-q12 Ligase
MUTYH 7 1p34.1
Glycosylase
NEIL1 4 15q33.33
Glycosylase
NEIL2 43 8p23.1
Glycosylase
NTHL1 9 16p13.3 Endonuclease and Glycosylase
OGG1 7 3p26
Glycosylase
PARP1 18 1q41-q42 poly(ADP-ribosyl)ation
POL! 6 8p12-p11 Polymerase
POLD1 7 19q13.3 Polymerase
SMUG1 10 12q13.11-13.3
Glycosylase
XRCC1 21 19q13.2 Scaffolding protein
Genes are listed according to HUGO gene nomenclature format (http://www.genenames.org)
Abbreviations: APEX1, APEX nuclease (multifunctional DNA repair enzyme) 1; FEN1, flap structure-
specific endonuclease 1; LIG1, ligase I, DNA, ATP-dependent; LIG3, ligase III, DNA, ATP-dependent;
MUTYH, mutY homolog (E. coli); NEIL1, nei endonuclease VIII-like 1 (E. coli); NEIL2, nei
endonuclease VIII-like 2 (E. coli); NTHL1, nth endonuclease III-like 1 (E. coli); OGG1, 8-oxoguanine
DNA glycosylase ; PARP, poly (ADP-ribose) polymerase 1; POL!, polymerase (DNA directed),beta;
POLD1, polymerase (DNA directed), delta 1, catalytic subunit 125kDa; SMUG1, single-strand-selective
monofunctional uracil-DNA glycosylase 1; XRCC1, X-ray repair complementing defective repair in
Chinese hamster cells 1.
16

Table 2. Genes/SNPs included in study and minor allele frequencies
Gene SNP Chromosome Position
Minor
Allele N MAF
b
N MAF
b
N MAF
b
N MAF
b
N MAF
b
APEX1 rs11160682 14 19978193 G 1358 0.38 715 0.38 233 0.37 256 0.36 154 0.42
APEX1 rs938891 14 19978500 C 1350 0.39 713 0.35 227 0.42 256 0.40 154 0.50
APEX1 rs938889 14 19978581 T 1348 0.42 709 0.41 230 0.39 256 0.44 153 0.51
APEX1 rs2319196 14 19979656 A 1362 0.20 720 0.23 232 0.14 256 0.21 154 0.09
APEX1 rs17111750 14 19980248 T 1359 0.29 721 0.32 231 0.26 254 0.27 153 0.24
APEX1 rs7140314 14 19983132 G 1351 0.43 719 0.40 222 0.43 256 0.47 154 0.52
APEX1 rs4465523 14 19983363 A 1363 0.34 720 0.36 233 0.30 257 0.31 153 0.39
APEX1 rs4429194 14 19983547 G 1344 0.44 711 0.41 227 0.48 254 0.48 152 0.52
APEX1 rs2275008 14 19986089 C 1350 0.26 709 0.27 232 0.33 256 0.26 153 0.10
APEX1 rs938883 14 19986798 T 1363 0.46 719 0.45 233 0.45 257 0.53 154 0.53
APEX1 rs999692 14 19987505 C 1355 0.36 717 0.35 231 0.40 255 0.33 152 0.39
APEX1 rs1320150 14 19989883 C 1364 0.43 720 0.41 233 0.41 257 0.47 154 0.51
APEX1 rs1760941 14 19999878 C 1361 0.35 719 0.25 233 0.55 256 0.34 153 0.59
FEN1 rs108499 11 61303813 T 1354 0.36 718 0.31 231 0.12 253 0.55 152 0.64
FEN1 rs509360 11 61305135 A 1362 0.38 720 0.32 233 0.78 256 0.21 153 0.33
FEN1 rs174532 11 61305450 A 1357 0.22 718 0.32 231 0.05 254 0.19 154 0.01
FEN1 rs740006 11 61314444 C 1352 0.07 711 0.10 233 0.03 255 0.03 153 0.00
FEN1 rs412334 11 61316837 T 1362 0.11 720 0.16 233 0.03 256 0.10 153 0.00
FEN1 rs695867 11 61317864 G 1359 0.02 717 0.03 232 0.01 256 0.03 154 0.01
FEN1 rs174545 11 61325882 G 1363 0.37 721 0.32 233 0.12 256 0.55 153 0.65
LIG1 rs251692 19 53310523 A 1357 0.37 718 0.45 233 0.20 254 0.47 152 0.10
LIG1 rs274883 19 53314357 G 1345 0.30 712 0.19 229 0.72 251 0.26 153 0.33
LIG1 rs156645 19 53317024 A 1363 0.33 721 0.36 232 0.09 257 0.27 153 0.57
LIG1 rs3731014 19 53317604 A 1361 0.15 720 0.13 232 0.26 255 0.16 154 0.17
LIG1 rs3731007 19 53318416 A 1363 0.11 721 0.06 233 0.30 256 0.09 153 0.16
LIG1 rs156641 19 53323220 T 1360 0.34 718 0.36 233 0.12 256 0.28 153 0.57
LIG1 rs156639 19 53323649 G 1365 0.37 721 0.45 233 0.19 257 0.46 154 0.10
LIG1 rs754948 19 53325659 T 1362 0.09 720 0.05 232 0.23 256 0.08 154 0.16
LIG1 rs2241721 19 53325976 C 1358 0.34 715 0.36 233 0.13 257 0.28 153 0.57
LIG1 rs3786764 19 53327245 C 1358 0.47 718 0.42 232 0.48 256 0.38 152 0.74
LIG1 rs3730912 19 53344647 T 1362 0.16 720 0.14 233 0.27 256 0.15 153 0.17
LIG1 rs8100261 19 53347571 T 1363 0.37 720 0.46 233 0.18 256 0.47 154 0.10
LIG1 rs3730898 19 53347684 A 1356 0.47 717 0.51 231 0.44 255 0.54 153 0.26
LIG1 rs3730895 19 53348383 G 1362 0.50 720 0.48 232 0.44 256 0.45 154 0.73
LIG1 rs3730849 19 53360607 A 1353 0.34 715 0.36 233 0.11 254 0.29 151 0.56
LIG1 rs2386522 19 53361084 C 1363 0.48 720 0.49 232 0.56 257 0.53 154 0.27
LIG1 rs274869 19 53369960 A 1362 0.40 720 0.48 232 0.26 257 0.49 153 0.10
LIG1 rs274873 19 53371521 T 1357 0.43 717 0.49 231 0.35 255 0.51 154 0.11
LIG1 rs10405655 19 53374591 C 1360 0.18 717 0.15 233 0.30 256 0.18 154 0.17
LIG1 rs11083918 19 53374843 G 1364 0.44 721 0.48 232 0.42 257 0.51 154 0.26
LIG1 rs4802436 19 53376624 G 1364 0.15 720 0.21 233 0.05 257 0.11 154 0.06
LIG1 rs2007183 19 53377910 T 1357 0.18 718 0.14 232 0.31 256 0.17 151 0.16
LIG1 rs10421339 19 53380180 G 1364 0.41 720 0.48 233 0.28 257 0.50 154 0.11
LIG3 rs1351554 17 30321178 T 1361 0.20 718 0.13 233 0.55 256 0.13 154 0.16
LIG3 rs3135962 17 30335005 C 1362 0.03 718 0.06 233 0.01 257 0.01 154 0.00
LIG3 rs3135989 17 30344256 G 1359 0.09 719 0.06 232 0.03 255 0.16 153 0.12
Total
a
Non-Hispanic African- Hispanic Asian-Pacific
White American Islander
17

Table 2. Genes/SNPs included in study and minor allele frequencies (contd)
Gene SNP Chromosome Position
Minor
Allele N MAF
b
N MAF
b
N MAF
b
N MAF
b
N MAF
b
LIG3 rs3135998 17 30346435 A 1362 0.32 720 0.39 232 0.10 256 0.33 154 0.33
LIG3 rs4796030 17 30354263 A 1359 0.39 718 0.42 233 0.16 254 0.46 154 0.50
LIG3 rs1052536 17 30355688 T 1364 0.38 720 0.45 233 0.17 257 0.38 154 0.29
LIG3 rs3744358 17 30361027 G 1362 0.27 720 0.29 233 0.20 255 0.38 154 0.14
MUTYH rs10890324 1 45563741 G 1364 0.31 720 0.28 233 0.67 257 0.22 154 0.13
MUTYH rs3219487 1 45571142 T 1358 0.08 716 0.09 233 0.04 257 0.07 152 0.07
MUTYH rs3219484 1 45572743 T 1361 0.05 721 0.08 232 0.01 256 0.02 151 0.00
MUTYH rs3219474 1 45576000 G 1365 0.04 721 0.07 233 0.04 257 0.02 153 0.00
MUTYH rs3893383 1 45583510 A 1358 0.14 717 0.10 232 0.27 256 0.11 153 0.22
MUTYH rs2185549 1 45586690 C 1351 0.31 715 0.26 230 0.28 253 0.41 153 0.39
MUTYH rs4660849 1 45592459 A 1363 0.32 720 0.27 232 0.28 257 0.42 154 0.39
NEIL1 rs3809547 15 73415894 C 1343 0.32 712 0.27 226 0.53 253 0.27 152 0.34
NEIL1 rs11853141 15 73419920 T 1349 0.44 716 0.43 229 0.21 254 0.55 150 0.54
NEIL1 rs4462560 15 73435017 G 1360 0.34 717 0.28 232 0.54 257 0.29 154 0.38
NEIL1 rs3809549 15 73438025 G 1356 0.45 718 0.48 231 0.19 254 0.50 153 0.55
NEIL2 rs13262643 8 11649688 C 1296 0.24 679 0.16 226 0.23 241 0.28 150 0.51
NEIL2 rs13273672 8 11649790 C 1363 0.34 720 0.30 233 0.36 256 0.36 154 0.54
NEIL2 rs804280 8 11650107 A 1362 0.37 720 0.45 233 0.42 255 0.28 154 0.03
NEIL2 rs3729851 8 11650251 A 1363 0.09 720 0.15 233 0.04 257 0.08 153 0.00
NEIL2 rs4841588 8 11651634 T 1345 0.22 712 0.13 230 0.19 253 0.27 150 0.64
NEIL2 rs3757949 8 11652407 C 1350 0.29 711 0.26 233 0.39 254 0.25 152 0.42
NEIL2 rs1062219 8 11653819 C 1361 0.35 719 0.46 231 0.21 257 0.29 154 0.04
NEIL2 rs12825 8 11653956 G 1355 0.42 718 0.41 232 0.43 253 0.39 152 0.54
NEIL2 rs11785481 8 11654551 T 1358 0.12 718 0.14 230 0.05 256 0.11 154 0.03
NEIL2 rs12458 8 11654649 T 1355 0.35 715 0.31 231 0.39 255 0.35 154 0.49
NEIL2 rs3203358 8 11654914 G 1339 0.23 705 0.33 230 0.07 254 0.20 150 0.06
NEIL2 rs9329248 8 11655516 A 1362 0.24 719 0.16 233 0.39 257 0.24 153 0.49
NEIL2 rs13281294 8 11656614 G 1353 0.11 714 0.13 232 0.04 254 0.10 153 0.03
NEIL2 rs809203 8 11656913 A 1328 0.30 697 0.34 230 0.10 249 0.36 152 0.27
NEIL2 rs7015453 8 11657395 T 1348 0.22 717 0.17 228 0.30 251 0.22 152 0.41
NEIL2 rs1004712 8 11659702 T 1363 0.41 719 0.38 233 0.48 257 0.39 154 0.53
NEIL2 rs17153785 8 11660274 G 1359 0.13 718 0.11 233 0.06 255 0.19 153 0.22
NEIL2 rs17810889 8 11660645 G 1248 0.33 659 0.35 212 0.24 236 0.37 141 0.27
NEIL2 rs1466785 8 11660865 T 1357 0.38 717 0.41 232 0.20 254 0.37 154 0.52
NEIL2 rs804279 8 11661298 A 1361 0.27 720 0.25 232 0.38 256 0.25 153 0.23
NEIL2 rs17754589 8 11663217 T 1361 0.17 720 0.22 231 0.09 257 0.15 153 0.02
NEIL2 rs904009 8 11665538 C 1364 0.19 721 0.24 233 0.12 256 0.17 154 0.02
NEIL2 rs2686211 8 11665784 T 1335 0.27 705 0.32 230 0.30 248 0.21 152 0.03
NEIL2 rs8191529 8 11666135 C 1365 0.06 721 0.09 233 0.01 257 0.05 153 0.00
NEIL2 rs804267 8 11666650 G 1365 0.32 721 0.34 233 0.52 257 0.24 154 0.03
NEIL2 rs8191542 8 11667218 G 1336 0.17 712 0.22 231 0.15 252 0.12 140 0.00
NEIL2 rs4124646 8 11669441 A 1357 0.06 716 0.10 231 0.01 256 0.04 153 0.00
NEIL2 rs8191589 8 11671603 A 1365 0.17 721 0.22 233 0.16 257 0.13 153 0.00
NEIL2 rs8191598 8 11673025 G 1360 0.16 718 0.23 233 0.06 255 0.14 154 0.02
NEIL2 rs4840584 8 11673426 A 1354 0.07 713 0.11 231 0.03 256 0.04 153 0.00
NEIL2 rs804256 8 11674271 C 1275 0.31 660 0.34 222 0.10 246 0.36 147 0.30
Total
a
Non-Hispanic African- Hispanic Asian-Pacific
White American Islander
18

Table 2. Genes/SNPs included in study and minor allele frequencies (contd)
Gene SNP Chromosome Position
Minor
Allele N MAF
b
N MAF
b
N MAF
b
N MAF
b
N MAF
b
NEIL2 rs4840585 8 11675770 G 1364 0.06 720 0.10 233 0.01 257 0.04 153 0.00
NEIL2 rs1874546 8 11676287 G 1348 0.25 713 0.21 231 0.16 252 0.31 152 0.42
NEIL2 rs8180912 8 11677400 T 1327 0.22 705 0.19 225 0.12 249 0.29 148 0.39
NEIL2 rs8191649 8 11678816 T 1362 0.17 719 0.21 233 0.15 257 0.18 153 0.02
NEIL2 rs6982453 8 11679280 T 1360 0.50 717 0.53 233 0.47 257 0.51 153 0.28
NEIL2 rs8191663 8 11680868 T 1359 0.24 718 0.22 232 0.30 257 0.21 152 0.32
NEIL2 rs1534862 8 11681212 T 1345 0.23 710 0.21 230 0.29 252 0.21 153 0.28
NEIL2 rs1043180 8 11682230 T 1362 0.08 718 0.12 233 0.02 257 0.08 154 0.01
NEIL2 rs904015 8 11684343 T 1358 0.31 716 0.34 232 0.29 257 0.26 153 0.27
NEIL2 rs2088349 8 11685476 A 1299 0.32 687 0.30 224 0.35 242 0.30 146 0.39
NEIL2 rs2645447 8 11686283 G 1359 0.25 717 0.20 232 0.24 256 0.26 154 0.39
NEIL2 rs2686184 8 11686768 A 1333 0.42 703 0.41 228 0.50 251 0.37 151 0.41
NTHL1 rs2516781 16 2025681 T 1352 0.31 716 0.29 230 0.19 255 0.38 151 0.37
NTHL1 rs2531210 16 2025998 A 1333 0.15 703 0.14 227 0.06 253 0.26 150 0.16
NTHL1 rs12447809 16 2027461 T 1326 0.20 696 0.18 227 0.40 251 0.18 147 0.02
NTHL1 rs11876 16 2028313 T 1354 0.16 715 0.19 232 0.13 255 0.16 150 0.01
NTHL1 rs2516740 16 2037111 C 1357 0.23 716 0.21 230 0.47 257 0.20 154 0.01
NTHL1 rs8059880 16 2041592 C 1363 0.07 719 0.08 233 0.05 257 0.06 153 0.00
NTHL1 rs2516734 16 2045336 G 1358 0.14 719 0.13 230 0.30 257 0.13 152 0.01
NTHL1 rs2074968 16 2050572 G 1280 0.47 682 0.45 214 0.57 237 0.53 147 0.25
NTHL1 rs2074969 16 2051231 G 1353 0.49 715 0.49 233 0.23 253 0.55 152 0.79
OGG1 rs159159 3 9752158 C 1341 0.28 707 0.28 231 0.52 251 0.21 152 0.15
OGG1 rs159157 3 9753315 C 1364 0.13 720 0.12 233 0.03 257 0.11 154 0.28
OGG1 rs2269112 3 9763168 T 1357 0.16 718 0.16 231 0.04 254 0.20 154 0.17
OGG1 rs159153 3 9764875 C 1360 0.28 718 0.30 232 0.36 256 0.31 154 0.15
OGG1 rs125701 3 9765478 A 1346 0.12 710 0.15 231 0.07 253 0.11 152 0.04
OGG1 rs2072668 3 9773140 G 1360 0.29 720 0.21 233 0.24 255 0.35 152 0.57
OGG1 rs293796 3 9784082 T 1364 0.10 720 0.10 233 0.02 257 0.10 154 0.22
PARP1 rs12567614 1 224611043 T 1363 0.47 719 0.59 233 0.39 257 0.46 154 0.13
PARP1 rs12568297 1 224611229 C 1361 0.29 718 0.37 233 0.25 256 0.26 154 0.10
PARP1 rs6668722 1 224612399 C 1353 0.10 719 0.08 230 0.23 252 0.06 152 0.08
PARP1 rs6668851 1 224612489 C 1362 0.31 721 0.24 232 0.28 256 0.38 153 0.53
PARP1 rs8679 1 224615177 G 1351 0.17 716 0.22 228 0.07 255 0.19 152 0.04
PARP1 rs747658 1 224617801 G 1353 0.21 715 0.17 231 0.32 255 0.16 152 0.33
PARP1 rs3219142 1 224618691 A 1364 0.14 720 0.19 233 0.05 257 0.15 154 0.03
PARP1 rs3219123 1 224621971 A 1296 0.06 685 0.07 222 0.05 245 0.06 142 0.01
PARP1 rs3219119 1 224623066 A 1327 0.48 699 0.34 224 0.63 253 0.51 151 0.82
PARP1 rs3219110 1 224624501 C 1360 0.36 719 0.44 232 0.32 256 0.31 153 0.15
PARP1 rs1805401 1 224628735 G 1361 0.03 719 0.04 233 0.02 256 0.04 153 0.00
PARP1 rs907190 1 224633349 A 1363 0.24 720 0.17 233 0.18 257 0.33 153 0.47
PARP1 rs1805410 1 224635288 C 1362 0.13 718 0.18 233 0.11 257 0.11 154 0.06
PARP1 rs2048424 1 224649630 C 1344 0.45 710 0.33 231 0.52 249 0.49 154 0.81
PARP1 rs7542788 1 224651926 C 1363 0.22 720 0.17 233 0.36 257 0.16 153 0.33
PARP1 rs2570367 1 224669133 C 1365 0.26 721 0.18 233 0.28 257 0.34 154 0.45
PARP1 rs2136875 1 224673159 A 1359 0.48 717 0.35 231 0.64 257 0.50 154 0.80
PARP1 rs10915989 1 224677261 A 1361 0.19 720 0.26 231 0.09 257 0.17 153 0.08
Total
a
Non-Hispanic African- Hispanic Asian-Pacific
White American Islander
19

Table 2. Genes/SNPs included in study and minor allele frequencies (contd)
Gene SNP Chromosome Position
Minor
Allele N MAF
b
N MAF
b
N MAF
b
N MAF
b
N MAF
b
POLB rs3136711 8 42314623 C 1360 0.05 718 0.08 232 0.01 256 0.04 154 0.01
POLB rs2979895 8 42319364 G 1362 0.15 720 0.06 231 0.52 257 0.08 154 0.14
POLB rs2976244 8 42326391 T 1362 0.16 718 0.06 233 0.54 257 0.08 154 0.14
POLB rs2953983 8 42332413 G 1363 0.15 721 0.06 233 0.53 255 0.07 154 0.14
POLB rs2272615 8 42338205 G 1361 0.18 719 0.10 233 0.58 256 0.10 153 0.14
POLB rs2073664 8 42350943 A 1365 0.15 721 0.06 233 0.53 257 0.08 154 0.14
POLD1 rs1405655 19 55574431 C 1360 0.37 720 0.33 233 0.50 256 0.47 151 0.15
POLD1 rs4802703 19 55576697 A 1334 0.28 706 0.29 230 0.20 246 0.40 152 0.11
POLD1 rs3219281 19 55578899 T 1361 0.13 719 0.10 231 0.15 257 0.14 154 0.14
POLD1 rs2546551 19 55588172 T 1344 0.35 712 0.44 230 0.23 250 0.38 152 0.14
POLD1 rs3219337 19 55589419 G 1346 0.19 713 0.22 229 0.07 253 0.30 149 0.01
POLD1 rs2445837 19 55613084 C 1355 0.19 716 0.09 230 0.58 256 0.15 153 0.16
POLD1 rs2244095 19 55614182 A 1298 0.17 682 0.11 225 0.11 243 0.08 148 0.68
SMUG1 rs971 12 52861725 T 1364 0.32 720 0.32 233 0.20 257 0.43 154 0.38
SMUG1 rs2233921 12 52862067 A 1354 0.39 717 0.48 230 0.12 253 0.37 154 0.38
SMUG1 rs2279400 12 52867581 G 1362 0.48 719 0.55 233 0.40 256 0.44 154 0.38
SMUG1 rs3087404 12 52867881 T 1363 0.47 720 0.55 232 0.35 257 0.44 154 0.38
SMUG1 rs4759345 12 52869471 C 1350 0.43 713 0.39 230 0.50 254 0.51 153 0.39
SMUG1 rs2029166 12 52876366 T 1350 0.32 713 0.29 231 0.41 253 0.29 153 0.40
SMUG1 rs7296239 12 52877971 C 1360 0.39 718 0.34 232 0.59 256 0.32 154 0.49
SMUG1 rs17108960 12 52879329 T 1364 0.17 721 0.24 233 0.09 257 0.13 153 0.03
SMUG1 rs2087058 12 52879672 G 1348 0.41 713 0.42 228 0.27 253 0.44 154 0.46
SMUG1 rs17108995 12 52880220 A 1365 0.07 721 0.07 233 0.02 257 0.08 154 0.17
XRCC1 rs2030404 19 48735288 C 1362 0.34 720 0.43 233 0.36 256 0.29 153 0.11
XRCC1 rs2682560 19 48736388 T 1363 0.15 721 0.18 231 0.16 257 0.15 154 0.00
XRCC1 rs1799778 19 48750981 T 1271 0.32 663 0.35 224 0.27 240 0.24 144 0.28
XRCC1 rs2854501 19 48751841 A 1355 0.17 714 0.23 232 0.15 255 0.12 154 0.11
XRCC1 rs3213344 19 48752493 C 1364 0.11 720 0.06 233 0.07 257 0.12 154 0.35
XRCC1 rs2023614 19 48756705 G 1361 0.09 719 0.08 233 0.02 255 0.22 154 0.07
XRCC1 rs1001581 19 48757228 T 1359 0.37 718 0.39 233 0.41 257 0.27 151 0.33
XRCC1 rs2682552 19 48761581 A 1341 0.15 711 0.18 231 0.17 250 0.15 148 0.00
XRCC1 rs3213282 19 48764720 G 1345 0.40 712 0.45 230 0.40 252 0.36 151 0.23
XRCC1 rs2854510 19 48766052 G 1362 0.17 720 0.20 232 0.20 256 0.17 152 0.01
XRCC1 rs3213266 19 48767476 A 1362 0.12 720 0.08 232 0.10 257 0.23 153 0.08
XRCC1 rs2854506 19 48769229 A 1355 0.16 719 0.22 227 0.14 257 0.11 152 0.13
XRCC1 rs3213255 19 48769347 G 1360 0.34 717 0.42 233 0.38 257 0.28 153 0.14
XRCC1 rs3810378 19 48773441 C 1365 0.30 721 0.35 233 0.21 257 0.24 154 0.28
XRCC1 rs2682587 19 48774269 A 1363 0.15 719 0.18 233 0.15 257 0.15 153 0.00
XRCC1 rs304731 19 48774785 T 1364 0.16 721 0.22 232 0.15 257 0.11 154 0.12
XRCC1 rs184239 19 48775995 G 1361 0.17 718 0.22 232 0.16 257 0.11 154 0.13
XRCC1 rs11407 19 48777667 T 1363 0.15 719 0.18 233 0.15 257 0.15 153 0.00
XRCC1 rs2682588 19 48779566 G 1363 0.17 720 0.20 233 0.20 256 0.17 152 0.01
XRCC1 rs3817 19 48782035 A 1359 0.47 717 0.45 232 0.51 257 0.51 153 0.42
XRCC1 rs767540 19 48782349 T 1361 0.13 720 0.13 233 0.03 255 0.25 153 0.05
a
Excldues 21 individuals who characterized their race/ethnic group as "other";
b
Minor Allele Frequency among controls
Total
a
Non-Hispanic African- Hispanic Asian-Pacific
White American Islander
20

RESULTS
Demographic characteristics of all cases (N = 721) and controls (N = 736) in our
study are summarized in Table 3. Genotyping data was available for 1,368 individuals
(677 cases and 691 controls); demographic and matching characteristics for individuals
with and without genotype data were not statistically significantly different. As we
previously described (Haile, Witte et al. 1997; Stern, Siegmund et al. 2005), we found
that individuals who participated in phase II of our study did not differ from phase I
participants in age, gender, ethnicity, alcohol intake and smoking patterns. However,
Phase II participants had higher dietary folate intake than participants who participated in
phase I (p = <0.001). Fifty-two percent of enrolled subjects were non-Hispanic White.
The mean age of cases was 61.46 years (± 6.75) and the mean age of controls was 61.67
years (±6.88). Cases smoked longer and more intensely than controls and were more
likely to be current smokers (p <0.001). Cases were also found to have a lower mean
dietary folate intake (mcg/day) and a lower mean dietary fiber intake (g/day) than
controls (p = 0.013; p = 0.036). Less than 12% of cases had more than 1 adenoma.
Approximately 81% of adenomas were colon adenomas and approximately 67% were
small adenomas (< 1 cm).

BER Genes and Colorectal Adenoma Risk
We assessed the per allele associations of 182 tagSNPs in 14 genes belonging to
the BER pathway with adenoma risk. Considering all individuals combined, only 1
tagSNP in the NEIL2 gene (rs11785481) showed a statistically significant association
21

Table 3. Demographics and descriptive characteristics of cases and controls
Controls Cases p
n (%) n (%)
n=736 n=721
Mean age at interview,y (±SD) 61.46 6.75 61.67 6.88 0.55
Mean Smoking years,y (±SD) 14.60 16.77 18.52 17.86 <.001
Mean smoking packyears, (±SD) 16.61 31.51 21.04 31.51 0.008
Mean alcohol intake, y (±SD) 8.03 14.76 9.44 18.54 0.108
Mean dietary folate , mcg/day (±SD) 366.63 210.28 340.08 194.28 0.013
Mean Calories (±SD) 2046.25 951.76 2081.00 924.43 0.473
Mean dietary fiber, g/day (±SD) 22.84 13.18 21.43 12.67 0.036
Mean saturated fat, g/day (±SD) 23.37 13.38 24.40 13.42 0.147
Mean BMI (±SD) 27.08 4.79 27.57 4.61 0.051
Median Age at interview,y
!60yrs 331 44.97 313 43.41 0.549
>60yrs 405 55.03 408 56.59
Race/ethnicity
non-Hispanic White 386 52.59 372 52.10 0.344
African American 112 15.26 126 17.65
Hispanic 149 20.30 123 17.23
Asian-Pacific Islander 79 10.76 80 11.20
Other 8 1.09 13 1.82
Sex
Male 479 65.17 465 64.49 0.787
Female 256 34.83 256 35.51
Clinic of diagnosis
Bellflower 472 64.13 464 64.36 0.929
Sunset 264 35.87 257 35.64
Study Phase
Phase I 460 62.50 438 60.75 0.492
Phase II 276 37.50 283 39.25
Number of Adenomas
1 638 88.73
"2 81 11.27
Missing 2
Adenoma site
Rectum (<15 cm) 137 19.13
Colon ("15 cm) 579 80.87
Missing 5
Adenoma size
Small (<1 cm) 476 66.85
Large ("1 cm) 236 33.15
Missing 9
22

Table 3. Demographics and descriptive characteristics of cases and controls (contd)
Controls Cases p
n (%) n (%)
n=736 n=721
Smoking
Never 301 42.94 253 36.77 0.019
Ever 400 57.06 435 63.23
Missing 35 33
Smoking
Never 301 42.94 253 36.77 <.001
Former 326 46.50 312 45.35
Current 74 10.56 123 17.88
Missing 35 33
Smoking years
Never 301 43.43 253 37.21 <.001
>0-26 yrs 198 28.57 168 24.71
>26yrs 194 27.99 259 38.09
Missing 43 41
Smoking Pack-years
Never 301 43.56 253 37.26 0.001
>0-21 196 28.36 171 25.18
>21 194 28.08 255 37.56
Alcohol intake
Never 283 38.45 268 37.33 0.658
Ever 453 61.55 450 62.67
Missing 0 3
Alcohol intake
Never 283 38.45 268 37.33 0.323
!6g/day 227 30.84 204 27.41
>6g/day 226 30.71 246 34.26
Missing 0 3
Alcohol intake
Never 283 38.45 268 37.33 0.864
!15g/day 333 45.24 327 45.54
>15g/day -30g/day 50 6.79 46 6.41
>30g/day 70 9.51 77 10.72
Missing 0 3
Alcohol intake
Never 283 38.45 268 37.33 0.871
!15g/day 333 45.24 327 45.54
>15g/day 120 16.30 123 17.13
Missing 0 3
Dietary folate
Low (! 317 mcg/day) 370 50.27 378 54.23 0.134
High (> 317 mcg/day) 366 49.73 319 45.77
Missing 24
23

Table 3. Demographics and descriptive characteristics of cases and controls (contd)
Controls Cases p
n (%) n (%)
n=736 n=721
Dietary folate
Low (! 267 mcg/day) 248 33.70 283 40.60 0.024
Medium (>267 - 387 mcg/day) 241 32.74 210 30.13
High (" 388 mcg/day) 247 33.56 204 29.27
Missing
Multivitamin use
No 303 41.28 293 42.40 0.668
Yes 431 58.72 398 57.60
Missing 2 30
24

with adenoma risk; however, it was not statistically significant after correction for
multiple testing using P
ACT
(OR = 0.70; 95%CI = 0.55-0.90; p = 0.006; p
ACT
= 0.140).
When restricting analyses to non-Hispanic whites we observed a similar but non-
significant association (OR = 0.64; 95%CI = 0.48-0.87; p
ACT
= 0.089). Among all
individuals combined, we did not observe any other tagSNPs to be statistically
significantly associated with adenoma risk after correction for multiple testing using
P
ACT
.

Figure 1. Single SNP analysis of the entire study population

When considering stratified analyses by racial/ethnic group, we observed
statistically significant associations for one tagSNP among African-Americans and three
25

tagSNPs among Asian-Pacific Islanders. Among African-Americans we observed an
association with APEX1 rs17111750 (OR = 2.19; 95%CI = 1.36-3.55; p
ACT
= 0.013;
p
pathway
= 0.180), with evidence of statistically significant heterogeneity across all
racial/ethnic groups (p
heterogeneity
= 0.004). Among Asian-Pacific Islanders we observed an
association with two tagSNPs in the FEN1 gene, rs108499 (OR = 2.12; 95%CI = 1.30-
3.45; p
ACT
= 0.009; p
pathway
= 0.129; p
heterogeneity
= 0.024) and rs509360 (OR = 0.40;
95%CI = 0.30-0.79; p
ACT
= 0.011; p
pathway
= 0.159; p
heterogeneity
= 0.052), and one tagSNP
in NTHL1, rs2516781 (OR= 0.45; 95%CI = 0.25-0.80; p
ACT
= 0.032; p
pathway
= 0.461;
p
heterogeneity
= 0.062). Results for the top tagSNP with a nominally significant association
among all individuals in the study and the 4 tagSNPs exhibiting significance by
race/ethnicity after correction for multiple testing with P
ACT
are shown in Table 4.

BER Genes and Adenoma Risk Taking Into Account Adenoma Size and Location
Based on numbers, we were able to assess per allele associations taking into
account subtypes of adenomas as defined by polyp size for 154 tagSNPs. We did not find
evidence of any statistically significant per allele associations within either small polyp
(< 1 cm) or large polyp (≥ 1cm) subtype after correction for multiple testing using P
ACT
.
Based on numbers, we were able to assess per allele associations taking into
account subtypes of adenomas as defined by adenoma location (colon versus rectum) for
141 tagSNPs. We observed evidence that two tagSNPs in NEIL2 were statistically
significantly associated with increased risk for rectal adenomas after correction for
multiple testing using P
ACT
: NEIL2 rs7015453 (OR = 1.72; 95%CI = 1.24-2.39; p
ACT
=
0.025) and rs3757949 (OR = 1.58; 95%CI = 1.18-2.13; p
ACT
= 0.044) (Table 5).
26

Gene SNP
Ca/Co
a
OR
b
95%CI p
c
p ACT
d
p heterogeneity
e
NEIL2 rs11785481 666/692 0.70 0.55-0.90 0.006 0.139 0.131
FEN1 rs108499 691/663 1.06 0.90-1.25 0.516 1.000 0.024
APEX1 rs17111750 695/664 1.00 0.85-1.18 0.981 1.000 0.004
FEN1 rs509360 697/665 0.93 0.78-1.09 0.358 0.888 0.052
NTHL1 rs2516781 688/664 0.91 0.77-1.08 0.271 0.840 0.062
Ca/Co OR
b
95%CI p
c
p ACT
d
NEIL2 rs11785481 354/364 0.64 0.48-0.87 0.004 0.089
FEN1 rs108499 353/365 0.95 0.76-1.18 0.635 1.000
APEX1 rs17111750 354/367 0.88 0.71-1.10 0.271 0.781
FEN1 rs509360 353/367 0.95 0.76-1.19 0.662 1.000
NTHL1 rs2516781 353/363 1.01 0.80-1.27 0.960 1.000
Ca/Co OR
b
95%CI p
c
p ACT
d
NEIL2 rs11785481 121/109 0.60 0.24-1.53 0.286 1.000
FEN1 rs108499 122/109 0.67 0.37-1.21 0.185 0.645
APEX1 rs17111750 121/110 2.19 1.36-3.55 0.001 0.013
FEN1 rs509360 122/111 1.27 0.81-2.01 0.302 0.746
NTHL1 rs2516781 122/108 0.83 0.51-1.34 0.445 0.960
Ca/Co OR
b
95%CI p
c
p ACT
d
NEIL2 rs11785481 116/140 1.29 0.72-2.31 0.387 1.000
FEN1 rs108499 113/140 1.09 0.76-1.57 0.641 1.000
APEX1 rs17111750 114/140 0.78 0.52-1.15 0.208 0.573
FEN1 rs509360 116/140 1.09 0.71-1.68 0.694 1.000
NTHL1 rs2516781 115/140 1.06 0.74-1.52 0.747 1.000
Ca/Co OR
b
95%CI p
c
p ACT
d
NEIL2 rs11785481 75/79 0.25 0.05-1.37 0.111 0.806
FEN1 rs108499 75/77 2.12 1.30-3.45 0.002 0.009
APEX1 rs17111750 75/78 1.04 0.58-1.86 0.897 1.000
FEN1 rs509360 74/79 0.49 0.30-0.79 0.003 0.011
NTHL1 rs2516781 74/77 0.45 0.25-0.80 0.006 0.033
Hispanic
a
Excldues 21 individuals who characterized their race/ethnic group as "other";
b
Per allele odds ratios and
95% confidence intervals computed using logistic regression assuming a log-additive model and adjusting
for age at sigmoidoscopy, exam date, sex, clinic, study phase, and race among all study participants;
c
Crude
p-value;
d
Crude p-value corrected for multiple comparisons using P ACT ;
e
Race/ethnicity specific per allele
ORs and 95% confidence intervals computed using logistic regression assuming a log additive model and
adjusting for age at sigmoidoscopy, exam date, sex, clinic, study phase;
e
p-value for heterogeneity by race
Table 4. Adenoma SNP associations among the entire study population and by race/ethnicity
Total
Non-Hispanic White
African-American
Asian-Pacific Islander
27

Table 5. Per allele associations by polyp subsite
Gene SNP CA/CO
a
OR
b
95%CI p
c
p
ACT
d
p
pathway
e
CA/CO
a
OR
b
95%CI p
c
p
ACT
d
p
pathway
e
p
heterogeneity

f
NEIL2 rs3757949 123/669 1.58 1.18-2.13 0.002 0.044 0.610 528/669 1.02 0.85-1.23 0.795 1.000 1.000 0.004
NEIL2 rs7015453 123/670 1.72 1.24-2.39 0.001 0.025 0.346 525/670 1.03 0.84-1.27 0.738 1.000 1.000 0.003
a
Excludes 21 individuals who characterized their race/ethnic group as "other.";
b
Per allele ORs and 95% confidence intervals computed using multinomial
logistic regression assuming a log additive model and adjusting for age at sigmoidoscopy, exam date, sex, clinic, study phase, race among all study participants;
c
Crude p-value;
d
Crude p-value corrected for multiple comparisons using P
ACT
;
e
Bonferroni adjusted p
ACT
value based on the number of gene regions in the
BER pathway (N=14);
f
p-value for heterogeneity by polyp subsite
Rectal Polyp Colon Polyp
28

There was no statistically significant heterogeneity between race/ethnic groups. Both
tagSNPs showed evidence of statistically significant heterogeneity of the ORs by
adenoma location (rs7015453, p
heterogeneity
= 0.003; rs3757949, p
heterogeneity
= 0.004). These
two tagSNPs were not found to be in linkage disequilibrium (LD) among non-Hispanic
whites (r
2
= 0.11). When analysis were restricted to non-Hispanic Whites, both tagSNPs
were statistically significant associated with increasing rectal adenoma risk (rs7015453
p
ACT
= 0.009; rs3757949 p
ACT
= 0.025).

Genetic Variation in BER Genes, Adenoma Risk and Alcohol
We assessed whether polymorphisms in BER genes might modify the association
between alcohol consumption and adenoma risk among all individuals combined and
observed evidence that LIG3 rs1052536 modified the association between alcohol intake
and adenoma risk. Whereas no statistically significant interaction was observed when
considering alcohol intake status (never/ever interaction p
gene
= 0.225), after Bonferroni
correction for multiple testing within gene region (interaction p
gene
), statistically
significant interactions were observed for amount of alcohol intake (0 g/day, ≤ 6 g/day,
> 6 g/day; interaction p
gene
= 0.028, interaction p
pathway =
0.390) and frequency of alcohol
intake (never, 1 drink/day, > 1 drink/day; interaction p
gene
= 0.048, interaction p
pathway
=

0.671) (Table 6). The positive associations between increasing alcohol intake (0 g/day,
≤ 6 g/day, > 6 g/day; p
for trend
= 0.003 and never, ≤ 1 drink/day, >1 drink/day p
for trend
=
0.024) and adenoma risk was observed among individuals with two copies of the major
(C) allele. Specifically, among carriers of two major (C) alleles, those who drank more
than one drink per day had an 84% increased risk of adenoma compared with non-
drinkers (OR = 1.84; 95%CI = 1.09-3.11; p = 0.022), and those who drank more than 6
29

grams per day had a 77% increased risk of adenoma compared with non-drinkers (OR =
1.77; 95%CI = 1.17-2.68; p = 0.006). There was no association between alcohol and
adenoma risk for subjects with 1 minor (T) allele. Among carriers of two minor (T)
alleles, we observed a non-statistically significant inverse association between increasing
alcohol consumption and adenoma risk (Table 6). When restricting analyses to non-
Hispanic Whites, after correction for multiple testing, we observed interactions for LIG3
rs1052536 that were of similar magnitude and similar direction as those observed in the
total study population. However, these interactions were statistically significant (data not
shown).

Genetic Variation in BER Genes, Adenoma Risk and Dietary Folate Intake
When considering a two level dietary folate intake variable (≤ 317 mcg/day
versus > 317 mcg/day), among all individuals combined, we did not find evidence that
BER tagSNPs modified the association with adenoma risk after Bonferroni correction for
multiple testing within each gene region (interaction p
gene
). When considering a three
level dietary folate intake variable (≤ 267 mcg/day, > 267-387 mcg/day, ≥ 388 mcg/day),
we observed that the association with adenoma risk was modified by two LIG3 tagSNPs
(rs1052536 interaction p
gene
= 0.009, interaction p
pathway
=

0.122; rs3744358 interaction
p
gene
= 0.048, interaction p
pathway
=

0.676) and one XRCC1 tagSNP (rs3213344 interaction
p
gene
= 0.046, interaction p
pathway
=

0.646) (Table 6). For each of the two LIG3 tagSNPs,
among individuals with 2 copies of the major alleles there was no statistically significant
trend across increasing levels of dietary folate intake (for LIG3 rs3744358 p
for trend
=
0.577; for LIG3 rs1052536 p
for trend
= 0.175). However, statistically significant inverse
30

association between increasing levels of dietary folate intake and adenoma risk were
observed among individuals with one copy of the minor allele (rs1052536 p
for trend
=
0.021; rs3744358 p
for trend
= 0.010) and two copies of the minor allele (rs1052536 p
for trend

= 0.0006; rs3744358
p for trend
= 0.003). Among individuals with one copy of the minor
allele, those with the highest level of dietary folate intake had a 31% and 37% decreased
adenoma risk compared to those with the lowest level of dietary folate intake for LIG3
rs1052536 (OR = 0.61; 95%CI = 0.51-0.93; p = 0.015) and LIG3 rs3744358 (OR = 0.63;
95%CI = 0.45-0.90; p = 0.010), respectively. Among individuals with two copies of the
minor allele, those with the highest level of dietary folate intake had a 72% and 73%
decreased adenoma risk compared to those with the lowest level of dietary folate intake
for LIG3 rs1052536 (OR = 0.37; 95%CI = 0.20-0.65; p = 0.0007) and LIG3 rs3744358
(OR = 0.38; 95%CI = 0.19-0.75; p = 0.006), respectively. When restricting analyses to
non-Hispanic Whites, similar statistically significant trends of decreasing adenoma risk
with increasing dietary folate intake among individuals with one or two minor alleles
were observed for LIG3 rs3744358 and rs1052536. However, tests of interaction were not
statistically significant after Bonferroni correction for multiple testing within gene region
(rs1052536 interaction p
gene
= 0.082; rs3744358 interaction p
gene
= 0.311) (data not
shown). There is no evidence of linkage disequilibrium between LIG3 tagSNPs
rs3744358 and rs1052536 among non-Hispanic whites.
When considering all individuals combined, we also observed evidence that one
tagSNP in the XRCC1 gene modified the association between folate intake and adenoma
risk (interaction p
gene
= 0.046; interaction p
pathway
= 0.646). Specifically, we observed an
inverse association between dietary folate intake and adenoma risk among carriers of two
31

major (G) alleles (p
for trend
= 0.009) (Table 6). Compared to those at the lowest level of
dietary folate intake, there was a 25% decrease in adenoma risk for subjects with the
medium level of dietary intake (> 267–387 mcg/day) (OR = 0.75; 95%CI = 0.56-1.01)
and a 35% decrease in adenoma risk for subjects with the highest level of dietary folate
intake (≥ 388 mcg/day) (OR = 0.65; 95%CI = 0.48-0.89; p = 0.007). In contrast, among
carriers of two copies of the minor (C) allele, a positive association between folate intake
and adenoma risk was observed for the highest level of dietary folate intake versus the
lowest level (p
for trend
= 0.011). However, we highlight that the number of individuals
with the CC genotype was very small (Table 6).

Genetic Variation in BER Genes, Adenoma Risk and Smoking
We also assessed whether polymorphisms in BER genes might modify the
association between smoking and adenoma risk among all individuals combined. No
statistically significant interactions were observed when considered smoking status
(never versus ever and never/quit/current) or smoking duration, after correction for
multiple testing within each gene region. However, we observed that the association
between smoking intensity (pack-years) and adenoma risk was modified by MUTYH
rs10890324 (interaction p
gene
= 0.009, interaction p
pathway
= 0.131) (Table 6). When
restricting analyses to non-Hispanic whites, we also observed that MUTYH1 rs10890324
modified the association between smoking pack-years and adenoma risk (interaction p
gene
= 0.049, interaction p
pathway
= 0.639) (data not shown). Whereas among individuals with
two copies of the major (A) allele there was no association between increasing smoking
pack-years and adenoma risk (p
for trend
= 0.871), among carriers of one copy of the minor
32

(G) allele having smoked over 21 pack-years was associated with a 76% increased
adenoma risk when compared to never smokers (OR = 1.76; 95%CI = 1.29-2.42; p =
0.0004; p
for trend
= 0.00008). Similar estimates and trend were observed when restricting
analyses to non-Hispanic whites (> 21 pack-years versus never OR = 1.85; 95%CI =
1.17-2.92, p= 0.008; p
for trend
< 0.001). Among, carriers of two minor (G) alleles, smoking
more than 21 pack-years was associated with a 3-fold increased risk of adenomas when
considering all individuals combined (p
for trend
< 0.001) and among non-Hispanic whites
only (p
for trend
= 0.002).

33

Table 6. Smoking, alcohol, folate and adenoma risk by genotype among all subjects
Gene SNP Exposure variable CA/CO CA/CO CA/CO OR
a,b,c
95%CI P OR
a,b,c
95%CI P OR
a,b,c
95%CI p interaction interaction interaction
p
d
p gene
e
p pathway
f
LIG3 rs1052536 Alcohol Intake CC CT TT
Never 97/108 99/106 34/30 1
ref
1
ref
1
ref
!6g/day 67/81 73/76 31/38 1.05 0.70-1.58 0.818 0.90 0.67-1.22 0.499 0.77 0.44-1.35 0.369
>6g/day 89/61 99/99 23/38 1.77 1.17-2.68 0.006 1.01 0.75-1.36 0.962 0.57 0.32-1.01 0.053
p trend 0.003 0.719 0.071 0.005 0.028 0.390
LIG3 rs1052536 Alcoholic Drinks CC CT TT
Never 97/108 99/106 34/30 1
ref
1
ref
1
ref
! 1 drink/day 112/116 121/117 41/56 1.24 0.86-1.80 0.247 0.96 0.73-1.26 0.752 0.74 0.44-1.23 0.243
> 1 drink/day 44/26 51/58 13/20 1.84 1.09-3.11 0.022 0.97 0.67-1.38 0.848 0.51 0.25-1.01 0.052
p trend 0.024 0.807 0.048 0.010 0.048 0.671
LIG3 rs1052536 Dietary Folate Intake CC CT TT
Low (! 267 mcg/day) 106/101 110/92 36/26 1
ref
1
ref
1
ref
Medium (>267 - 387 mcg/day) 65/85 84/89 31/38 0.77 0.52-1.15 0.205 0.74 0.55-0.99 0.040 0.70 0.40-1.23 0.215
High (" 388 mcg/day) 82/64 77/100 21/42 1.29 0.86-1.93 0.212 0.69 0.51-0.93 0.015 0.37 0.20-0.65 0.001
p trend 0.175 0.021 < 0.001 0.001 0.009 0.122
LIG3 rs3744358 Dietary Folate Intake TT TG GG
Low (! 267 mcg/day) 139/128 91/79 22/12 1
ref
1
ref
1
ref
Medium (>267 - 387 mcg/day) 81/117 74/74 26/19 0.66 0.46-0.94 0.022 0.83 0.59-1.16 0.269 1.05 0.54-2.04 0.890
High (" 388 mcg/day) 103/100 65/80 11/26 1.07 0.75-1.53 0.719 0.63 0.45-0.90 0.010 0.38 0.19-0.75 0.006
p trend 0.577 0.010 0.003 0.008 0.048 0.676
XRCC1 rs3213344 Dietary Folate Intake GG GC CC
Low (! 267 mcg/day) 207/166 39/47 6/6 1
ref
1
ref
1
ref
Medium (>267 - 387 mcg/day) 158/180 22/26 1/6 0.75 0.56-1.01 0.056 0.75 0.42-1.35 0.343 0.76 0.23-2.47 0.648
High (" 388 mcg/day) 128/171 46/32 6/3 0.65 0.48-0.89 0.007 1.57 0.92-2.66 0.095 3.78 1.29-11.03 0.015
p trend 0.009 0.071 0.011 0.002 0.046 0.646
MUTYH rs10890324 Smoking Pack-years AA AG GG
Never 116/132 89/108 25/32 1
ref
1
ref
1
ref
>0-21 75/86 59/74 20/24 0.98 0.67-1.45 0.928 0.99 0.72-1.36 0.950 1.00 0.54-1.86 0.993
>21 95/99 89/64 44/18 1.02 0.70-1.49 0.903 1.76 1.29-2.42 < 0.001 3.03 1.67-5.51 < 0.001
p trend 0.871 < 0.001 < 0.001 0.002 0.009 0.131
a
Odds ratios (ORs) are from models with genotype coded as log additive (treated as continuous) and adjusted for age at sigmoidoscopy, sex, clinic, exam date, study phase, and race among all study participants. Excldues 21
individuals who characterized their race/ethnic group as "other.";
b
ORs for smoking additionally adjusted for alcohol intake (g/d) and dietary folate intake (mcg/day); ORs for alcohol additionally adjusted for smoking pack-years
and and dietary folate intake(mcg/d); ORs for dietary folate intake, additionally adjusted for alcohol intake (g/d) and smoking pack-years.;
c
ORs derived from a common baseline model that included the SNP, smoking, alcohol,
dietary folate exposure and interaction terms between genotype and smoking, alcohol, dietary folate exposure levels.;
d
Crude interaction p-value;
e
Bonferroni adjusted interaction p-value based on the number of SNPs within the
respective gene region ;
f
Bonferroni adjusted interaction p gene based gene regions in the BER pathway (N=14).
G/G G/C C/C
A/A A/G G/G
C/C C/T T/T
T/T T/G G/G
C/C C/T T/T
C/C C/T T/T

34

DISCUSSION

We investigated potential associations between 182 tagSNPs from 14 BER gene
regions and colorectal adenoma risk, as well as their role in modifying the effects of
smoking, dietary folate intake and alcohol consumption on colorectal adenoma risk.
Overall, we observed statistically significant associations between colorectal adenoma
risk and polymorphisms in the FEN1 and NTHL1 among Asian-Pacific Islanders, and
APEX1 among African-Americans. Statistically significant associations were also
observed for two unlinked tagSNPs in the NEIL2 gene and rectal adenoma risk. These six
tagSNPs were not found to modify the effects of smoking, dietary folate, or alcohol on
adenoma risk. Instead, we observed evidence that SNPs in other BER genes modified the
effect of smoking (MUTYH), alcohol (LIG3) and dietary folate (LIG3, XRCC1) on
colorectal adenoma risk. These findings support our hypothesis that genetic variation in
DNA repair genes can modify the risk of colorectal adenoma; thus, supporting a role for
oxidative damage induced by these exposures on colorectal cancer formation.
Previously, several epidemiological studies have investigated BER gene
polymorphisms and adenoma risk (Hansen, Saebo et al. 2005; Stern, Siegmund et al.
2005; Skjelbred, Saebo et al. 2006; Stern, Siegmund et al. 2006; Berndt, Huang et al.
2007; Gao, Hayes et al. 2011; Gsur, Bernhart et al. 2011). With the exception of Gao et
al. (Gao, Hayes et al. 2011), these previous studies have been limited to a handful of
candidate SNPs within XRCC1, PARP1, OGG1, and APEX genes. Of those studies, even
fewer have investigated BER polymorphisms as modifiers of alcohol, smoking or dietary
folate intake (Stern, Siegmund et al. 2006; Berndt, Huang et al. 2007; Gao, Hayes et al.
2011).
35

Our analysis encompasses SNPs in both protein coding and non-protein coding
DNA regions. Coding SNPs may alter a protein’s amino acid sequence and ultimately its
structure or function. However, none of the SNPs identified to be associated with
colorectal adenoma risk in this study were found in coding regions. Instead, SNPs
associated with colorectal adenoma risk were found in intronic regions and untranslated
regions (UTR), both in the 5’-UTR and 3’-UTR. SNPs found outside of protein coding
regions may have no known function, but may possess regulatory functions.
Transcription factors, alone or in a complex with other proteins, have the ability bind to a
gene’s transcription factor binding sites (TFBS) and positively or negatively regulate the
transcription process (Mitchell and Tjian 1989; Latchman 1997; Nikolov and Burley
1997). A SNP occurring within a gene’s TFBS could alter the binding ability of the
corresponding transcription factor, possibly affecting the gene’s expression levels (Guo
and Jamison 2005; Mohrenweiser 2007). Sequences in the 3’-UTR are not translated into
proteins, but their sequences are important for translation, subcellular localization and
mRNA stability (Wilkie, Dickson et al. 2003). SNPs within this 3’-UTR region could
alter regulation of gene expression, post-transcriptionally by modifying protein or
microRNA (miRNA) binding sites.
Specifically within the 3’-UTR, miRNAs are of interest in 3’-UTR regulation.
Approximately 22 nucleotides, these noncoding RNAs can regulate coding genes at the
post-transcriptional level in human genes by binding to their complementary 3’-UTR
mRNA binding sites on target genes and inhibiting translation or promoting degradation
of the mRNA complex (Bartel 2004; Lewis, Burge et al. 2005). Thousands of human
genes are predicted to contain miRNA targets, subjecting expression of these genes to
36

miRNA control (Lewis, Burge et al. 2005). Xie et al. (Xie, Lu et al. 2005) discovered 106
highly conserved human 3'-UTRs motifs that may be involved in post-transcriptional
regulation, including serving as possible binding sites for miRNA. The role of miRNAs
in regulating processes that include angiogenesis, apoptosis, development, proliferation
and development suggests an important role in human diseases (Alvarez-Garcia and
Miska 2005). In genes associated with inflammatory processes, prostaglandin synthesis,
thromboxane synthesis, obesity and insulin resistance, 3’-UTR SNPs belonging to
miRNA binding sites have been associated with sporadic colorectal cancer risk (Landi,
Gemignani et al. 2008). Fourteen studies have investigated miRNA expression levels in
colorectal cancer and colorectal adenoma, including the differential expression between
adenocarcinoma and non-neoplastic mucosa (Bartley, Yao et al. 2011). Additionally, a
study by Bartely et al. investigated miRNA expression during the mucosa-adenoma-
adenocarcinoma sequence (Bartley, Yao et al. 2011). Together, these miRNA studies
point to alterations in miRNA expression levels that are frequently occurring in colorectal
adenoma and colorectal cancer. SNPs located within miRNA binding sites of BER genes
have the potential to modify the binding of miRNA to mRNA, subsequent translation and
eventual DNA repair capacity.
To understand the potential mechanisms underlying the associations, we utilized
the University of California Santa Cruz (UCSC) Genome browser (genome.ucsc.edu) to
examine annotation tracks with respect to regulation (Fujita, Rhead et al. 2011;
Rosenbloom, Dreszer et al. 2012). This regulation track contains experimentally derived
data regarding TFBS, miRNA regulatory targets, DNaseI-hypersensitive sites, RNA
binding proteins, histone modifications as well as other regulatory features. We also
37

searched databases of reported TFBS and miRNA binding sites and utilized freely
available in silico web-based tools developed to identify or predict 5’-UTR and 3’-UTR
regulatory sequences, such as: is-rSNP,TRANSFAC, Mapper, TESS, FastSNP,
FuncPred, rVista, SNPinfo, RegRNA, miRbase, MotifMap, do-RiNA, UTRdb, UTRsite
(Matys, Fricke et al. 2003; Loots and Ovcharenko 2004; Marinescu, Kohane et al. 2005;
Huang, Chien et al. 2006; Yuan, Chiou et al. 2006; Schug 2008; Xie, Rigor et al. 2009;
Xu and Taylor 2009; Grillo, Turi et al. 2010; Macintyre, Bailey et al. 2010; Daily, Patel
et al. 2011; Kozomara and Griffiths-Jones 2011; Anders, Mackowiak et al. 2012).
Although these in silico tools may yield many false positive results, they may serve as a
starting point for further investigation of genetic and environmental factors involved in
the expression of BER genes. For SNPs in genes involved in BER, there is a lack of
functional characterization regarding 5’-UTR or 3’-UTR SNPs. For each of the genes for
which we found tagSNPs associated with adenoma risk or involved in interactions with
environmental exposures, we describe below the available knowledge on the potential
functional impact of the SNP and other epidemiological evidence. Experimentally
validated evidence, along with use of prediction tools could help begin to explain some of
the significant associations observed in our study.
The human APEX1 protein is an apurinic/apyrimidinic endonuclease that
possesses transcriptional regulatory activity, and repairs DNA damage caused by
oxidative and alkylating agents (Simonelli, Mazzei et al. 2012). The APEX1 protein
possesses AP DNA endonuclease, 3’-5’ DNA exonuclease and 3’ phosphodiesterase
activity, in addition to the ability to bind and cleave RNA (Simonelli, Mazzei et al. 2012).
As part of BER, APEX1 interacts with XRCC1, FEN1, POLB, OGG1 and MUTYH
38

(Dianova, Bohr et al. 2001; Parker, Gu et al. 2001; Vidal, Boiteux et al. 2001). We did
not find any statistically significant associations with colorectal adenoma risk for any of
the tagSNPs in the APEX1 coding region. However, we did observe a two-fold increased
adenoma risk for APEX1 rs17111750, which is located 5’-upstream of APEX1, only
among African-Americans. The minor allele frequency for APEX1 rs17111750 ranged
from 0.24 among Asian-Pacific Islanders to 0.32 among non-Hipsanic whites in this
study. We did not observe evidence that this, or any other APEX1 tagSNPs modified the
effects of smoking, alcohol intake or dietary folate intake. To our knowledge, only two
other studies have reported on the APEX1 rs17111750 SNP. A nested case-control study
with over 90% non-Hispanic whites reported a positive association with colorectal
adenoma (Barry, Koutros et al. 2011; Gao, Hayes et al. 2011), while a nested case-control
study of non-Hispanic white male pesticide workers reported lack of association with
prostate cancer risk (Barry, Koutros et al. 2011). Other studies have reported that the
APEX1 codon 51 His allele (rs1048945) was associated with an inverse CRC and
adenoma risk (Berndt, Huang et al. 2007; Brevik, Joshi et al. 2010), while the APEX1
codon 148 Glu allele (rs1130409) was associated with an increased CRC and adenoma
risk (Berndt, Huang et al. 2007; Kasahara, Osawa et al. 2008; Pardini, Naccarati et al.
2008; Brevik, Joshi et al. 2010). Among African-Americans, APEX1 rs17111750 is not in
LD (r
2
< 0.05) with either APEX1 rs1048945 nor APEX1 rs1130409.
The functional impact of the APEX1 rs17111750 SNP is unknown. While the
location of this SNP did not match sequences belonging to any motif in the TRANSFAC
database of reported TFBS (Matys, Fricke et al. 2003; Xie, Lu et al. 2005), there was
evidence suggestive of this SNP belonging to a TFBS sequence (Rosenbloom, Dreszer et
39

al. 2012). In addition to evidence of histone modification, this location is also subject to
DNAase hypersensitivity due to unfolded chromatin, suggestive of a regulatory region
(http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). Further studies are needed
to determine what, if any, functional effect rs17111750 may have, or whether this SNP is
in linkage disequilibrium with another functional SNP that may explain the observed
association.
The FEN1 protein is involved in BER and DNA replication. This endonuclease
recognizes specific DNA structures independent of DNA sequence, the single unpaired
3’-flap that overlaps a single stranded 5’-flap of variable length (Chapados, Hosfield et
al. 2004). FEN1 removes the 5'-overhanging flap structure during long patch BER and
when processing of 5’-ends of Okazaki fragments during lagging strand synthesis (Zhou
2007). FEN1 also has 5’-exonuclease activity and gap dependent endonuclease activity
(Zhou 2007). Because of FEN1’s role in various DNA metabolic pathways and DNA
repair, various studies have reported on FEN1’s role in genomic stability. Yeast Rad27,
the FEN1 homolog, when mutated, has been shown to increase genomic instability
(Johnson, Kovvali et al. 1995; Reagan, Pittenger et al. 1995; Tishkoff, Filosi et al. 1997;
Kokoska, Stefanovic et al. 1999; Zheng, Zhou et al. 2005). Fen1 mutations have
predisposed mutant mice to chronic inflammation, autoimmunity and cancers (Zheng,
Dai et al. 2007). Fen1 homozygous knockouts are not viable in mice but heterozygotes
for a Fen1 null mutation, while viable, when combined with an adenomatous polyposis
coli (Apc) gene mutation, experienced increased gastrointestinal tumor progression
(Kucherlapati, Yang et al. 2002). In human lung cancer lines, FEN1 mRNA was shown to
be overexpressed in small cell lung cancer compared to normal controls, possibly in
40

response to higher levels of DNA damage (Sato, Girard et al. 2003). SNPs capable of
modifying the structure or activity of FEN1 may alter DNA repair and replication,
contributing to colorectal adenoma or colorectal cancer susceptibility. In two studies
conducted on Chinese populations, polymorphisms in the FEN1 promoter (rs174538) and
3’-UTR (rs4246215) were associated with reduced FEN1 expression, increased DNA
damage and increased risks for lung and colorectal cancer (Yang, Guo et al. 2009; Liu,
Zhou et al. 2012).
In our study, the minor alleles of the FEN1 SNPs rs509360 and rs108499 were
associated with a two-fold increased and decreased risk of adenoma among Asian-
Americans, respectively. Both rs509360 and rs108499 are located 5’-upstream of FEN1,
occurring in intronic regions of C11orf9 and have no known functions (Yuan, Chiou et al.
2006; Macintyre, Bailey et al. 2010). A study conducted by Gao et al. on a predominantly
non-Hispanic white population did not report any significant associations with FEN1
SNPs and colorectal adenoma (Gao, Hayes et al. 2011). GWAS of breast cancer, prostate
cancer, ischaemic stroke, polyunsaturated fatty acids (PUFA), blood lipids and
Parkinson’s disease have included rs509360 (Fung, Scholz et al. 2006; Hunter, Kraft et
al. 2007; Matarin, Brown et al. 2007; Yeager, Orr et al. 2007; Tanaka, Shen et al. 2009;
Teslovich, Musunuru et al. 2010). Likewise, rs108499 has been included in GWAS of
blood lipids, and diabetes risk (Tanaka, Shen et al. 2009; Dupuis, Langenberg et al. 2010;
Teslovich, Musunuru et al. 2010). Neither of these two SNPs has reached genome wide
significance.
In a GWAS of PUFA plasma level, the most significant associations were observed
in a gene cluster composed of fatty acid desaturase 1 (FADS1), FEN1, C11orf10 and the
41

fatty acid desaturase 2 (FADS2) promoter region, on chromosome 11 (Tanaka, Shen et al.
2009). SNPs in this gene cluster have previously been associated with n-3 and n-6
concentrations in adipose tissue, erythrocyte membranes, plasma and serum (Schaeffer,
Gohlke et al. 2006; Baylin, Ruiz-Narvaez et al. 2007; Malerba, Schaeffer et al. 2008;
Rzehak, Heinrich et al. 2009; Tanaka, Shen et al. 2009). Within that gene cluster, FADS1
rs174537, which maps to C11orf9, achieved genome-wide significance for associations
with lower arachidonic acid (AA) (p = 5.95 x 10
-46
), lower eicosadienoic acid (EDA) (p =
6.78 x 10
-9
) and lower eicosapentanoic acid concentrations (EPA) (p = 1.07 x 10
-14
).
FADS1 rs174537 was also associated with higher alpha linolenic acid (ALA) (p = 2.76 x
10
-5
), higher linoleic acid (LA) concentrations (p = 5.58 x 10
-7
), higher low-density
lipoprotein (LDL-C) (p= 0.0112) and higher total cholesterol levels (p = 0.0268),
although these associations did not reach genome-wide significance. Among Asian-
Americans, rs509360 and rs108499 are in strong LD (r
2
> 0.80) with SNPs in the FADS1
and FADS2 genes, as well as the SNP FADS1 rs174537 (r
2
= 1.00, r
2
= 1.00). Among
non-Hispanic whites, rs108499 and rs509360 are not in strong LD (r
2
< 0.80) with
FADS1 or FADS2 SNPs, specifically rs174537 (r
2
= .70, r
2
= 0.35). The latter may
explain why we only found associations for FEN1 SNPs among Asian-Americans,
suggesting that the SNPs we observed associated with adenoma risk might be capturing
an association with nearby FADS1 causal SNPs.
Arachidonic acid (AA) is a long chain PUFA (20:4,n-6) that is endogenously
synthesized from n-6 precursor fatty acids and is also found in animal fats.
Cycloooxygenase (COX), lipoxygenase (LOX) and P450 epoxygenase pathways can
convert AA into eicosanoids, pro-inflammatory leukotrienes and prostaglandins capable
42

of promoting tumor growth and modulating proliferation, differentiation, migration and
apoptosis (Yue, Melamud et al. 2006). Animal and in-vitro studies give credence to a role
of PUFAs in colon carcinogenesis (Stern, Siegmund et al. 2005). Epidemiological and
experimental evidence suggests lipid metabolism, particularly the AA pathway, has a role
in colorectal cancer development (Jones, Adel-Alvarez et al. 2003; Wang and Dubois
2010). Yet, according to the 2007 Food, Nutrition and the Prevention of Cancer report by
the World Cancer Research Fund and the American Institute for Cancer Research, the
epidemiological data to support a fatty acid composition-colorectal cancer association has
been deemed inconclusive while there is limited evidence consumption of fish, a source
of n3-PUFAs, protects against colorectal cancer (American Institute for Cancer Research.
and World Cancer Research Fund. 2007).
The NTHL1 protein has DNA N-glycosylase activity as well as apurinic and/or
apyrimidinic endonuclease activity responsible for repair of oxidative damage and
spontaneous lesions (Aspinwall, Rothwell et al. 1997; Ikeda, Biswas et al. 1998; Luna,
Bjoras et al. 2000). This wide specificity enables NTHL1 to incise damaged DNA at
cytosines, thymines and guanines (Luna, Bjoras et al. 2000). One of the oxidized
pyrimidine derivatives, thymine glycol is known to block the replication process if not
repaired by NTHL1 or by nucleotide excision repair (Ikeda, Biswas et al. 1998). The
NTHL1 gene and tuberous sclerosis complex 2 (TSC2) tumor suppressor genes are
located adjacent to each other on chromosome 16 in opposite directions and are bi-
directionally regulated by a common promoter in humans, mice and rats (Imai, Sarker et
al. 1998; Ikeda, Mochizuki et al. 2000; Honda, Kobayashi et al. 2003). The NTHL1
rs2516781 SNP was associated with a two-fold decreased risk of adenoma only among
43

Asian-Americans. NTHL1 rs2516781 is 3’-downstream of the NTHL1 gene, an intronic
SNP, within the solute carrier family 9 (sodium/hydrogen exchanger), member 3
regulator 2 (SLC9A3R2) gene. SLC9A3R2 is a regulatory protein involved in regulation
of SLC9A3, the sodium/hydrogen exchanger involved in intestinal sodium absorption
(Yun, Oh et al. 1997). With no known functional significance, rs2516781 is predicted to
function as an intronic enhancer of low risk (Yuan, Chiou et al. 2006). This SNP has not
been previously reported in studies of colorectal adenoma or colorectal cancer. It was not
found to be associated with prostate or papillary thyroid cancer risk (Barry, Koutros et al.
2011; Neta, Brenner et al. 2011). The locations of rs2516781 corresponds to sites of
histone modification, as determined by ChIP-seq; H3K4me3 marks found near
promoters, H3K4me1 marks often found near regulatory elements and H3k27ac marks
often found near active regulatory elements and associated with transcriptional initiation
and unfolded chromatin (http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). Its
location is also subject to DNAase hypersensitivity due to unfolded chromatin, suggestive
of a regulatory region (http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012).
Further studies are needed to determine what if any functional effect rs2516781 may
have, or which nearby causal SNPs it may be tagging.
NEIL2 protein has DNA glycosylase activity and apurinic/apyrimidinic
endonuclease activity (Simonelli, Mazzei et al. 2012). As a glycosylase, NEIL2 starts the
first BER step by removing oxidized DNA bases, and subsequently introduces a break in
the DNA strand. Unlike other glycosylases OGG1 and NTHL1, it can act on a single
strand of sequence in bubble DNA and duplex DNA (Dou, Mitra et al. 2003). NEIL2 is
known to interact with POLβ, LIG3, PNKP, and XRCC1 (Almeida and Sobol 2007). It
44

seems likely NEIL2 may be actively repairing oxidized DNA bases during critical steps
of transcription or translation. Besides residing in the nucleus, NEIL2 has been reported
to participate in BER of oxidative damage of mtDNA in the mitochondria, despite not
containing standard mitochondrial targeting sequences (Mandal, Hegde et al. 2012). A
recent study identified many possible TFBS for NEIL2 and reported that increased
oxidative stress stimulated a promoter regulatory region that eventually resulted in
increased NEIL2 expression and activity (Kinslow, El-Zein et al. 2010). Genetic variation
within the NEIL2 5’-UTR has been shown to modify TFBS and subsequently the
transcription and expression of NEIL2 (Kinslow, El-Zein et al. 2008; Kinslow, El-Zein et
al. 2010). NEIL2 5’-UTR SNP variation may interfere in its ability to repair oxidative
DNA damage. In our study, one NEIL2 SNP was associated with adenoma risk and two
SNPs were associated with risk for a subtype of adenoma, rectal adenoma. None of these
three SNPs were in LD (r
2
< 0.10) with Kinslow’s previously reported regulatory SNPs
(Kinslow, El-Zein et al. 2010). NEIL2 SNPs were not found to modify the effects of
smoking, alcohol intake or dietary folate intake on adenoma risk in our study.
Two NEIL2 SNPs, rs11785481 and rs3757949, associated with adenoma risk and
rectal adenoma risk respectively, are found within the GATA binding protein 4 (GATA4)
gene. GATA4 encodes a zinc finger transcription factor capable of recognizing GATA
motifs through a DNA binding domain (Molkentin 2000; Temsah and Nemer 2005).
Expressed in endoderm and mesoderm derived tissues, GATA4 transcription factor is
capable of regulating tissue specific gene expression in gastrointestinal epithelium
(Molkentin 2000; Temsah and Nemer 2005). Promoter hypermethylation and the
subsequent transcriptional silencing of GATA4 are commonly seen in colorectal cancer
45

cell lines and primary colorectal cancers (Zheng and Blobel 2010). NEIL2 rs11785481,
which has no known function, is located in the 3’-UTR of the GATA4 gene. This
rs11785481 variant was significantly associated with a 30% decreased risk of adenoma
among all subjects in our study and 36% decreased risk among non-Hispanic whites prior
to multiple comparisons adjustment with P
ACT
. NEIL2 rs3757949 is an intronic SNP
located within GATA4 and has no known function. Yet, its position corresponds with
H3K4me1 marks, which are usually found near regulatory elements and it exhibits
characteristics of open chromatin due to DNaseI hypersensitivity and histone
modifications as identified by ChIP-seq (http://genome.ucsc.edu/) (Rosenbloom, Dreszer
et al. 2012). The rs3757949 SNP is predicted to belong to multiple TFBS (Matys, Fricke
et al. 2003; Schug 2008; Xu and Taylor 2009) but current experimental data do not
support multiple TFBS (http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012).
Among non-Hispanic whites, rs3757949 is not found to be in LD with rs11785481
(r
2
< 0.04).
Taking into account adenoma subtype, we observed a 58% increased risk in rectal
adenoma for rs3757949 that remained significant after multiple comparisons adjustment
with P
ACT
, but such a risk was not observed for colon adenomas. The rs11785481 SNP is
predicted to belong to multiple TFBS sequences and predicted to be a miRNA binding
site for multiple miRNAs (Matys, Fricke et al. 2003; Lewis, Burge et al. 2005;
Marinescu, Kohane et al. 2005; Huang, Chien et al. 2006; Schug 2008; Xu and Taylor
2009) but again current experimental data do not support multiple miRNA binding sites
nor multiple TFBS (http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). There is
evidence rs11785481’s position corresponds to areas of open chromatin due to DNaseI
46

hypersensitivity and histone modifications as identified by ChIP-seq
(http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012).
Taking into account subtype for a third NEIL2 SNP, rs7015453 is associated with
rectal adenoma risk. After multiple comparisons adjustment with P
ACT
, a significant 72%
increased risk was observed in rectal adenoma for rs7015453 but not observed for distal
colon adenomas. While it has no known or predicted function, it is 3’-downstream of
GATA4 and 5’-upstream of NEIL2, located within an open reading frame in chromosome
8 (C8orf49). There is no evidence of LD between rs7015453 and rs3757949 (r
2
< 0.15)
nor between rs7015453 and rs11785481 (r
2
< 0.03). The position of rs7015453
corresponds to a region of open chromatin due to DNaseI hypersensitivity and histone
modifications as identified by ChIP-seq (http://genome.ucsc.edu/) (Rosenbloom, Dreszer
et al. 2012). H3k4me1 marks, found near regulatory elements, as well as experimental
evidence for binding of multiple transcription factors by ChIP-seq method correspond to
rs7015453’s location as well. Further studies are needed to determine what if any
functional effect these NEIL2 SNPs may have, or which nearby causal SNPs they may be
tagging.
XRCC1 gene encodes one of the most important proteins in the BER pathway. The
XRCC1 gene encodes a scaffolding protein involved in repairing single strand breaks and
directing BER of damaged bases. In the process of repairing single strand breaks or
directing BER, it interacts with several DNA repair proteins (LIG3, PARP1, PARP2,
POLβ, APEX1, PCNA, PNKP) (Caldecott 2003; Almeida and Sobol 2007). Specifically,
XRCC1 interacts with LIG3 and PARP1 via both of its BRCT domains and with Polβ
through a N-terminal domain. Although XRCC1 Arg194Trp (rs1799782), Arg280His
47

(rs25489) and Arg399Gln (rs25487) are the most commonly investigated XRCC1
polymorphisms, they were not genotyped in this study but rs1799782 and rs25487 have
been reported previously (Stern, Siegmund et al. 2005; Stern, Siegmund et al. 2006).
We failed to find any significant associations between tagSNPs in XRCC1 and colorectal
adenoma risk. In line with results from our previously published report (Stern, Siegmund
et al. 2006), we failed to find evidence that the current XRCC1 tagSNPs modified the
effect of alcohol on adenoma risk. Although we previously reported a significant gene x
smoking interaction for a dichotomous smoking variable (never/ever) among those who
carry the XRCC1 codon 194 Arg/Arg and codon 399 Arg/Arg or Arg/Gln genotypes
(Stern, Siegmund et al. 2006), we failed to find evidence XRCC1 tagSNPs modified the
effect of smoking on adenoma risk.
While we did not find a significant association between XRCC1 rs3213344 and
adenoma risk, we observed evidence that this SNP might modify the association between
dietary folate intake and colorectal adenoma risk. However, the number of carriers of the
minor allele was too low among the different exposure categories to make results
meaningful. While XRCC1 rs1799782 (Arg194Trp) was not genotyped in this study, it is
in complete LD with XRCC1 rs3213344 (r
2
= 1.00). One other study has found XRCC1
rs1799782 modified the effect of folate intake on adenoma risk (p= 0.02) (Berndt, Huang
et al. 2007). A 2010 XRCC1-colon cancer risk meta-analysis suggested an (Gln/Gln)
inverse association for XRCC1 rs25487 (Arg399Gln) but found no association with risk
for XRCC1 rs1799782 (Arg194Trp) (Jiang, Li et al. 2010). A separate meta-analysis on
colorectal cancer risk and XRCC1 found no significant association between XRCC1
(rs1799782, rs25489, rs25487) and risk of colorectal cancer (Wang, Wang et al. 2010).
48

The MUTYH gene encodes a DNA glycosylase involved in repair of oxidative
damage. DNA oxidation can induce a stable mutagenic lesion, 8-oxo-7,8-dihydroguanine
(8-oxoG), which can cause G:C to A:T transversions unless repaired before DNA
replication (Lipton and Tomlinson 2004). MUTYH, OGG1, and MTH1 work together to
recognize 8-oxoG, remove the damage, and repair 8-oxoG that has incorporated into
DNA (Lipton and Tomlinson 2004). Germline mutations in highly conserved residues of
the MUTYH gene predispose individuals to MUTYH-associated polyposis coli (MAP), an
autosomal recessive inherited colorectal adenoma and carcinoma disorder (Cheadle and
Sampson 2007). These biallelic germ-line mutations have been associated with hereditary
multiple colon adenomas and CRC as well as sporadic CRC (Al-Tassan, Chmiel et al.
2002; Jones, Emmerson et al. 2002; Cheadle, Dolwani et al. 2003; Halford, Rowan et al.
2003; Lipton, Halford et al. 2003; Sampson, Dolwani et al. 2003; Sieber, Lipton et al.
2003; Fleischmann, Peto et al. 2004; Isidro, Laranjeira et al. 2004; Eliason, Hendrickson
et al. 2005; Farrington, Tenesa et al. 2005; Cleary, Cotterchio et al. 2009). Of all BER
genes, only defects in MUTYH via MAP, have been associated with CRC risk.
In this study, we did not find any significant associations between SNPs in
MUTYH and adenoma risk. A German study previously reported that MUTYH
rs10890324 was non-significantly associated with a modest increased risk of sporadic
colon cancer (Schafmayer, Buch et al. 2007). We found rs10890324, modified the
association between smoking and adenoma risk. To our knowledge, this is the first study
to report an interaction between rs10890324 and smoking. Smoking for over 21 pack-
years was associated with an approximately two-fold increased risk of adenoma among
the entire study population and among non-Hispanic white subjects carrying one minor
49

(G) allele and a 3 fold risk among those carrying 2 copies of the minor (G) allele.
Although the interactions were not significant, similar results were observed for an
increasing number of years smoked.
The rs10890324 SNP, which flanks the 3’-end of the MUTYH gene, is also
located 5’-upstream of the 4-hydroxyphenylpyruvate dioxygenase-like (HPDL) gene.
HPDL is transcribed on the positive strand, whereas MUTYH is transcribed on the minus
strand. Its position belongs to a region associated with open chromatin as identified by
DNaseI hypersensitivity, histone modifications and binding of transcription factors
(http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). We do not know what if
any functional effect rs2516781 may have, or if it is tagging nearby causal SNPs. It is
plausible that this variant or nearby tagging causal SNPs may be modifying the repair of
oxidative damage in the colon due to the increased oxidative stress caused from smoking.
Given the recognized association between MUTYH mutations and MAP, it is plausible
genetic variation within MUTYH may confer susceptibility to colorectal adenoma and
colorectal cancer.
Human LIG3, a ligase involved in maintaining genomic integrity, encodes
proteins in the mitochondria and nucleus. In the nucleus, it is known to interact with
proteins such as XRCC1, PARP1, PARP2, NEIL1, PNK and NEIL2 (Almeida and Sobol
2007). Specifically, the short patch BER pathway repairs only one nucleotide and
requires APEX1, POLβ, and LIG3/XRCC1 heterodimer (Lu, Li et al. 2001). LIG3 is post-
transcriptionally modified, phosphorylated on Ser123 and then de-phosphorylated during
oxidative stress (Dong and Tomkinson 2006). LIG3 is crucial for mitochondrial DNA
integrity (Gao, Katyal et al. 2011; Simsek, Furda et al. 2011). The mitochondrial form of
50

LIG3 has a translation initiation site, an N-terminal mitochondrial targeting sequence,
upstream from nuclear initiation site (Lakshmipathy and Campbell 1999). Its ligase
activity during replication and repair in mitochondria does not require XRCC1 (Gao,
Katyal et al. 2011; Simsek, Furda et al. 2011).
We did not find any associations between LIG3 tagSNPs and adenoma risk.
However, we found evidence that LIG3 rs1052536 modified the association between
alcohol intake and adenoma risk. A significant increased adenoma risk was observed with
increasing alcohol intake among subjects with two copies of the rs1052536 major (C)
allele. With increasing alcohol intake, a significant inverse association was observed
among subjects with not one, but two copies of the minor (T) allele. In addition, two
LIG3 SNPs (rs1052536, rs3744358) modified the association between dietary folate
intake and adenoma risk. With increasing dietary folate intake, carrying one minor allele
of either SNP was associated with a significantly decreased adenoma risk. Carrying two
copies of the minor allele for either SNP resulted in an even higher reduced adenoma
risk.
The rs1052536 SNP, located in the LIG3 3’-UTR, has no known function. Its
location is predicted to be within an mRNA sequence that corresponds to a miRNA
binding site and is 489 bases away from a verified poly(A)signal (PAS) (Huang, Chien et
al. 2006; Xu and Taylor 2009; Grillo, Turi et al. 2010) . There are currently no reported
miRNA binding sites attributed to this location (http://genome.ucsc.edu/) (Rosenbloom,
Dreszer et al. 2012). There is evidence this site corresponds to an area containing open
chromatin as identified by DNaseI hypersensitivity and histone modifications with DNA
likely being transcribed (http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012).
51

The rs3744358 SNP, located in the LIG3 3’-UTR, has no known function either
and is actually a 3’-UTR for ring finger and FYVE-like domain containing E3 ubiquitin
protein ligase (RFFL), a gene on the minus strand of chromosome 17. The rs3744358
SNP is located within an mRNA sequence that corresponds to a miRNA binding site for
RFFL (Huang, Chien et al. 2006; Xu and Taylor 2009; Grillo, Turi et al. 2010). LIG3
mRNA transcripts may be subjected to post-transcriptional gene regulation. There is
evidence that rs3744358’s location falls within an area that can bind RNA binding
proteins as identified by RIP-seq but there are no currently reported miRNA binding sites
(http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). RNA binding proteins are
involved in regulation of gene expression from mRNA. There is evidence this site
corresponds to an area containing open chromatin as identified by DNaseI
hypersensitivity and histone modifications with DNA likely being transcribed
(http://genome.ucsc.edu/) (Rosenbloom, Dreszer et al. 2012). Gao et al., the only other
study to investigate LIG3 SNPS, failed to find any significant associations between LIG3
SNPs and colorectal adenoma (Gao, Hayes et al. 2011). This is the first study to report on
interactions between LIG3 and alcohol intake or LIG3 and dietary folate.
Our study had several strengths, including the relatively large sample size and the
high response rates for cases and controls, which decreased the potential for our results
being affected by selection bias. Moreover, our sample size allowed us to conduct many
stratified analyses. Lastly, our study used a comprehensive tagSNP approach to
thoroughly investigate the genetic variation in 14 genes involved in the BER pathway.
Among the limitations of our study is the fact that results are only applicable to the
sigmoid colon and rectum. Cases, those individuals with distal or rectal adenomas, had
52

colonoscopy performed but not controls. It is possible controls, lacking polyps in the
rectum and sigmoid colon, might have had more proximal polyps out of reach of
sigmoidscope (Foutch, Mai et al. 1991). In addition, our findings may be limited to
populations that are not supplemented with folic acid, since this study was conducted
prior to fortification of the American food supply with folic acid. While individuals with
colorectal cancer were excluded, individuals with polyps that evolved rapidly into
colorectal cancer would be less likely to be in this study compared to those with slower
evolving polyps. Adenomas are usually not symptomatic and it is not likely that
individuals would modify dietary habits or alcohol/smoking behavior that would bias
responses to dietary questionnaires. Finally, a potential for recall bias exists because
interviews were administered on average 5 months after sigmoidoscopy and cases may
remember their alcohol consumption or smoking habits differently than controls. Even
though we conducted a large number of tests as part of our analyses, which could have
led to false positive results, we believe our approach for multiple comparisons adjustment
in the gene main effect analysis and the gene environment analysis is a conservative
method of reducing the number of false positive results.
In conclusion, our findings suggest that 5’-UTR and 3’-UTR SNP variation in
BER genes can modify the risk of adenoma. It is plausible that genetic and environmental
factors also play a role in regulating the expression in BER genes, which may alter BER
repair capacity, and thus modify adenoma risk. Further research is needed to better
understand possible associations between adenoma risk and expression in BER genes.

53

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Abstract (if available)

Abstract Three inter-related and consistent factors emerging from the epidemiological literature for colorectal adenoma are cigarette smoking, alcohol intake, and dietary folate levels. Oxidative damage caused by these three factors can be repaired through the base excision repair pathway (BER). The ability to repair such damage may be modified by common genetic variants in BER pathway genes. In a sigmoidoscopy based study, we examined associations between 182 halplotype tagging single nucleotide polymorphisms (SNPs), which captured common genetic variation in 14 BER genes, and colorectal adenoma risk. Additionally, the interaction effects between SNPs and cigarette smoking, alcohol intake, and dietary folate levels were examined. Using logistic regression, per allele odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for the association between SNPs and colorectal adenoma risk. Using multinomial logistic regression, per allele ORs and 95% CIs for each SNP were calculated after stratifying on adenoma location (rectal versus left colon) and adenoma size (< 1 cm versus ≥ 1 cm). Significant associations were observed between SNPs in BER genes (FEN1, NTHL1, APEX1) and colorectal adenoma risk. Significant associations were also observed between SNPs in the NEIL2 gene and rectal adenoma risk. SNPs in BER genes modified the effect of smoking (MUTYH), alcohol consumption (LIG3) and dietary folate (LIG3, XRCC1) on colorectal adenoma risk. The findings support our hypothesis that genetic variation in DNA repair genes can modify the association of key colorectal adenoma risk factors. Our findings support a role for oxidative damage induced by these exposures on colorectal cancer formation.

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Creator Corral, Roman (author)

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

School Keck School of Medicine

Degree Master of Science

Degree Program Molecular Epidemiology

Publication Date 08/28/2012

Defense Date 08/08/2012

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

Tag Alcohol,base excision repair,colorectal adenoma,folate,OAI-PMH Harvest,Smoking

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Lewinger, Juan Pablo (committee chair), Stern, Mariana C. (committee chair), Ingles, Sue Ann (committee member)

Creator Email rcorral@med.usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-93708

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