Individual and Cumulative Effects of GWAS Susceptibility
Loci in Lung Cancer: Associations after Sub-Phenotyping
Robert P. Young1,3*, Raewyn J. Hopkins1, Chris F. Whittington1, Bryan A. Hay1, Michael J. Epton2,
Gregory D. Gamble1
1Department of Medicine, Auckland Hospital, Auckland, New Zealand, 2Department of Medicine, University of Otago, Christchurch, New Zealand, 3Synergenz
Biosciences Ltd, Auckland, New Zealand
Epidemiological studies show that approximately 20–30% of chronic smokers develop chronic obstructive pulmonary
disease (COPD) while 10–15% develop lung cancer. COPD pre-exists lung cancer in 50–90% of cases and has a heritability of
40–77%, much greater than for lung cancer with heritability of 15–25%. These data suggest that smokers susceptible to
COPD may also be susceptible to lung cancer. This study examines the association of several overlapping chromosomal loci,
recently implicated by GWA studies in COPD, lung function and lung cancer, in (n=1400) subjects sub-phenotyped for the
presence of COPD and matched for smoking exposure. Using this approach we show; the 15q25 locus confers susceptibility
to lung cancer and COPD, the 4q31 and 4q22 loci both confer a reduced risk to both COPD and lung cancer, the 6p21 locus
confers susceptibility to lung cancer in smokers with pre-existing COPD, the 5p15 and 1q23 loci both confer susceptibility to
lung cancer in those with no pre-existing COPD. We also show the 5q33 locus, previously associated with reduced FEV1,
appears to confer susceptibility to both COPD and lung cancer. The 6p21 locus previously linked to reduced FEV1is
associated with COPD only. Larger studies will be needed to distinguish whether these COPD-related effects may reflect, in
part, associations specific to different lung cancer histology. We demonstrate that when the ‘‘risk genotypes’’ derived from
the univariate analysis are incorporated into an algorithm with clinical variables, independently associated with lung cancer
in multivariate analysis, modest discrimination is possible on receiver operator curve analysis (AUC=0.70). We suggest that
genetic susceptibility to lung cancer includes genes conferring susceptibility to COPD and that sub-phenotyping with
spirometry is critical to identifying genes underlying the development of lung cancer.
Citation: Young RP, Hopkins RJ, Whittington CF, Hay BA, Epton MJ, et al. (2011) Individual and Cumulative Effects of GWAS Susceptibility Loci in Lung Cancer:
Associations after Sub-Phenotyping for COPD. PLoS ONE 6(2): e16476. doi:10.1371/journal.pone.0016476
Editor: Amanda Toland, Ohio State University Medical Center, United States of America
Received July 31, 2010; Accepted December 30, 2010; Published February 3, 2011
Copyright: ? 2011 Young et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project was jointly funded by an HRC grant and Synergenz BioScience Ltd. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: Dr. Robert Young is a Scientific Advisor to Synergenz BioScience Ltd who assisted with funding this project. This does not alter the
authors’ adherence to all the PLos ONE policies on sharing data and materials.
* E-mail: email@example.com
Lung cancer and chronic obstructive pulmonary disease
(COPD) are both lung diseases that result from the combined
effects of smoking exposure and genetic susceptibility [1,2].
Epidemiological studies show that although tobacco smoke
exposure accounts for nearly 90% of cases, only 10–15% of
smokers develop lung cancer while 20%–30% develop COPD [3–
5]. Genetic factors might explain these observations as heritability
of lung cancer and reduced FEV1(forced expiratory volume in one
second that defines COPD) is estimated to be 15–25% and 40–
77% respectively [6,7]. The presence of COPD, a disease
characterized by airflow limitation secondary to lung remodelling
(emphysema and small airways fibrosis), confers a 4-6 fold
increased risk of lung cancer compared to smokers (a) with normal
lung function  or (b) randomly recruited from the community
. Studies also show that the distribution of FEV1is bi-modal in
heavy smokers and uni-modal in light smokers, supporting a
genetic basis to COPD and the lung remodelling (FEV1) response
to chronic smoking exposure [10–12]. Importantly, between 50–
90% of those with lung cancer have pre-existing COPD,
compared to 15% in randomly selected community-based smoking
controls [8,13–15]. This means lung cancer is not just a ‘‘complex
disease’’ from a genetic perspective but that it is also a mixed
phenotype that includes COPD as a sub-phenotype. The question
that then arises is ‘‘Are the genetic effects underlying COPD also
important in susceptibility to lung cancer?’’
Recent genome-wide association (GWA) studies in lung cancer,
COPD and lung function (FEV1) have reported significant
associations at several chromosomal loci [16–23]. Interestingly,
several of these loci (and implicated candidate genes) are common
to both COPD and lung cancer, suggesting the possibility that
shared pathogenetic pathways may underlie susceptibility to these
diseases (Table 1). The above epidemiological and genetic findings
suggest that lung cancer and COPD are not discrete diseases
related only through smoking exposure, but that many of the
smokers who are susceptible to COPD might also be susceptible to
lung cancer [8,12,24–28]. Such a suggestion was made by Dr Tom
Petty 5 years ago  and recently reviewed by Punturieri et al.
. Given the apparent overlap in susceptibility loci, it appears
PLoS ONE | www.plosone.org1 February 2011 | Volume 6 | Issue 2 | e16476
plausible that some of the genetic factors implicated in COPD
might also be relevant in lung cancer [24–29]. This is analogous to
the inter-related pathways underlying obesity and type 2 diabetes,
where the FTO (Fat mass and obesity associated) gene has been
implicated in both diseases . In this context BMI is the
physiological biomarker used to define the sub-phenotype of
obesity just as FEV1defines COPD. The question that then arises
is ‘‘Given the possible overlap in genetic susceptibility between
COPD and lung cancer, is there an alternative study design to
current approaches that might better identify susceptibility genes
in lung cancer?’’
The above observations suggest that an alternate genetic model
to current case-control studies could be used for disease gene
discovery in lung cancer . This model would be different from
that used in the recent GWA case-control studies [17–19], where
genetic effects are explored in lung cancer cases and smoking
controls with unknown, but likely different, COPD prevalence
[26,27,32,33]. With regards to the latter, the possibility that co-
existing COPD in lung cancer cases might introduce an interactive
or confounding effect in lung cancer association studies has been
raised [26,34]. To better understand the complex relationship
between COPD and lung cancer, smokers in both cases and
controls would ideally be matched for smoking exposure and sub-
phenotyped for COPD using spirometry. Lung function testing is
necessary to define this phenotype as COPD is insidious in onset
and, due to a widespread underutilisation of spirometry, under-
diagnosed in 50-80% of cases [9,33]. Sub-phenotyping for COPD
would then define three smoking cohorts, those with normal lung
function (‘‘resistant’’ controls), those with COPD and those with
lung cancer sub-phenotyped for co-existing COPD. Using such an
approach, the authors have shown that the chromosome 15q25
locus, originally associated with lung cancer in GWA studies [17–
19], is also associated with COPD . This observation has been
subsequently replicated in both GWA  and candidate gene
studies . Using this same approach, the authors have also
shown that the chromosome 4q31 locus, associated with a reduced
risk of COPD [21–23], is also independently associated with a
reduced risk of lung cancer .
The lung cancer, lung function and COPD GWA studies have
identified to date at least nine chromosomal regions and eleven
candidate genes (Table 1) that appear to be associated with
COPD, lung function and/or lung cancer (1q23 , 4q22 ,
4q24 [22,23], 4q31 [17,20–23], 5p15 [17,18], 5q33 [22,23], 6p21
[17–19,22,23] and 15q25 [17–21]). The question arises, ‘‘How do
these loci affect susceptibility to lung cancer after sub-phenotyping
for COPD and can they be combined to define a high risk
smoker?’’ With this question in mind, we used the sub-
phenotyping approach described above to examine the individual
Table 1. Chromosomal loci associated with COPD, reduced lung function and Lung Cancer identified by GWA studies and overlap
suggested by case-control study.
Disease Chromosomal lociCandidate genes GWA Study Reference*
COPD (FEV1)1q23 IL6RWilk et al. (16)
4q22 FAM13A Hancock et al. (23)
Cho et al. (63)
4q24 GSTCD Repapi et al. (22)
Hancock et al. (23)
4q31HHIP/GYPAWilk et al. (16)
Pillai et al. (20)
Repapi et al. (22)
Hancock et al. (23)
5q33 HTR4/ADAM19 Repapi et al. (22)
Hancock et al. (23)
6p21BAT3/AGER Repapi et al. (22)
Hancock et al. (23)
6q24GPR126Hancock et al. (23)
15q25CHRNA 3/5 Pillai et al. (20)
Lung Cancer 1q21 CRPAmos et al. (17)
4q31GYPA Amos et al. (17)
5p15 CRR9 (TERT)Amos et al. (17)
Hung et al. (18)
6p21 BAT3 Amos et al. (17)
Hung et al. (18)
You et al. (81)
15q25CHRNA 3/5 Amos et al. (17)
Hung et al. (18)
Thorgeirsson et al (19)
COPD and Lung Cancer overlap Case-control Reference
15q25CHRNA 3/5 Young et al. (26)
4q31 HHIP/GYPAYoung et al. (28)
4q22FAM13A Young et al. (64)
*Available at www.genome.gov/gwastudies. Accessed 25/03/2010.
1Associated with familial lung cancer .
Sub-Phenotyping for COPD in Lung Cancer Loci
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and cumulative effect of recently identified GWA loci implicated
in both COPD (lung function) [20–23] and lung cancer [1,17–19]
studies. Using an algorithm from a previously published model,
that includes age, family history of lung cancer and the prior
diagnosis of COPD [27,32], we combined both susceptible and
protective genotypes from this analysis to derive and validate a risk
score for susceptibility to lung cancer.
Materials and Methods
The subjects in this study have been previously described .
In brief, subjects were of Caucasian ancestry based on their
grandparents’ descent (all four grandparents of Caucasian
descent). Lung cancer and COPD cases were recruited from a
tertiary hospital clinic between 2000 and 2007 in Auckland while
healthy smoking controls were recruited from the same commu-
nity after volunteering for screening spirometry. Inclusion criteria
were Caucasian ancestry (see above), aged 40 years or more and
past smoking history (see below) while those unable to adequately
perform spirometry were excluded (approximate 5% failure rate in
each group). All participants gave written informed consent, and
underwent blood sampling for DNA extraction, pre-bronchodila-
tor spirometry and an investigator-administered questionnaire.
Spirometry was performed using a portable spirometer (Easy-
OneTM; ndd Medizintechnik AG, Zurich, Switzerland). Lung
function conformed to American Thoracic Society (ATS) standards
for reproducibility (http://www.thoracic.org/statements/), with
the highest value of the best three acceptable blows used for
classification of COPD status. COPD was defined according to
Global Initiative forChronic
(GOLD) stage 2 or more criteria (FEV1/FVC,70% and
FEV1% predicted #80%) using pre-bronchodilator spirometric
measurements [www.goldcopd.com]. A modified ATS respira-
tory questionnaire (http://www.thoracic.org/statements/ was
used which collected demographic data including age, sex,
medical history, family history of lung disease, history of active
and passivetobacco exposure,
occupational aero-pollutant exposures.
Lung cancer cohort.
Subjects with lung cancer were
recruited from a tertiary hospital clinic , aged .40 yrs and
the diagnosis confirmed through histological or cytological
specimens in 95% of cases. Non-smokers with lung cancer were
excluded from the study and only primary lung cancer cases with
adenocarcinoma, squamous cell cancer, small cell cancer and
non-small cell cancer (generally large cell or bronchoalveolar
subtypes). Lung function measurement (pre-bronchodilator) was
performed within 3 months of lung cancer diagnosis, prior to
surgery and in the absence of pleural effusions or lung collapse on
plain chest radiographs . For lung cancer cases that had already
undergone surgery, pre-operative lung function performed by the
hospital lung function laboratory was sourced from medical
Subjects with COPD were identified through
hospital specialist clinics as previously described . Subjects
recruited into the study were aged 40–80 yrs, with a minimum
smoking history of 20 pack-yrs and COPD confirmed by a
respiratory specialist based on pre-bronchodilator spirometric
criteria (GOLD stage 2 or more).
Control subjects were recruited based on the
following criteria: aged 40–80 yrs and with a minimum smoking
history of 20 pack-yrs. Control subjects were volunteers who were
recruited from the same patient catchment area (residential area)
as those serving the lung cancer and COPD hospital clinics
through either (a) a community postal advertisement or (b) while
attending community-based retired military/servicemen’s clubs.
Controls with COPD, based on pre-bronchodilator spirometry
(GOLD stage 1 or more), who constituted 35% of the smoking
volunteers, were excluded from further analysis.
The study was approved by the Multi Centre Ethics Committee
The present cross-sectional case–control study compared
smokers of the same ethnicity with comparable demographic
variables (specifically age, sex and smoking history). The controls
in the current study were carefully chosen to best represent the
majority of smokers who have maintained normal or near-normal
lung function despite decades of smoking (‘‘resistant smoker’’) as
shown by many studies [4,5,10–12]. Accordingly, the resistant
smoker group best reflects those smokers least likely to develop
lung cancer or COPD, thus minimising phenotype misclassifica-
tion and improving the power to detect differences between
affected and unaffected smokers . We hypothesised that SNP
associations might identify protective or susceptibility effects to one
or a combination of COPD only (G1), COPD and lung cancer
(G2), lung cancer only (G3) or neither disease (G0) (see Figure 1).
Genomic DNA was extracted from whole blood samples using
standard salt-based methods and purified genomic DNA was
aliquoted (10 ng?mL–1concentration) into 96-well or 384-well
plates. Samples were genotyped using either the SequenomTM
system (SequenomTMAutoflex Mass Spectrometer and Samsung
24 pin nanodispenser) by the Australian Genome Research
Facility (www.agrf.com.au) or by our university lab using
TaqmanH SNP genotyping assays (Applied Biosystems, USA)
utilising minor groove-binder probes. The SequenomTMsequenc-
es were designed in house by AGRF with amplification and
separation methods (iPLEXTM, www.sequenom.com) as previous-
ly described [26,27,32]. TaqmanH SNP genotyping assays were
run in 384-well plates according to the manufacturer’s instruc-
tions. PCR cycling was performed on both GeneAmpH PCR
System 9700 and 7900HT Fast Real-Time PCR System (Applied
Biosystems, USA) devices. SNP primers were designed by Applied
Biosystems. Real-time amplification plots of selected plates were
used to verify end-point allelic discrimination to establish reliability
of the Taqman based genotyping.
The present study investigated the genotype frequencies of 11
SNPs. The rs16969968 SNP, situated within the nicotinic
acetylcholine receptor (nAChR) gene on 15q25, the rs1052486
SNP, situated within the HLA-B associated transcript (BAT3) gene
on 6p21, and the rs402710 SNP, situated within the cisplatin-
resistance regulated gene 9 (CRR9) gene on 5p15, were genotyped
using the SequenomTMsystem, whilst the remaining eight SNPs,
the rs7671167 SNP, situated within the Family with sequence
similarity 13A (FAM13A) gene on 4q22, the rs1489759 SNP,
situated near the hedgehog-interacting protein (HHIP) gene on
4q31, the rs2202507 SNP, situated near the glycophorin A
(GYPA) gene on 4q31, the rs2808630 SNP, situated near the C-
reactive protein (CRP) gene on 1q21, the rs10516526 SNP,
situated within the glutathione S-transferase C-terminal domain
(GSTCD) gene on 4q42, the rs1422795 SNP, situated within the A
Disintegrin and Metalloproteinase 19 (ADAM19) gene on 5q33,
the rs2070600 SNP, situated within the receptor for advanced
rs11155242 SNP, situated within the G-protein receptor 126
gene on 6p21,and the
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(GPR126) gene on 6q24, were genotyped by TaqmanH SNP
genotyping assays. Failed samples were repeated until call rates of
$95% for each SNP in each cohort were achieved. Genotype
frequencies for each SNP were compared between the 3 primary
groups (control smokers, COPD and lung cancer cohorts) and with
sub-phenotyping the lung cancer cohort according to the presence
or absence of COPD based on GOLD 2 criteria.
Algorithm and susceptibility score
The cumulative effect of those SNP genotypes identified as
susceptible (Odds ratio, OR.1) or protective (OR,1), based on
significant distortions in frequency (P,0.05) between the cases or
sub-phenotypes and the control smokers, was examined using a
previously published algorithm [27,32]. Only the lung cancer and
control smoker cohorts were used for this analysis. In this
algorithm, for each subject, a numerical value of 21 was assigned
for each of the protective genotypes present among the protective
SNPs and +1 for each of the susceptible genotypes present. Where
an individual did not have either the protective or susceptibility
genotype for that SNP, the score was 0 (i.e. did not contribute to
the genetic score). This approach is consistent with a recently
published study in prostate cancer . As previously described
[27,32], weighting the presence of specific susceptible or protective
genotypes according to their individual odds ratios (ORs; from
univariate regression) did not significantly improve the discrimi-
natory performance of the cumulative SNP score (unpublished
The algorithmic approach used here involved deriving an
overall ‘‘susceptibility score’’ for each subject (from the control and
lung cancer cohorts) by combining genetic data (cumulative SNP
scores) and clinical variables, identified in a multivariate analysis as
previously described [27,32]. The clinical variables (and score)
were age .60 years of age (+4), family history of lung cancer (+3)
and prior diagnosis of COPD (+4) . By using multivariate
logistic and stepwise regression analysis, the 9-SNP panel was
examined in combination with the pre-stipulated clinical variables
above. As smoking exposure (pack-yrs) was a recruitment criterion
for this study, and comparable between cases and controls, it was
not included in the scoring system described here. The lung cancer
susceptibility score (for the control and lung cancer cohorts) was
plotted with (a) the frequency of lung cancer and (b) the floating
absolute risk (FAR, equivalent to OR) across the combined
smoker/ex-smoker cohort [38,39]. The FAR approach was
adopted since it uses a ‘floated’ variance across all polychotomous
risk categories rather than choosing on referent level and enables
confidence intervals to be presented for all risk categories.
Patient characteristics in the cases and controls were compared
by ANOVA for continuous variables and Chi-squared test for
discrete variables (Mantel–Haenszel, odds ratio (OR)). Genotype
and allele frequencies were checked for each SNP by Hardy–
Weinberg Equilibrium (HWE). Population admixture across
cohorts was performed using structure analysis on genotyping
data from 40 unrelated SNPs . Distortions in the genotype and
allele frequencies were identified by comparing lung cancer (sub-
phenotyped by COPD) and/or COPD cases with ‘‘resistant’’
smoking controls using two-by-two contingency tables. Both the
additive (allelic) and genotype based genetic models were tested
although the latter is preferred . Correction for multiple
comparisons was not done as the SNPs were selected ‘‘a priori’’
from the GWA studies. Individual SNPs were not included in the
Figure 1. Genes conferring resistance (G0) and susceptibility to COPD (G1), lung cancer (G3) or both (G2): a pharmacogenetic
approach to chronic smoke exposure*.
Sub-Phenotyping for COPD in Lung Cancer Loci
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combined risk model on the basis of statistical significance shown
here but were included because they were identified by the GWA
studies to be highly significantly associated with lung cancer. In
this respect, this study was sufficiently powered to enable a small
level of discrimination between cases and controls to be
demonstrated for the resultant overall model rather than
individual SNPs. With at least 450 cases and 450 controls this
study achieves 80% likelihood to detect an area under the ROC
curve of 0.55 using a two-sided z-test at the 5% significance level,
ie we can conclude that the ROC curve for the SNP model offers
better than chance association when the area under the receiver
operating characteristics curve is at least 0.55 (Hintze, J (2006)
PASS 2002, WWW.NCSS.COM)
Genotype data (9-SNP panel) and the clinical variables were
combined in a stepwise logistic regression to assess their relative
effects on discriminating low and high risk (by point estimate and
receiver operating characteristic (ROC) curve) by score quintile.
The frequency distribution of the lung cancer susceptibility score
was compared across the cases and controls. Its clinical utility was
assessed using ROC analysis, which assesses how well the model
predicts risk across the score (i.e. clinical performance of the score
with respect to sensitivity, and specificity).
Characteristics of the lung cancer cases, COPD cases and
healthy control smokers are summarized in Table 2. The
demographic variables and histological subtypes of the lung
cancer cases are comparable to previously published data .
The staging at diagnosis was also comparable to this published
series (data not shown) suggesting the lung cancer cohort is
representative. The COPD cases have higher pack-year exposure
than the lung cancer cases and healthy control smokers (P,0.05).
This reflects outliers with high smoking histories in the COPD
cohort that after log transformation of pack-years showed all
groups were comparable (data not shown). All groups are
comparable with respect to age started smoking, years smoked,
years since quitting and cigarettes/day (Table 2). Overall, we
believe the three groups are well matched for smoking exposure.
We note a lower frequency of current smokers in the lung cancer
and COPD cohorts, compared to healthy smokers (35% vs 40%
vs 48% respectively) which may reflect an effect from their
smoking-related diagnosis. Current smoking status had no effect
on the lung function in the lung cancer cases group. The lung
cancer cases, COPD cases and smoking controls were also
comparable with respect to other aero-pollutant exposures
(Table 2). Those with lung cancer had a higher prevalence for
a positive family history of lung cancer compared to the COPD
cases and healthy smokers (19% vs 11% vs 9%). As expected,
lung function was worse in the lung cancer and COPD cohorts
compared to the healthy smoker controls. Testing lung function
in the lung cancer cases (as described above) enabled stratification
of results to test for an interactive or confounding effect of
The genotyping results for the 12 SNPs are shown in Table 3.
The allele and genotype frequencies were comparable to those
reported in the literature and from the International Hapmap
Project (www.hapmap.org). The observed genotypes for the two
Chr 4q31 SNPs (HHIP and GYPA) in this study were 65%
concordant, in accordance with the reported degree of LD
between these SNPs. The concordance for the other SNPs in
‘‘close’’ proximity (BAT3 and AGER on 6p21) showed very poor
concordance as expected. As all SNPs were in Hardy-Weinberg
equilibrium and amplification plots were used to ensure correct
genotype calls, significant genotyping error is unlikely. We found
no evidence for population stratification between the cohorts using
40 unlinked SNPs from unrelated genes (mean x2=3.3, P=0.58)
. Based on distortions in genotype frequency between the 3
groups, risk genotypes were assigned as generally conferring
protection or susceptibility to COPD and/or lung cancer
according to Figure 1.
Genotype associations according to sub-phenotyping for
COPD (Table 3)
The results below describe individual SNP associations between
resistant smokers and those with COPD or lung cancer (total and
subdivided by co-existing COPD). We found no effects from
gender, height or smoking status (current vs former) on any of
these associations. A relationship between SNP variants and lung
function was only found for rs 16969968 in the lung cancer cases
as previously published (26) but not for the other SNP variants
(unpublished data). The numbers were considered too small to
look at lung cancer sub-grouped by histology. The genotype results
below are summarised in Table 3.
Rs16969968, 15q25 (CHRNA 3/5).
, compared to controls the AA genotype was more frequently
found in lung cancer cases (N=454, 16% vs 9%, OR=1.76,
P=0.005) COPD cases (N=458, 14% vs 9%, OR=1.47,
P=0.06) and for all COPD cases (GOLD 2+) with or without
lung cancer (N=706, 16% vs 9%, OR=1.76, P=0.002). More
importantly, when the lung cancer cases were sub-phenotyped into
those with and without COPD (GOLD 2+ criteria, n=429), the
frequency of the AA genotype was quite different: 19% (vs 9% in
controls, OR=2.26, P=0.002) and 11% (vs 9% in controls,
OR=1.15, P=0.64) respectively (Table 3). Based on the data to
date, the AA genotype of the CHRNA 3/5 SNP most likely
confers susceptibility to both lung cancer and COPD (G2 in
Figure 1 and Table 4).
studies, the CC genotype was found more frequently in control
subjects compared to those with COPD (N=458, 30% vs. 23%,
OR=0.71, P=0.024) (63), lung cancer (N=454, OR=0.64,
P=0.003) (Table 3) lung cancer with COPD cases excluded
(N=207, OR=0.58, P=0.006) and lung cancer with COPD
(N=215, OR=0.66, P=0.03). No association was found with
lung function among the lung cancer cases. The CC genotype of
the FAM13A SNP appears to confer protection against both
COPD and lung cancer (G0 in Figure 1 and Table 4).
Rs1052486, 6p21 (BAT3).
The GG genotype was 23% in the
controls group compared to 26% in the lung cancer group
(N=454, OR=1.19, P=0.25) and 21% in the COPD group
(N=458, OR=0.88, P=0.44) (Table 4). Compared to controls,
the GG genotype was significantly greater in those with lung
cancer and COPD (N=215) (23% vs 31%, OR=1.50, P=0.03)
but no different in the lung cancer only subgroup (N=207) (23%
vs 21%, OR=0.89, P=0.57). The GG genotype was significantly
greater in the lung cancer with COPD group than the lung cancer
only group (31% vs 21%, OR=1.68, P=0.02). The GG genotype
of the BAT3 SNP appears to confer susceptibility for lung cancer
in those with COPD (G2 in Table 4).
Rs402710, 5p15 (CRR9/TERT).
the GG genotype frequency in controls and COPD cases (44% vs
44%, OR=0.97, P=0.83) or lung cancer cases (44% vs 47%,
OR=1.10, P=0.45) (Table 4). Compared to controls, the GG
genotype was significantly higher in lung cancer cases only
As previously reported
Consistent with previous
We found no difference in
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(N=207, 44% vs 53%, OR=1.40, P=0.05) but not in lung
cancer cases with COPD (44% vs 42%, OR=0.90, P=0.54)
(Table 4). The GG genotype is significantly greater in the lung
cancer only patients compared to the lung cancer with COPD
group (53% vs 42%, OR=1.54, P=0.03). The GG genotype of
the CRR9 (TERT) SNP appears to confer susceptibility for lung
cancer only (G3 in Figure 1 and Table 4).
The GG genotype of the HHIP (rs 1489759)
SNP was found to be more prevalent in the control group
compared to COPD (17% vs 11%, OR=0.59, P=0.006) and
lung cancer (17% vs 13%, OR=0.70, P=0.05) groups (Table 4).
Similarly, the corresponding (minor) CC genotype of the GYPA (rs
2202507) SNP was more prevalent in the resistant smokers group
compared to those with COPD (27% vs 19%, OR=0.65,
P=0.06) and lung cancer (27% vs 21%, OR=0.70, P=0.02)
groups (Table 4). When the lung cancer cases were stratified by
available spirometric data (n=419 and n=416 for HHIP and
GYPA genotyping, respectively), into those with and without
COPD (GOLD 2+ criteria), the distribution of the minor allele
homozygote for both SNPs does not change significantly. The
effect sizes of the homozygote minor allele in these sub-analyses
remain the same, although the p values are degraded due to
smaller sample sizes. When grouping all subjects with COPD
(combining COPD and lung cancer with COPD groups, N=670),
the protective effect was nearly identical to that from using the
COPD cohort alone (OR=0.60, P=0.003 and OR 0.66,
P=0.004 for the HHIP and GYPA, respectively). The minor
allele homozygotes for HHIP and GYPA SNPs (GG and CC,
respectively) appear to confer protection from both lung cancer
and COPD (G0 in Figure 1 and Table 4).
Rs1422795, 5q33, (ADAM19).
frequency of the CC genotype was marginally increased lung
cancer cases (9% vs 13%, OR=1.44, P=0.08) and COPD cases
(9% vs 13%, OR 1.47, P=0.07) groups (Table 3). When the lung
cancer cases were divided according to COPD the effect size
remained the same although p-values were degraded due to
smaller numbers (lung cancer with COPD 13%, OR=1.51,
P=0.10 and lung cancer without COPD 13%, OR=1.40,
P=0.20). When the CC genotype frequency of the controls is
compared to those with COPD and lung cancer with COPD (9%
vs 13%, OR=1.45, P=0.05) the larger cohort identifies a
significant increase in the CC genotype in those with the COPD
phenotype. The CC genotype is likely to be associated with modest
susceptibility to both COPD and lung cancer (G2 in Figure 1 and
Rs2070600, 6q21 (AGER).
TC genotype frequency was significantly decreased in COPD
patients (10% vs 15%, OR=0.60, P=0.01) but not in lung cancer
(13% vs 15% in controls, OR=0.87, P=0.87). Sub-grouping lung
cancer cases according to COPD phenotype did not identify any
other associations. The TT/TC genotypes of the AGER SNP
appeared to confer a protective effect for COPD (G1 in Figure 1
and Table 4).
Compared to controls, the
Compared to controls, the TT/
Table 2. Summary of characteristics for the lung cancer and resistant smokers.
Parameter Mean (1 SD)Lung Cancer N=454COPD N=458 Control smokers N=488
% male53% 59% 60%
Age (yrs)69 (10)66 (9)65 (10)
Height (m)*1.67 (0.08)1.68 (0.09)1.69 (0.09)
Current smoking (%)35% 40% 48%
Age started (yr)18 (4)17 (3)17 (3)
Yrs smoked41 (12)42 (11)35 (11)
Cigarettes/day20 (10)23 (9)24 (11)
Yrs since quitting11.4 (6.7)9.8 (7.4) 13.9 (8.1)
History of other exposures
Work dust exposure* 63% 59% 47%
Work fume exposure41% 40% 38%
Asbestos exposure*23% 22%16%
FHx of COPD33%37%28%
FHx of lung cancer*19% 11% 9%
FEV1 (L)* 1.86 (0.48)1.25 (0.48) 2.86 (0.68)
FEV1 % predicted* 73%46% 99%
FEV1/FVC* 64% (13)46% (8) 78% (7)
Spirometric COPD#* 51% 100%0%
#According to GOLD 2+ criteria,
1No significant difference after log transformation of pack- years due to skewed distribution.
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Table 3. Genotype frequencies for the candidate SNP identified by GWA studies of COPD, lung function and lung cancer.
(rs) Genotypes Primary Cohorts
Lung cancer (LC)
Sub-phenotyped for COPD##
LC + COPD
CC vs TT/TCOR (95% CI)
CC vs TT/TC OR (95% CI)
GG/AG vs AA OR (95% CI)
GG vs AA/AGOR (95% CI)
CC vs AA/ACOR (95% CI)
5p15 CRR9 (TERT) (rs
GG vs GA/AAOR (95% CI)
CC vs TT/TCOR (95% CI)
CC vs TT/TCOR (95% CI)
GG vs AA/AGOR (95% CI)
TT/TC vs CCOR (95% CI)
OR (95% CI)
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Rs2808630, 1q23 (CRP).
genotype was slightly less frequent in lung cancer (11% in 8%,
OR=0.68, P=0.09)and COPD
OR=0.69, P=0.10) but significantly lower in the lung cancer
only group (11% in controls vs 5%, OR=0.47, P=0.02). The
frequency of the CC genotype was also significantly lower in the
lung cancer only cohort compared to lung cancer with COPD
despite the modest numbers (5% vs 9%, OR=0.54, P=0.03).
This suggests the CC genotype of the CRP SNP was associated
with susceptibility to lung cancer only (G0 in Figure 1 and
ompared to controls, the CC
Gene-based risk model
Using the results of the uni-variate analysis above, nine ‘‘risk
genotypes’’ were identified as either protective or susceptible
(Table 4). For each subject in the smoking control and lung cancer
cohorts, the sum total of these SNP-based scores were added to the
scores for the clinical variables (age, diagnosis of COPD, family
history of lung cancer) to derive a total lung cancer susceptibility
score [27,32]. On FAR analysis [25,26], the plot of the total score
with the frequency of lung cancer shows a linear relationship
across SNP score quintiles for both the 9 SNP (Figure 2a) and 19
SNP (Figure 2b) panels, as previously shown [27,32]. The
distribution plot of the total scores according to control smokers
(blue line, Figure 3) and lung cancer cases (red line, Figure 3) is
bimodal and the corresponding AUC is 0.69 for the 9 SNP panel
used here (Figure 3a). When genotype data of the 10 most
significant SNPs (smallest P values) from a previous analysis 
are added to the 9 SNP panel, the AUC increases to 0.72
(Figure 3b). We note when the clinical variables only are used the
AUC is 0.67 compared to the 9 SNPs alone of 0.59 and 19 SNPs
alone of 0.67. We conclude that the addition of the 9 SNPs or 19
SNPs improves the AUC and the risk prediction utility of the risk
Table 4. Summary of the frequencies of the ‘‘risk genotype’’ for the 9 SNP panel for lung cancer susceptibility.
GenotypeControls COPDLung Cancer LC + + COPDLC only
44% 44% 47% 42%
*P-value ,0.05 for the risk genotype vs non-risk genotype/s compared to matched smoking controls (Mantel-Haenszel).
1P value ,0.05 for the risk genotype vs non-risk genotype/s comparing LC only to LC+ COPD (Mantel-Haenszel).
qq increased in cases compared to controls, QQ in cases compared to controls.
G0: protective against COPD and lung cancer, G1: associated with COPD only, G2: associated with both lung cancer and COPD, G3: associated with
lung cancer only.
(rs)Genotypes Primary Cohorts
Lung cancer (LC)
Sub-phenotyped for COPD##
15q25 CHRNA 3/5 a
AA vs GG/GAOR (95% CI)
# COPD defined according to pre-bronchodilator GOLD 2+ spirometry criteria.
*P-value of genotype/s - cases vs controls ,0.05.
1 P-value of risk allele - cases vs controls ,0.05. Risk alleles are: CRP-C, FAM13A-C, HHIP-G, AGER-C, CHRNA3/5 a-A.
Table 3. Cont.
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This study provides further evidence that the genes underlying
susceptibility to lung cancer may include genes relevant to
susceptibility to COPD. This has been possible by using cohorts
of smokers, matched for smoking exposure, but quite different in
their phenotypic response to smoking exposure. This phenotypic
response has been defined in part by the presence or absence of
COPD, itself a common sub-phenotype of lung cancer [8,13,14],
defined by a measurable biomarker (FEV1) with a strong genetic
basis [2,7]. By comparing chronic smokers with normal lung
function with those with COPD and lung cancer, sub-phenotyped
for COPD, the genetic associations identified to date can be better
understood. Indeed, by re-examining the associations reported
from recently reported lung cancer and COPD (FEV1) GWA
studies, the results of this current study suggest the genetic effects
from these loci confer specific protective or susceptibility effects on
COPD, Lung cancer or both (Figure 1, Tables 1 and 4). Despite
comparatively small sample sizes here, using this approach the
authors have recently shown that the 15q25 (CHRNA 3/5) and
4q31 (HHIP/GYPA) loci might be relevant in both COPD and
lung cancer [26,28]. The results in this study suggest that the
rs1052486 SNP on the 6p21 locus (BAT3) confers susceptibility to
lung cancer in smokers with pre-existing COPD and that, the
rs402710 SNP on the 5p15 locus (CRR9/TERT) and the
rs2808630 SNP on the 1q23 locus (CRP) confer susceptibility to
lung cancer in those with no pre-existing COPD. The rs1422795
SNP on the 5q33 locus (ADAM 19), previously associated with
reduced FEV1[22,23], might also confer susceptibility to both
COPD and lung cancer. The rs7671167 SNP on the 4q22 locus
(FAM13A), previously linked to reduced lung function and COPD
[23,] is associated with both COPD and lung cancer. Larger
studies will be needed to confirm these findings as the sample sizes
here are small, particularly after sub-phenotyping the lung cancer
cases for COPD. These results also suggest that the previously
published risk algorithm [27,32], where combining risk genotypes
and clinical variables identified in a multivariate analysis, can
segment smokers into moderate, high and very high risk of lung
cancer. The authors conclude that when spirometry is used to sub-
Figure 2. Cumulative effect of the (a) 9 SNP panel and (b) 19
SNP panel of protective and susceptible SNPs in combination
with non-genetic variables to derive a ‘‘lung cancer risk score’’
in lung cancer cases and controls (n=controls and lung cancer
Figure 3. Distribution of the lung cancer susceptibility score
using the (a) 9 SNP panel and (b) 19 SNP panel, of protective
and susceptible SNPs in combination with non-genetic vari-
ables in lung cancer cases and controls.
Sub-Phenotyping for COPD in Lung Cancer Loci
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phenotype smokers, genes with effects on reduced lung function or
COPD appear to be relevant in ‘‘susceptibility’’ to lung cancer.
This provides further evidence to support existing epidemiological
studies suggesting COPD and lung cancer are related by more
than smoking exposure [24,30] but also an overlapping genetic
susceptibility to smoking (Figure 1 and Tables 1 and 4) [26,28].
Epidemiological studies suggest COPD is an important sub-
phenotype of lung cancer. The results of this study suggest genetic
associations broadly define three disease groups: smokers primarily
susceptible to COPD (G1), smokers susceptible to both COPD and
lung cancer (G2), and smokers susceptible to lung cancer only (G3)
(Figure 1 and Table 4). More importantly, the epidemiological
studies also show there is a fourth group of smokers, consisting of
the majority of smokers (<70%) [4,5,12], who maintain normal or
near normal lung function. This group, have a ‘‘resistant’’
phenotype (G0), either do not develop, or are at least risk of,
COPD and lung cancer [4,5,8,9,12]. This is likely to be due, in
part, to an excess of protective genetic variants compared to
susceptibility variants [27,31]. Based on the results of this study,
the G0 genes conferring protection from COPD and lung cancer
include the rs7671167 SNP (FAM13A gene on the Chr 4q22
locus) and the rs1489759 and rs2202507 SNPs (GYPA and HHIP
genes on the Chr 4q31 locus). The rs2070600 SNP (AGER on the
Chr 6p21 locus), previously linked to reduced FEV1, appears to be
a susceptibility gene for COPD but not lung cancer (G1). Both the
rs169968 SNP (CHRNA3/5 gene on the Chr 15q25 locus) and the
rs1052486 SNP (BAT3 gene on the Chr 6p21 locus) appear to
confer susceptibility to lung cancer, but the latter only in
conjunction with COPD (G2). The rs402710 SNP (CRR9 (TERT)
on the Chr 5p15 locus) appears to confer susceptibility to lung
cancer in those with no pre-existing COPD, in keeping with other
studies (G3) [34,43,44]. These observations require validation in
larger studies where SNP effects on histological subtypes might
also be relevant to our findings [1,43]. Several loci linked to lung
function in the general population, such as the rs10516526,
rs11168048 and rs11155242 SNPs (GSTCD on 4q24, HTR4 on
5q33, and GPR126 on 6q24, respectively) [22,23] do not appear
to be related to COPD in this study. However, given that the
population study did not look specifically at smokers, it is possible
that these loci are not relevant to the lung’s response to tobacco
smoke exposure. The authors conclude that the novel study design
used here provides a viable approach with which to better
understand the genetic epidemiology of lung cancer.
The pathologic link between COPD and lung cancer may stem
in part from the overlapping inflammatory, apoptotic and matrix
remodelling/repair processes [45–47] underlying COPD, and the
development of squamous metaplasia, epithelial-mesenchymal
transition (EMT) and DNA damage that underlies lung carcino-
genesis [28,45,48–51]. In particular, there is growing evidence that
suggests these smoking induced changes are orchestrated by the
bronchial epithelium [28,45,48–51] - the HHIP, CHRNA 3/5
and FAM13A proteins are all known to be expressed on the
bronchial epithelium (see below). Although several of the SNPs,
investigated in this study have been shown to have functional
effects on gene expression or protein function, they may not
themselves be the causal variant, but instead representative of the
causal allele through linkage disequilibrium . We note that in
many instances, the physical distance between these risk SNPs and
the proposed candidate genes is large. Despite this, it remains
possible that the investigated SNPs are themselves functional as (a)
studies have shown that SNPs with regulatory effects on genes
maybe some distance away , and (b) it has recently been
recognised that common SNPs with consistent disease association
signals, through ‘‘Synthetic associations’’, may represent the
biological effects of rare variants in nearby genes as much as 2
mega-bases apart . If such an effect were true, then there is
potential for considerable overlap between the susceptibility genes
for COPD and for lung cancer. The rs16969968 SNP (CHRNA
3/5 on 15q25,) investigated in this study results in a non-
synonymous amino-acid change in a highly conserved region of
the second intra-cellular loop of the a5 subunit of the nicotinic
acetylcholine receptor. This receptor is expressed on both
bronchial epithelial cells and inflammatory cells, and is believed
to moderate pulmonary inflammation  and lung destruction
. This receptor also binds both nitrosamines (known
carcinogens in cigarette smoke ) and nicotine linking it to
lung cancer and nicotine addiction respectively . The
rs1052486 SNP (BAT3 on 6p21,) is a missense mutation
(Ser619Pro) in the BAT3 gene and has been previously linked to
lung cancer . BAT3 is a nuclear protein that influences
apoptosis through it’s interaction with p53  linking it to both
COPD and lung cancer. The rs1489759 SNP (HHIP on 4q31,) is
93 kb upstream of the HHIP gene and of unknown function. The
HHIP protein is believed to be important in the bronchial
epithelial response to smoking  and epithelial repair processes
in lung cancer . The HHIP protein has been linked with
epithelial-mesenchymal transition, a pathological process that
results from lung remodelling (with release of metalloproteinases
and growth factors [29,45,61]) and initiates lung carcinogenesis
. The rs2202507 SNP (GYPA on 4q31,) is of unknown
function and downstream of the GYPA gene. The GYPA protein,
found on erythrocytes, shows reduced expression in COPD and is
indicative of oxidative stress . Whether the GYPA association
with COPD and lung cancer reflects an independent effect or
linkage effect with the HHIP locus (LD<0.70) is still debated .
The rs7671167 SNP (FAM13A on 4q22,) is found in intron 4 of
the FAM13A gene and has no known biological function [43,63].
The FAM13A protein, expressed in respiratory cells, is thought to
be involved in signal transduction with possible tumor suppressor
activity [63,64]. The rs1422795 SNP (ADAM 19 on 5q33,) is a
missense mutation (Ser284Gly) in the ADAM 19 gene. ADAM 19
is a transmembrane protein expressed in human lung implicated in
cell-matrix interactions , pulmonary inflammation  and
lung cancer . The rs402710 SNP (CRR9 (TERT) on 5p15,) is
an intronic SNP of unknown function in the CRR9 gene and
associated with lung cancer in many studies [1,17,18,34]. This
SNP is 25 kb upstream from the TERT gene encoding, which
encodes the catalytic subunit of telomerase, a reverse transcriptase
that affects telomere shortening, which has been implicated in
both aging and lung cancer . The results of the current study
suggest that the CRR9/TERT locus confers susceptibility to lung
cancer in the absence of COPD. Such a finding is in accordance
with those recently reported by Yang et al , who found after
adjusting for the presence of COPD, only the rs 402710 SNP
(Chr5p15 locus) was associated with lung cancer while the effects
of the other GWA associated SNPs were lost. The rs2808630 SNP
(CRP on 1q23,) is found in the 39 flanking region of the CRP gene
and has been associated with serum CRP levels (C allele with
reduced CRP) . Elevated CRP levels have been shown in
prospective studies to be associated with greater decline in lung
function  and elevated lung cancer risk after adjustment for
smoking . In the current study, where all cohorts were
matched for smoking exposure, the CC genotype (low CRP level)
was less frequent in both COPD and lung cancer cases although
only achieved significance in the lung cancer only sub-phenotype.
The rs2070600 SNP (AGER on 6p21,) is a missense mutation
(Gly82Ser) of the AGER gene and shown to affect the
inflammatory response in humans . AGER protein expression
Sub-Phenotyping for COPD in Lung Cancer Loci
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has been shown to be increased in the lungs of smokers with
COPD  whilst decreased in human lung cancer cell lines .
We conclude that the SNP associations described here with
COPD and/or lung cancer can be explained by plausible, but as
yet unproven, biological functions. We also conclude that through
sub-phenotyping for COPD, possible clues as to the independent
and overlapping pathogenic processes underlying COPD and lung
cancer can be better examined.
The use of healthy smokers as controls in this study represents a
novel though possibly controversial approach  to identifying
the genetic basis of lung cancer. The authors contend that such an
approach is classically used in pharmacogenetic studies where the
disparate response to a standardised dose of drug provides a
dynamic phenotype (high vs low metabolisers or responders vs
nonresponders) from which to identify relevant genes . In the
setting of lung cancer, smoking is the drug and FEV1 the
biomarker of responsiveness. The latter is based on the
epidemiological studies showing that FEV1is the most important
risk factor for lung cancer among smokers [8,9,12,8,25,76] and
has a bimodal distribution among chronic smokers [10–12]. The
latter is very relevant as bimodal distribution supports a genetic
basis as suggested by twin studies where heritability of FEV1is
estimated to be 40–77% compared to only 15–25% for lung
cancer [6,7]. From a genetic epidemiology perspective, a cohort of
chronic smokers with the resistant or ‘‘non-responder’’ phenotype
(normal or near normal FEV1), might provide an alternate control
group to the non-random (and unscreened) smokers used in case-
controls to date [17–19]. Controls recruited from hospital clinics
or in the absence of spirometric screening (volunteers), report a
COPD prevalence of 30% or more ). If the control group
includes a high proportion of smokers with COPD, the effect of
the COPD related genes on lung cancer susceptibility will be
diluted or lost. This is also relevant as the proportion of COPD
patients who eventually develop lung cancer may be as high as 25–
30% [8,77] and the frequencies of several disease-related SNPs are
very similar between lung cancer and COPD groups (See Table 3,
eg FAM13A, HHIP). This might explain why the lung cancer
GWA studies to date failed to consistently identify the Chr4q31
(HHIP/GYPA) and Chr4q22 (FAM13A) loci as a protective loci
[17–19], and the Chr 5q33 (ADAM19) locus as a possible
susceptibility locus. It would also explain why matching for COPD
in the lung cancer cases and controls might identify only the
Chr5p21 (CRR9/TERT) locus which in the current study was
associated with lung cancer in smokers with no underlying COPD
. The authors propose that FEV1be routinely measured in
genetic epidemiology studies of lung cancer to better understand
the role of ‘‘COPD genes’’ in lung cancer [8,12]. Subtyping for
emphysema using computerised tomography or reduced diffusion
capacity would further refine the subphenotyping for COPD .
It is possible that the specific associations reported in this study
reflect in part, small sample size and chance findings. This
represents an important limitation of the current study requiring
replication in a larger study. It is also possible that the findings
reflect true associations that have been better identified, despite
small sample sizes, by more precise phenotyping of subjects.
Minimising misclassification has been shown to improve the power
of a study to identify true associations . The authors suggest
that some important associations may be either missed [18,19] or
miss-assigned [17–19] in studies where the COPD status of
smoking controls is unknown, especially using hospital based
controls where the prevalence of COPD has been found to be as
high as 30% . The latter would be analogous to searching for
type 2 diabetes genes by comparing obese patients with type 2
diabetics thereby missing the genetic effects contributing to
obesity. If previous case-control studies use control groups where
the prevalence of COPD is 25–30%, then relevant genetic effect
may be obscured. This is well illustrated in Table 3 where, for
several SNPs (eg HHIP, GYPA, CRR9 (TERT), ADAM19 and
CHRNA 3/5), the frequencies of ‘‘risk genotypes’’ between
COPD and lung cancer cases are very similar. In addition,
matching of other confounding variables, in particular smoking
dose exposure, may also help to detect relevant genetic
associations which might otherwise be diluted by using unexposed
people (non-smokers [17–19]). Matching for smoking is particu-
larly important in these studies of smoking related disease as the
penetrance of SNP effects, reflected in the odds ratio, are likely to
be related to the degree and/or duration of smoking exposure.
The effect of certain SNPs have been shown to be greater when
investigated only in those with greater smoking exposure [21,29].
This is the case in a1-anti-trypsin deficiency where people
homozygote for the Z allele (low a1-antitrypsin level) are at risk
of emphysema when they smoke, but much less so when they are
non-smokers . Lastly, there remains the possibility that the
SNP associations reported here result from gender, age or height
differences between the group comparisons. Although our sample
sizes are modest, we think this is unlikely as the groups are
comparable with respect to these variables and we specifically
examined this possibility and did not find any SNP effects
confounded by these variables.
The authors have previously reported a lung cancer suscepti-
bility model whereby genotype data is combined with non-genetic
data [27,32]. This model is based on the results of a multivariate
analysis that include the genotypes, scored according to whether
they conferred a small protective (-1) or susceptibility (+1) effect
[27,32]. The clinical variables, identified as independent predic-
tors of lung cancer following multivariate analysis were, age over
60 years, a family history of lung cancer and previous diagnosis of
COPD. In stepwise regression, family history of lung cancer is
independently associated with lung cancer risk after inclusion of
the SNP genotype data  and likely reflects rare family-specific
genetic effects not accounted for by the genotypes tested here. An
example of such a genetic effect is represented by the RGS17 gene
on Chr 6q24 implicated in familial lung cancer but not
investigated here . Similarly, the prior diagnosis of COPD is
independently associated with lung cancer risk and likely reflects
the contribution of genetic susceptibility to COPD not otherwise
accounted for by the SNPs in the panel. The SNP data provides an
important and significant contribution to the overall score as ‘‘risk
genotypes’’ are a risk variable present from birth, and unlike
family history and diagnosis of COPD, not dependent on age or
natural history of disease. This is very relevant to prevention as
high risk SNP genotypes can be identified early in a person’s
smoking history, before irreversible malignant transformation has
occurred. Although lung function data itself is also an important
variable in defining the risk of lung cancer, it is usually not
available for the majority of smokers where it is often not done
until exertional breathlessness is severe and when over 50% of
lung function is irreversibly lost . For each subject in the
control smoker and lung cancer cohorts, a lung cancer
susceptibility score was derived according to these variables and
their distributions compared [27,32]. The distribution showed a
bimodal separation suggesting utility as a screening test of risk
[27,32,82]. Using the same approach in the current study, with the
susceptibility and protective genotypes derived from the GWA
SNPs (9 SNP panel, Table 4), the lung cancer susceptibility score
was also bimodal and showed a limited utility in an ROC analysis
(AUC=0.69) (Figures 2 and 3). This utility was increased when
the 10 most informative SNPs from the previous study were added
Sub-Phenotyping for COPD in Lung Cancer Loci
PLoS ONE | www.plosone.org 11February 2011 | Volume 6 | Issue 2 | e16476
(N=19 SNP model, AUC=0.72, data not shown). This suggests
that as new genetic variants are identified and added to the risk
model, a greater utility based on ROC analysis might be achieved
[31,80]. This study provides further evidence that lung cancer
results from the combined effects of several genetic variants 
with low penetrance  from genes implicated in both COPD
and lung cancer [26–28]. This study also highlights the limitations
of the lung cancer GWA studies reported to date  and the
need to consider sub-phenotyping using spirometry-defined
COPD to better understand the relative effects of genetic variants
on lung cancer susceptibility [26,28]. In conclusion, this study
provides additional evidence that genes involved in the risk of
COPD may also be relevant to the risk of lung cancer and that
spirometry be routinely used to identify COPD, an important
sub-phenotype of lung cancer. This study also supports the
potential of combining genotype data [27,32] in an algorithmic
fashion to identify smokers at greatest risk of lung cancer.
We gratefully acknowledge the participation of subjects in this study in
particular the patients with lung cancer.
Conceived and designed the experiments: RPY RJH GDG. Performed the
experiments: RPY BAH CFW. Analyzed the data: RPY RJH GDG.
Contributed reagents/materials/analysis tools: RPY. Wrote the paper:
RPY RJH CFW. Helped with recruitment of subjects: MJE.
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