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. 2018 Oct;16(10):1598–1606.e4. doi: 10.1016/j.cgh.2018.03.007

Interactions Between Genetic Variants and Environmental Factors Affect Risk of Esophageal Adenocarcinoma and Barrett’s Esophagus

Jing Dong ∗,‡,a, David M Levine §,a, Matthew F Buas ‖,, Rui Zhang §, Lynn Onstad , Rebecca C Fitzgerald #; Stomach and Oesophageal Cancer Study (SOCS) Consortium, Douglas A Corley ∗∗,‡‡, Nicholas J Shaheen §§, Jesper Lagergren ‖‖,¶¶, Laura J Hardie ##, Brian J Reid , Prasad G Iyer ∗∗∗, Harvey A Risch ‡‡‡, Carlos Caldas §§§,‖‖‖, Isabel Caldas ‖‖‖, Paul D Pharoah ‖‖‖,¶¶¶, Geoffrey Liu ###, Marilie D Gammon ∗∗∗∗, Wong-Ho Chow ‡‡‡‡, Leslie Bernstein §§§§, Nigel C Bird ‖‖‖‖, Weimin Ye ¶¶¶¶, Anna H Wu ####, Lesley A Anderson ∗∗∗∗∗, Stuart MacGregor ‡‡‡‡‡, David C Whiteman §§§§§, Thomas L Vaughan , Aaron P Thrift ∗,‡,
PMCID: PMC6162842  PMID: 29551738

Abstract

Background & Aims

Genome-wide association studies (GWAS) have identified more than 20 susceptibility loci for esophageal adenocarcinoma (EA) and Barrett’s esophagus (BE). However, variants in these loci account for a small fraction of cases of EA and BE. Genetic factors might interact with environmental factors to affect risk of EA and BE. We aimed to identify single nucleotide polymorphisms (SNPs) that may modify the associations of body mass index (BMI), smoking, and gastroesophageal reflux disease (GERD), with risks of EA and BE.

Methods

We collected data on single BMI measurements, smoking status, and symptoms of GERD from 2284 patients with EA, 3104 patients with BE, and 2182 healthy individuals (controls) participating in the Barrett’s and Esophageal Adenocarcinoma Consortium GWAS, the UK Barrett’s Esophagus Gene Study, and the UK Stomach and Oesophageal Cancer Study. We analyzed 993,501 SNPs in DNA samples of all study subjects. We used standard case–control logistic regression to test for gene-environment interactions.

Results

For EA, rs13429103 at chromosome 2p25.1, near the RNF144A-LOC339788 gene, showed a borderline significant interaction with smoking status (P = 2.18×10-7). Ever smoking was associated with an almost 12-fold increase in risk of EA among individuals with rs13429103-AA genotype (odds ratio=11.82; 95% CI, 4.03–34.67). Three SNPs (rs12465911, rs2341926, rs13396805) at chromosome 2q23.3, near the RND3-RBM43 gene, interacted with GERD symptoms (P = 1.70×10-7, P = 1.83×10-7, and P = 3.58×10-7, respectively) to affect risk of EA. For BE, rs491603 at chromosome 1p34.3, near the EIF2C3 gene, and rs11631094 at chromosome 15q14, at the SLC12A6 gene, interacted with BMI (P = 4.44×10-7) and pack-years of smoking history (P = 2.82×10-7), respectively.

Conclusion

The associations of BMI, smoking, and GERD symptoms with risks of EA and BE appear to vary with SNPs at chromosomes 1, 2, and 15. Validation of these suggestive interactions is warranted.

Keywords: Esophageal Neoplasm, Genetic Variants, Risk Factors, Esophagus

Abbreviations used in this paper: BE, Barrett’s esophagus; BMI, body mass index; CI, confidence interval; EA, esophageal adenocarcinoma; EAF, effect allele frequency; GERD, gastroesophageal reflux disease; GWAS, genome-wide association study; MAF, minor allele frequency; OR, odds ratio; SNP, single nucleotide polymorphism


Over the past 4 decades, the incidence of esophageal adenocarcinoma (EA) has increased markedly in many Western populations. Among white men in the United States the incidence has increased almost 10-fold,1 and rates continue to rise by 2% per year.2 EA is a highly fatal cancer with a median overall survival of <1 year following diagnosis.3 EAs typically arise on a background of a premalignant change in the lining of the esophagus known as Barrett’s esophagus (BE). Thus, proposals to prevent EA-associated morbidity and mortality have suggested focusing on identifying patients with BE and enrolling them in endoscopic surveillance programs, or on identifying and modifying risk factors for neoplastic progression.4, 5, 6

Epidemiologic studies have identified frequent or persistent symptoms of gastroesophageal reflux disease (GERD),7, 8 obesity,9 and smoking10, 11 as the principal factors associated with increased risks of EA and BE. These 3 factors together comprise almost 80% of the attributable burden of EA.12, 13 Genetic factors also influence risk of EA and BE. Recent genome-wide association studies (GWAS) and post-GWAS studies have identified more than 20 loci significantly associated with risks of EA and BE14; however, these variants seem to explain only a limited proportion of the heritability of these diseases (estimated to be 25% for EA and 35% for BE).15 It is possible that environmental risk factors for EA and BE may interact with multiple genes through various biological pathways to contribute to disease susceptibility. Given the strength of associations with known risk factors for EA and BE (especially when compared with most other cancers), and potentially shared biological pathways (eg, inflammation) underlying these risk factors,16 identifying gene-environment interactions may be more plausible in the setting of EA and BE. These gene-environment interactions may account for some of the missing heritability of EA and BE.15 However, previous efforts to identify gene-environment interactions for EA and BE have predominantly been candidate based and have involved only small numbers of single nucleotide polymorphisms (SNPs).17, 18, 19

With the aim of identifying SNPs that may modify the associations of body mass index (BMI), smoking, and GERD symptoms with risks of EA and BE, we used pooled questionnaire and genetic data from several studies to conduct a large scale genome-wide gene-environment interaction study of EA and BE.

Methods

Study Population

We obtained data from 1512 EA patients, 2413 BE patients, and 2185 control subjects of European ancestry from 14 epidemiologic studies conducted in Western Europe, Australia, and North America participating in the International Barrett’s and Esophageal Adenocarcinoma Consortium (http://beacon.tlvnet.net/) GWAS. The design of the Barrett’s and Esophageal Adenocarcinoma Consortium GWAS has been described in detail previously.20 Histological confirmation of EA and BE was carried out for all the participating studies. The pooled dataset also included an additional 1,003 EA patients and 882 BE patients from the United Kingdom Stomach and Oesophageal Cancer Study and the UK Barrett’s Esophagus Gene Study, respectively.20 The EA patients in the UK Stomach and Oesophageal Cancer Study had International Classification of Diseases coding of malignant neoplasm of the esophagus (C15) and pathological diagnosis of adenocarcinoma (M8140-8575). The BE patients were identified at endoscopy with confirmed histopathological diagnosis of intestinal metaplasia in the UK Barrett's Esophagus Gene Study. Each contributing study was performed under institutional review board approval and all participants gave informed consent.

SNP Genotyping

Genotyping of buffy coat or whole blood DNA from all participants was conducted using the Illumina Omni1M Quad platform (San Diego, CA), in accordance with standard quality-control procedures.21 For quality control, genotyped SNPs were excluded based on call rate <95%, Hardy-Weinberg Equilibrium P value over controls of <10–4, or minor allele frequency (MAF) ≤2%. After quality assurance and quality control, 993,501 SNPs were used for the current analysis. The analysis was restricted to the subset of ethnically homogenous individuals of European ancestry (confirmed in GWAS samples using principal components analysis).20

Environmental (“Exposure”) Variables

Individual-level exposure data for each study participant were harmonized and merged into a single deidentified dataset. The data were checked for consistency and completeness and any apparent inconsistencies were followed up with individual study investigators. Depending on the study, data from self-reported written questionnaires or in-person interviews were obtained at or near the time of cancer diagnosis for EA patients, at or near the time of BE diagnosis for BE patients, and at the time of recruitment for control subjects. BMI was calculated as weight divided by square of height (kg/m2). For the analysis we selected the weight from each participant that likely reflected usual adult weight (before, for example, any disease-related weight loss). For tobacco smoking, the exposure variables were smoking status (ever vs never) and total cigarette smoking exposure among ever smokers (pack-years of smoking exposure). Ever cigarette smoking was defined as either low threshold exposure (≥100 cigarettes over their whole life) or by asking whether they had ever smoked regularly. Pack-years of smoking exposure was derived by dividing the average number of cigarettes smoked daily by 20 and multiplying by the total number of years smoked. GERD symptoms were defined as the presence of heartburn (ie, a burning or aching pain behind the sternum) or acid reflux (ie, a sour taste from acid, bile, or other stomach contents rising up into the mouth). For analysis, we used the highest reported frequency for either GERD symptom. Participants were then categorized as recurrent vs not recurrent based on a frequency of weekly or greater GERD symptoms for “recurrent.”7 A total of 425 participants with missing values for all 3 covariates (BMI, smoking history, and history of GERD symptoms) were excluded from the analysis.

Statistical Analysis

We used standard case-control logistic regression to test for gene-environment interactions. SNP genotypes were treated as continuous variables and coded as 0, 1, or 2 copies of the minor allele. Exposure variables were either continuous (BMI and pack-years of smoking exposure) or dichotomous (smoking status and GERD symptoms). We modeled the gene-environment interaction by the product of the SNP genotype and the exposure variable, adjusting for age, sex, the first 4 principal components to control for possible population stratification, and the main terms of the SNP and the exposure variable. We used model-robust standard errors as suggested in Voorman et al22 to avoid inflated test statistics that can arise due to underestimation of variability in gene-environment GWAS. For SNPs from each of the top gene-environment interaction hits (ie, main text, P value for interaction <5.0 × 10–7) (Supplemental Material, P value for interaction <1.0 × 10–6) we also performed stratified analyses by genotype to examine the modified association of the known risk factor for EA or BE within the specific genotypes. Analyses were conducted using R software version 3.4.3. (R Foundation for Statistical Computing, Vienna, Austria), the GWASTools package,23 and Stata 13.0 (StataCorp LP, College Station, TX).

Results

The final study sample included 2284 EA patients, 3104 BE patients, and 2182 control subjects. Characteristics of the study sample are shown in Table 1. On average, BMI was higher among EA (mean, 28.4 kg/m2) and BE (mean, 28.7 kg/m2) patients than among control subjects (mean, 27.0 kg/m2). Similarly, EA and BE patients were more likely than control subjects to be ever smokers (74.8%, 64.8%, and 59.1%, respectively) and to report history of recurrent GERD symptoms (46.9%, 52.9%, and 19.4%, respectively).

Table 1.

Characteristics of the Study Population

Characteristic Control Subjects
n = 2182
EA
n = 2284
Control Subjects vs EA
P valuea
BE
n = 3104
Control Subjects vs BE
P Valuea
Age, y 61.7 ± 11.1 65.1 ± 10.3 <.001 62.9 ± 12.1 <.001
Sex <.001 .008
Male 1715 (78.6) 1990 (87.1) 2343 (75.5)
Female 467 (21.4) 294 (12.9) 761 (24.5)
Body mass index, kg/m2 <.001 <.001
 Mean 27.0 ± 4.7 28.4 ± 5.2 28.7 ± 5.1
 <25 786 (36.3) 245 (24.6) 608 (20.7)
 25–29.99 944 (43.5) 455 (45.8) 1191 (42.8)
 ≥30 436 (20.2) 296 (29.6) 935 (36.5)
 Missing 16 1288 370
Smoking status <.001 <.001
 Never 888 (40.9) 568 (25.2) 1081 (35.2)
 Ever 1282 (59.1) 1686 (74.8) 1994 (64.8)
 Missing 12 30 29
Cumulative smoking history, pack-yearsb .43 .001
 Mean 32.8 ± 27.9 33.6 ± 26.4 29.4 ± 24.8
Recurrent GERD symptoms <.001 <.001
 No 1446 (80.6) 965 (53.1) 1058 (47.1)
 Yes 348 (19.4) 854 (46.9) 1186 (52.9)
 Missing 388 465 860

NOTE. Values are mean ± SD or n (%).

BE, Barrett’s esophagus; EA, esophageal adenocarcinoma; GERD, gastroesophageal reflux disease.

a

P value from chi-square tests for categorical variables and Student’s t test for continuous variables. Missing categories were excluded from comparison tests.

b

Among ever smokers.

Gene-Environment Interactions for EA

For EA, at borderline genome-wide significance, 1 SNP interacted with smoking status and 3 interacted with recurrent GERD symptoms (P for interactions ranging from 3.58 × 10–7 to 1.70 × 10–7) (Table 2, Figure 1A and B). At chromosome 2p25.1, rs13429103 (effect allele frequency [EAF] = 15.0%) showed interaction with smoking status (RNF144A-LOC339788, P = 2.18 × 10–7 for interaction). We also observed borderline statistically significant interactions between recurrent GERD symptoms and rs12465911 (P = 1.70 × 10–7 for interaction), rs2341926 (P = 1.83 × 10–7 for interaction), and rs13396805 (P = 3.58 × 10–7 for interaction) at chromosome 2q23.3 (RND3-RBM43). These 3 SNPs are in high linkage disequilibrium (all r2 > 0.9) as indicated in Figure 1B. Additional suggestive gene-environment interactions for EA (where P < 1.0 × 10–6 for interaction) are shown in Supplemental Table 1.

Table 2.

Gene-Environment Interactions With EA or BE With a P Value for Interaction <5.0 × 10–7

Outcome Exposure SNP Chr Position Gene Effect/
Other
EAF OR P
EA
Smoking status rs13429103 2p25.1 7517231 RNF144A-LOC339788 A/G 0.15 2.04 2.18 × 10–7
Recurrent GERD symptoms rs12465911 2q23.3 151785742 RND3-RBM43 A/G 0.26 2.03 1.70 × 10–7
Recurrent GERD symptoms rs2341926 2q23.3 151783928 RND3-RBM43 C/T 0.26 2.02 1.83 × 10–7
Recurrent GERD symptoms rs13396805 2q23.3 151821512 RND3-RBM43 A/G 0.26 1.99 3.58 × 10–7
BE
BMI (continuous) rs491603 1p34.3 36532316 EIF2C3-LOC100128093 A/G 0.16 1.08 4.44 × 10–7
Pack-years of smoking rs11631094 15q14 34624438 SLC12A6 A/C 0.29 0.99 2.82 × 10–7

BE, Barrett’s esophagus; BMI, body mass index; EA, esophageal adenocarcinoma; EAF, effect allele frequency; GERD, gastroesophageal reflux disease; OR, odds ratio; SNP, single nucleotide polymorphism.

Figure 1.

Figure 1

Regional association plots for genotyped single nucleotide polymorphisms (SNPs) showing P values for interaction for (A) smoking status and (B) recurrent gastroesophageal reflux disease symptoms in esophageal adenocarcinoma and (C) body mass index and (D) pack-years of smoking exposure in Barrett’s esophagus. The SNPs in Table 2 are shown as a solid purple diamond, except in panel B where rs2341926 and rs13396805 are shown as circles near rs12465911. The color scheme indicates linkage disequilibrium between the SNP shown with a solid purple diamond and other SNPs in the region using the r2 value calculated from the 1000 Genomes Project. The y axis is the −log10 interaction P value computed from 5388 cases (3104 Barrett’s esophagus, 2284 esophageal adenocarcinoma) and 2182 control subjects. The recombination rate from CEU HapMap data (right-side y axis) is shown in light blue. (A) Chromosome 2p25.1; (B) chromosome 2q23.3 region; (C) chromosome 1p34.3 region; (D) chromosome 15q14 region.

In analyses stratified by genotype (Table 3), compared with never smoking, ever smoking was associated with nearly a 12-fold higher risk of EA among individuals with rs13429103-AA genotype (odds ratio [OR], 11.82; 95% confidence interval [CI], 4.03–34.67). In contrast, among individuals with rs13429103-GG genotype, ever smoking conferred only 1.6-fold higher risk of EA (OR, 1.59; 95% CI, 1.36–1.85). Similarly, the risk for EA associated with recurrent GERD symptoms was higher in individuals with rs12465911-AA genotype (OR, 13.12; 95% CI, 6.21–27.73) than among individuals with rs12465911-GG genotype (OR, 2.80; 95% CI, 2.29–3.41). Additional stratified analyses for risk of EA are shown in Table 3 and Supplemental Table 2.

Table 3.

Risk of EA and BE in Association With Obesity, Smoking History and Recurrent GERD Symptoms, Stratified by Genotype for SNPs in Table 2

Outcome Environmental Exposure SNP Genotype Cases/Control Subjects OR 95% CI P Valuea
EA
Ever smoker vs never smoker (ref) rs13429103 GG 1617/1572 1.59 1.36–1.85 <.001
GA 589/554 2.91 2.23–3.81 <.001
AA 48/44 11.82 4.03–34.67 <.001
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs12465911 GG 1206/1196 2.80 2.29–3.41 <.001
GA 885/823 5.32 4.10–6.90 <.001
AA 163/151 13.12 6.21–27.73 <.001
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs2341926 TT 975/985 2.80 2.30–3.42 <.001
TC 724/681 5.30 4.08–6.88 <.001
CC 120/128 13.12 6.21–27.73 <.001
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs13396805 GG 998/1005 2.85 2.34–3.48 <.001
GA 701/662 5.23 4.02–6.81 <.001
AA 120/127 12.73 6.12–26.49 <.001
BE
BMI ≥30 kg/m2 vs BMI <25 kg/m2 (ref) rs491603 GG 1306/1137 1.52 1.38–1.67 <.001
GA 438/518 2.11 1.80–2.47 <.001
AA 42/64 3.30 1.90–5.73 <.001
≥15 pack-years vs <15 pack-years (ref) rs11631094 CC 729/618 1.02 0.81–1.30 .846
CA 555/540 0.65 0.50–0.84 .001
AA 115/106 0.52 0.28–0.95 .033

BE, Barrett’s esophagus; BMI, body mass index; CI, confidence interval; EA, esophageal adenocarcinoma; GERD, gastroesophageal reflux disease; OR, odds ratio; SNP, single nucleotide polymorphism.

a

P values from logistic regression analysis adjusted for age and sex.

Gene-Environment Interactions for BE

For BE, at chromosome 1p34.3, we observed an interaction between rs491603 (EAF = 16.5%) and BMI (EIF2C3-LOC100128093, P = 4.44 × 10–7 for interaction) (Table 2, Figure 1C). At chromosome 15p14, rs11631094 (EAF = 28.7%) showed interaction with pack-years of smoking exposure (SLC12A6, P = 2.82 × 10–7 for interaction) (Table 2, Figure 1D). Additional suggestive significant interactions (where P < 1.0 × 10–6 for interaction) for BE with pack-years of smoking exposure at chromosomes 12q23.1, 16p12.3, and 17q12 are presented in Supplemental Table 1.

Stratified analyses by genotype showed that the risk for BE associated with obesity (BMI ≥30 kg/m2) was elevated by over 200% among individuals with rs491603-AA genotype (vs BMI <25 kg/m2; OR, 3.30; 95% CI, 1.90–5.73) but only by approximately 50% among individuals with rs491603-GG genotype (vs BMI <25 kg/m2; OR, 1.52; 95% CI, 1.38–1.67). Additional stratified analyses of gene-environment interactions for BE are shown in Table 3 and Supplemental Table 2.

Cross-Examination of Discovered Gene-Environment Interactions

For each SNP in Table 2 and Supplemental Table 1 that had a borderline significant genome-wide interaction in either EA or BE, we examined the equivalent gene-environment interaction in BE and EA, respectively (Supplemental Table 3). For all SNPs discovered in EA, we observed nominal levels of significance (P value for interaction <.05) and ORs in the same direction but somewhat attenuated in BE. For SNPs discovered in BE, only half had P value for interaction <.05 in EA, although all had similar ORs to those in BE. Although obesity and GERD are correlated, none of the SNPs with P value for interaction <1.0 × 10–6 with GERD had comparable ORs or P values when testing for interaction with obesity and similarly for the 1 obesity SNP when tested for GERD.

Discussion

To our knowledge, this is the first genome-wide gene-environment interaction study of EA and its precursor, BE. Although no gene-environment interactions reached genome-wide significance (ie, P < 5.0 × 10–8 for interaction), several borderline significant interactions were indicated between SNPs and known risk factors for EA and BE – BMI, smoking, and GERD symptoms.

A number of studies have pursued candidate-based gene-environment analyses of EA, and reported interactions between BMI, smoking or GERD symptoms and selected SNPs in genes related to detoxification, angiogenesis, DNA repair, apoptosis, and extracellular matrix degradation.24, 25, 26, 27, 28, 29, 30, 31 This body of work helped to establish the notion that the level of disease risk associated with GERD symptoms, in particular, may vary according to inherited genetic variation. All of these studies, however, were conducted in small samples (<350 cases) and were not replicated in independent populations. While direct comparison of our own results and these past findings is complicated by less-than-complete overlap of genotyped SNPs between studies, we did not find evidence in support of interactions among BMI, smoking, or GERD symptoms and any assessed variants in previously-implicated genes: GSTM1, GSTT1, VEGF, MGMT, EGF, IL1B, PERP, PIK3CA, TNFRSF1A, CASP7, TP53BP1, BCL2, HIF1AN, PDGRFA, VEGFR1, or MMP1 (Supplemental Table 4). It remains possible that nominal evidence for some of these associations may not have survived stringent correction for multiple comparisons, and larger samples are needed for true signals to reach significance. Alternatively, previously reported interactions may simply reflect chance findings in small samples because they did not validate in our large study population.

This study has several strengths. First, the pooled dataset including relatively large numbers of cases and control subjects provided us with a rare opportunity to perform, in parallel, genome-wide gene-environment interaction analyses for EA and its precursor lesion, BE. Past candidate-based gene-environment interaction studies of EA have focused on small numbers of genes selected according to biological plausibility, and collectively these reports sampled only a small fraction of the total SNPs presently analyzed (N = 993,501). Such preconceived “gene-centric” SNP selection methods fail to capture the large fraction of noncoding intergenic variations that have been linked to altered risk for these 2 conditions, and also artificially restricts the “genic” search space based on limited mechanistic knowledge, a limitation that is overcome by an unbiased comprehensive genome-wide gene-environment interaction assessment. Second, our study draws on genetic and epidemiologic data from a recent consortium-based GWAS of EA/BE,20 which is the largest of its kind. This sizable study sample afforded greater power to detect gene-environment interactions than in any previous study. Third, all genotyping from this GWAS was conducted on a single platform and in a single laboratory, and subjected to stringent quality-control procedures. Most GWAS analyses test only an additive model because an additive model has reasonable power to detect both additive and dominant effects and the 2 models yield similar results and many GWAS analyses, including ours, are underpowered to detect recessive effects. Nevertheless, for completeness we also tested a dominant model for the 16 SNPs with a P value for interaction <1.0 × 10–6 (Table 2 and Supplemental Table 1), and found slightly attenuated results of the ORs for some gene-environment interactions (data not shown).

Our study also has some limitations. First, our ability to detect true gene-environment interactions might have been limited by the manner in which the environmental (exposure) variables were measured and harmonized. For example, recall bias is a possibility during retrospective reporting of the exposures in the parent case-control studies. However, respondents were unaware of their genotype status at the time of the interviews, mitigating the impact of any possible recall bias in our interaction analyses. Similarly, while considerable care was taken during data harmonization, as described in a series of recent pooled analyses,10, 11 some potential for measurement error of the exposures examined is possible. However, given that case-control status was not considered during this process, any errors from harmonization would be nondifferential, resulting in attenuation of the resulting ORs. Second, central obesity (eg, waist-to-hip ratio) has been found to be more strongly associated with the risk of BE than BMI; however, as waist and hip measurements were not collected in the majority of the included studies, we were unable to examine for interactions with central obesity. Third, despite the comprehensive nature of the genome-wide analysis, we were nonetheless limited to examining common genetic variation (MAF >2%) represented on the Illumina Omni1M Quad GWAS platform employed. Further large-scale studies based on whole-exome or whole-genome sequencing would be required to identify additional gene-environment interactions with rare variants, and more precisely map the reported associations. Finally, our study results should be considered as discovery findings, worthy of independent replication. None of the interactions studied reached genome-wide significance (ie, P < 5.0 × 10–8 for interaction). This may be because there are truly no gene-environment interactions or it may be that power was still limited to detect modest or weak interactions despite our large sample size. In our analyses of 2284 EA patients, 3104 BE patients, and 2182 control subjects, we were adequately powered to detect interactions with an interaction OR in the range of 1.98–2.52 for MAF in the observed range (0.11–0.43), assuming a main effect of 1.08 for log-additive SNPs, a main effect of 1.90 for binary risk factors, and an α of 5.0 × 10–8. Given the large worldwide consortia sample of patients participating in this work, few additional studies of EA and BE patients are currently available and have data for replication; thus, such work may require additional time for study patients to accrue.

In conclusion, our report describes the first genome-wide gene-environment interaction analysis for EA and BE. These findings provide evidence that the magnitude of disease risk associated with BMI, smoking, and GERD symptoms may differ according to germline genetics, and suggest the potential utility of combing epidemiologic exposure data with selected genotyping for comprehensive risk assessment in patients susceptible to EA or BE. Pending validation of the observed interactions in independent study populations, further analyses will be required to investigate the biological basis for differential disease risk associated with the risk factors investigated in the presence of these variants.

Acknowledgments

The following UK hospitals participated in sample collection through the Stomach and Oesophageal Cancer Study (SOCS) collaboration network: Addenbrooke's Hospital, University College London, Bedford Hospital, Hinchingbrooke Hospital, Peterborough City Hospital, West Suffolk Hospital, Norfolk and Norwich University Hospital, Churchill Hospital, John Radcliffe Hospital, Velindre Hospital, St Bartholomew's Hospital, Queen's Hospital Burton, Queen Elisabeth Hospital, Diana Princess of Wales, Scunthorpe General Hospital, Royal Devon & Exeter Hospital, New Cross Hospital, Belfast City Hospital, Good Hope Hospital, Heartlands Hospital, South Tyneside District General Hospital, Cumberland Infirmary, West Cumberland Hospital, Withybush General Hospital, Stoke Mandeville Hospital, Wycombe General Hospital, Wexham Park Hospital, Southend Hospital, Guy's Hospital, Southampton General Hospital, Bronglais General Hospital, Aberdeen Royal Infirmary, Manor Hospital, Clatterbridge Centre for Oncology, Lincoln County Hospital, Pilgrim Hospital, Grantham & District Hospital, St Mary's Hospital London, Croydon University Hospital, Whipps Cross University Hospital, Wansbeck General Hospital, Hillingdon Hospital, Milton Keynes General Hospital, Royal Gwent Hospital, Tameside General Hospital, Castle Hill Hospital, St Richard's Hospital, Ipswich Hospital, St Helens Hospital, Whiston Hospital, Countess of Chester Hospital, St Mary's Hospital IOW, Queen Alexandra Hospital, Glan Clwyd Hospital, Wrexham Maelor Hospital, Darent Valley Hospital, Royal Derby Hospital, Derbyshire Royal Infirmary, Scarborough General Hospital, Kettering General Hospital, Kidderminster General Hospital, Royal Lancaster Infirmary, Furness General Hospital, Westmorland General Hospital, James Cook University Hospital, Friarage Hospital, Stepping Hill Hospital, St George's Hospital London, Doncaster Royal Infirmary, Maidstone Hospital, Tunbridge Hospital, Prince Charles Hospital, Hartlepool Hospital, University Hospital of North Tees, Ysbyty Gwynedd, St. Jame's University Hospital, Leeds General Infirmary, North Hampshire Hospital, Royal Preston Hospital, Chorley and District General, Airedale General Hospital, Huddersfield Royal Infirmary, Calderdale Royal Hospital, Torbay District General Hospital, Leighton Hospital, Royal Albert Edward Infirmary, Royal Surrey County Hospital, Bradford Royal Infirmary, Burnley General Hospital, Royal Blackburn Hospital, Royal Sussex County Hospital, Freeman Hospital, Royal Victoria Infirmary, Victoria Hospital Blackpool, Weston Park Hospital, Royal Hampshire County Hospital, Conquest Hospital, Royal Bournemouth General Hospital, Mount Vernon Hospital, Lister Hospital, William Harvey Hospital, Kent and Canterbury Hospital, Great Western Hospital, Dumfries and Galloway Royal Infirmary, Poole General Hospital, St Hellier Hospital, North Devon District Hospital, Salisbury District Hospital, Weston General Hospital, University Hospital Coventry, Warwick Hospital, George Eliot Hospital, Alexandra Hospital, Nottingham University Hospital, Royal Chesterfield Hospital, Yeovil District Hospital, Darlington Memorial Hospital, University Hospital of North Durham, Bishop Auckland General Hospital, Musgrove Park Hospital, Rochdale Infirmary, North Manchester General, Altnagelvin Area Hospital, Dorset County Hospital, James Paget Hospital, Derriford Hospital, Newham General Hospital, Ealing Hospital, Pinderfields General Hospital, Clayton Hospital, Dewsbury & District Hospital, Pontefract General Infirmary, Worthing Hospital, Macclesfield Hospital, University Hospital of North Staffordshire, Salford Royal Hospital, Royal Shrewsbury Hospital, Manchester Royal Infirmary.

Footnotes

Conflicts of interest The authors disclose no conflicts.

Funding This work was supported by funding from the U.S. National Cancer Institute (NCI) at the National Institutes of Health (grant number R01CA136725) (to T.L.V. and D.C.W.). The U.S. Multi-Center Study was funded by grants U01-CA57949 (to T.L.V.), U01-CA57983 (to M.D.G.), and U01-CA57923 (to H.A.R.). The Seattle Barrett’s Esophagus Study was supported by P01 CA 91955 (to B.J.R.) from the NCI. The Australian Cancer Study was supported by the Queensland Cancer Fund and the National Health and Medical Research Council (NHMRC) of Australia (Program no. 199600) (to D.C.W, Adele C. Green, Nicholas K. Hayward, Peter G. Parsons, David M. Purdie, and Penelope M. Webb). The Swedish Esophageal Cancer Study was supported by grant R01 CA57947-03 (to Olof Nyren and Hans-Olov Adami) from the NCI. The Los Angeles County Multi-ethnic Case-control Study was supported by grants 3RT-0122 (Smoking and Risk of Proximal Vs. Distal Gastric Cancer) (to A.H.W) and 10RT-0251 (Smoking, microsatellite instability & gastric cancers) (to A.H.W) from the California Tobacco Related Research Program and grant CA59636 (to L.B.) from the NCI. The FINBAR (Factors Influencing the Barrett’s Adenocarcinoma Relationship) study was supported by an Ireland-Northern Ireland Co-operation Research Project Grant sponsored by the Northern Ireland Research & Development Office, and the Health Research Board, Ireland (All-Ireland case-control study of Oesophageal Adenocarcinoma and Barrett’s Oesophagus) (to Liam Murray and Harry Comber). Additional funding was provided by the Ulster Cancer Foundation (Belfast, Northern Ireland) and the Northern Ireland Research and Development Office Clinical Fellowship. The Study of Digestive Health was supported by grant 5 RO1 CA 001833-02 (to D.C.W, Adele C. Green, Nicholas K. Hayward, Peter G. Parsons, Sandra J. Pavey, David M. Purdie, Penelope M. Webb, David Gotley, B. Mark Smithers, Glyn G. Jamieson, Paul Drew, David I. Watson, and Andrew Clouston) from the NCI. The Study of Reflux Disease was supported by grant R01 CA72866 (to T.L.V and Diana Farrow) from the NCI. The UK Barrett’s Oesophagus Gene Study was supported by a Medical Research Council Programme grant. The UK Stomach and Oesophageal Cancer Study was funded by Cancer Research UK, as well as by funding from the Cambridge National Institute of Health Research Biomedical Research Centre and the Cambridge Experimental Cancer Medicine Centre. D.A.C. was supported by RO1 DK63616-01, and R21 DK077742. N.J.S. is supported by P30 DK034987 from the National Institutes of Health. T.L.V. is supported by an Established Investigator Award (K05CA124911) from the NCI. DCW is supported by a Research Fellowship from the NHMRC. S.M. is supported by Australian Research Council and NHMRC Fellowships. This work was supported by Roswell Park Cancer Institute and NCI grant P30CA016056 (M.F.B.). J.D. is supported by a Research Training Grant from the Cancer Prevention and Research Institute of Texas (CPRIT) (RP160097). The funders of the individual studies had no role in the design, analysis, or interpretation of the data, or in writing or publication decisions related to this manuscript.

Note: To access the supplementary material accompanying this article, visit the online version of Clinical Gastroenterology and Hepatology at www.cghjournal.org, and at https://doi.org/10.1016/j.cgh.2018.03.007.

Supplementary Material

Supplemental Table 1.

Gene-Environment Interactions With EA or BE With a P Value for Interaction <1.0 × 10–6

Outcome Exposure SNP Chr Position Gene Effect/
Other
EAF OR P Value
EA
Smoking status rs2434584 5q11.2 57566073 ACTBL2-PLK2 C/T 0.08 2.52 7.44 × 10–7
Smoking status rs40210 5q11.2 57619964 ACTBL2-PLK2 A/G 0.08 2.46 8.82 × 10–7
Pack-years of smoking rs17002540 Xq27.1 139946061 CDR1-SPANXB2 T/C 0.19 0.99 5.92 × 10–7
Recurrent GERD symptoms rs2971030 7p21.3 10006341 LOC340268 G/A 0.42 1.77 6.02 × 10–7
Recurrent GERD symptoms rs7141987 14q32.31 101492224 SNORD114-31-LOC100130814 G/A 0.42 1.77 7.11 × 10–7
Recurrent GERD symptoms rs2971028 7p21.3 10007255 LOC340268 A/G 0.40 1.76 8.56 × 10–7
BE
Pack-years of smoking rs9668109 12q23.1 99011272 IKIP A/G 0.09 0.98 6.31 × 10–7
Pack-years of smoking rs1548445 16p12.3 19691583 C16orf62 G/A 0.06 1.02 8.21 × 10–7
Pack-years of smoking rs2671828 17q12 33731764 SLFN11-LOC729839 A/G 0.43 0.99 9.54 × 10–7
Pack-years of smoking rs10507102 12q23.1 98990871 SLC25A3 A/G 0.09 0.98 9.91 × 10–7

BE, Barrett’s esophagus; BMI, body mass index; EA, esophageal adenocarcinoma; EAF, effect allele frequency; GERD, gastroesophageal reflux disease; OR, odds ratio; SNP, single nucleotide polymorphism.

Supplemental Table 2.

Risk of EA and BE in Association With Smoking History and GERD Symptoms, Stratified by Genotype for SNPs in Supplemental Table 1

Outcome Environmental Exposure SNP Genotype Cases/Control Subjects OR 95% CI P Valuea
EA
Ever smoker vs never smoker (ref) rs2434584 TT 1907/1826 1.67 1.45–1.93 <.001
CT 342/328 4.33 3.00–6.24 <.001
CC 5/15 NA - -
Ever smoker vs never smoker (ref) rs40210 GG 1903/1821 1.67 1.45–1.92 <.001
GA 344/332 4.24 2.96–6.06 <.001
AA 6/16 NA - -
≥15 pack-years vs <15 pack-years (ref) rs17002540 CC 1053/1003 1.36 1.12–1.66 .002
CT 48/55 0.78 0.33–1.86 .579
TT 218/206 0.63 0.39–1.00 .052
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs2971030 AA 599/603 2.68 2.08–3.44 <.001
GA 908/895 3.81 3.06–4.75 <.001
GG 309/293 9.44 6.17–14.45 <.001
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs7141987 AA 591/590 2.69 2.08–3.49 <.001
GA 908/887 3.74 3.02–4.64 <.001
GG 319/317 9.32 6.04–14.36 <.001
Recurrent GERD symptoms vs nonrecurrent GERD symptoms (ref) rs2971028 GG 625/635 2.70 2.11–3.45 <.001
GA 900/890 3.87 3.10–4.82 <.001
AA 294/268 9.58 6.17–14.88 <.001
BE
≥15 pack-years vs <15 pack-years (ref) rs9668109 GG 1167/1058 0.92 0.77–1.11 .390
GA 221/201 0.38 0.25–0.60 <.001
AA 11/5 0.33 0.02–5.64 .443
≥15 pack-years vs <15 pack-years (ref) rs1548445 AA 1223/1097 0.76 0.63–0.91 .002
GA 170/164 1.11 0.68–1.80 .675
GG 6/3 NA - -
≥15 pack-years vs <15 pack-years (ref) rs2671828 GG 457/423 0.93 0.70–1.23 .595
GA 688/588 0.84 0.66–1.07 .163
AA 246/250 0.54 0.36–0.80 .002
≥15 pack-years vs <15 pack-years (ref) rs10507102 GG 1166/1058 0.93 0.77–1.11 .409
GA 222/200 0.38 0.24–0.59 <.001
AA 11/5 0.33 0.02–5.64 .443

BE, Barrett’s esophagus; BMI, body mass index; EA, esophageal adenocarcinoma; EAF, effect allele frequency; GERD, gastroesophageal reflux disease; SNP, single nucleotide polymorphism.

a

P values from logistic regression analysis adjusted for age and sex.

Supplemental Table 3.

Comparison of Gene-Environment Interactions in BE and EA for SNPs With P Value for Interaction <1.0 × 10–6 on the Outcomes

Exposure SNP Chr Position Gene Effect/
Other
BE
EA
OR P OR P
G × E hits for EA
Smoking status rs13429103 2p25.1 7517231 RNF144A-LOC339788 A/G 1.40 3.51 × 10–3 2.04 2.18 × 10–7
Smoking status rs2434584 5q11.2 57566073 ACTBL2-PLK2 C/T 1.56 3.90 × 10–3 2.52 7.44 × 10–7
Smoking status rs40210 5q11.2 57619964 ACTBL2-PLK2 A/G 1.54 4.94 × 10–3 2.46 8.82 × 10–7
Pack-years of smoking rs17002540 Xq27.1 139946061 CDR1-SPANXB2 T/C 0.99 4.83 × 10–3 0.99 5.92 × 10–7
Recurrent GERD symptoms rs12465911 2q23.3 151785742 RND3-RBM43 A/G 1.66 6.09 × 10–5 2.03 1.70 × 10–7
Recurrent GERD symptoms rs2341926 2q23.3 151783928 RND3-RBM43 C/T 1.65 7.38 × 10–5 2.02 1.83 × 10–7
Recurrent GERD symptoms rs13396805 2q23.3 151821512 RND3-RBM43 A/G 1.59 2.80 × 10–4 1.99 3.58 × 10–7
Recurrent GERD symptoms rs2971030 7p21.3 10006341 LOC340268 G/A 1.36 5.03 × 10–3 1.77 6.02 × 10–7
Recurrent GERD symptoms rs7141987 14q32.31 101492224 SNORD114-31-LOC100130814 G/A 1.29 1.40 × 10–2 1.77 7.11 × 10–7
Recurrent GERD symptoms rs2971028 7p21.3 10007255 LOC340268 A/G 1.35 6.10 × 10–3 1.76 8.56 × 10–7
G × E hits for BE
BMI (continuous) rs491603 1p34.3 36532316 EIF2C3-LOC100128093 A/G 1.08 4.44 × 10–7 1.04 1.83 × 10–2
Pack-years of smoking rs11631094 15q14 34624438 SLC12A6 A/C 0.99 2.82 × 10–7 1.00 0.125
Pack-years of smoking rs9668109 12q23.1 99011272 IKIP A/G 0.98 6.31 × 10–7 0.99 9.74 × 10–3
Pack-years of smoking rs1548445 16p12.3 19691583 C16orf62 G/A 1.02 8.21 × 10–7 1.01 9.70 × 10–2
Pack-years of smoking rs2671828 17q12 33731764 SLFN11-LOC729839 A/G 0.99 9.54 × 10–7 1.00 6.13 × 10–2
Pack-years of smoking rs10507102 12q23.1 98990871 SLC25A3 A/G 0.98 9.91 × 10–7 0.99 6.93 × 10–3

BE, Barrett’s esophagus; BMI, body mass index; EA, esophageal adenocarcinoma; EAF, effect allele frequency; G × E, gene-environment; GERD, gastroesophageal reflux disease; OR, odds ratio; SNP, single nucleotide polymorphism.

Supplemental Table 4.

Associations of Previously Reported Gene-Environment Interactions With Esophageal Adenocarcinoma in Our Study Population

Original Publication
Current Study
Author SNP Exposure P Value Directly Genotyped or High-LD SNP P Value
Casson et al, 200624 NA - - - -
Zhai et al, 200825 rs833061 Smoking .03 rs833070 .068
Doecke et al, 200826 rs12269324 GERD symptoms - Direct .979
rs12268840 GERD symptoms - Direct .714
Cheung et al, 200927 rs4444903 GERD symptoms <.001a Direct .240
Zhai et al, 201228 rs1143634 GERD symptoms .008 Direct .398
rs1052486 BMI + Smoking - - -
rs1052486 BMI - Direct .423
rs1052486 Smoking - Direct .532
Wu et al, 201129 rs648802 GERD symptoms .02 Direct .838
rs4855094 GERD symptoms .04 Direct .872
rs7644468 GERD symptoms .04 - -
rs4149579 GERD symptoms .04 - -
rs560191 Smoking .02 Direct .331
rs7907519 Smoking .04 rs11196449 .868
rs12454712 Smoking .04 Direct .435
Zhai et al, 201230 rs2295778 GERD symptoms .0005 rs12780796 .654
rs13337626 GERD symptoms .0067 rs34197769 .315
rs2295778 Smoking .004 - -
rs2296188 Smoking .014 Direct .905
rs2114039 BMI .0026 Direct .228
rs2296188 BMI .0023 Direct .452
rs11941492 BMI .013 Direct NAb
rs17708574 BMI .013 Direct .316
rs7324547 BMI .008 - -
rs17619601 BMI .016 - -
rs17625898 BMI .023 - -
Cheung et al, 201231 rs1799750 GERD symptoms .002 - -
rs3025058 GERD symptoms .04 - -

BMI, body mass index; GERD, gastroesophageal reflux disease; LD, linkage disequilibrium; SNP, single nucleotide polymorphism.

a

Two-way interaction.

b

On array but quality control failure. We were unable to validate all SNPs as some were biallelic or we failed to identify a high LD SNP (r2 < 0.70).

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