Introduction

Colorectal cancer (CRC) is the third-most common cancer in men and the second most common cancer in women worldwide [1]. Both environmental and genetic factors are involved in the development of CRC [2–7]. Since 2007, genome-wide association studies (GWAS) have identified about 50 loci associated with CRC risk[8–11]. However, only a small portion of the familial aggregation of CRC is explained by these identified genetic loci, and additional variants associated with CRC susceptibility are more likely to be identified through analyses of interactions between genes and environmental risk factors [12, 13]. Single nucleotide polymorphisms (SNP) that impact only a subgroup of the population or have opposite effects in different subgroups are likely to produce weak main effects that cannot be easily detected by marginal association testing of the SNPs. However, these variants may be identified by testing for interactions between SNP and environmental risk factors (genome-wide interaction analysis) [14, 15]. These findings may provide etiologic insight into CRC and identify potentially susceptible subpopulations [14, 15].

There is compelling evidence from epidemiologic studies that alcohol consumption and cigarette smoking are associated with risk of CRC [16–25]. Both alcohol consumption and cigarette smoking influence disease risk through pathways involving multiple gene products and regulatory elements, providing potential for biological interactions [26–28]. Accordingly, alcohol consumption and smoking are important lifestyle factors to study interactions with genetic variants. In this study, we performed a genome-wide interaction analysis using the large datasets from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) [3] to identify SNPs that modify the effects of alcohol and smoking on CRC risk.

Results

In this study, we included 14 studies from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) as described previously [3, 29, 30] and in the S1 Text and S1 and S2 Tables. Basic characteristics of the participants, stratified by study center, are described in S1 and S2 Tables, respectively. We were able to harmonize measures of alcohol consumption across 8,058 cases and 8,765 controls and measures of smoking across up to 11,219 cases and 11,382 controls. As seen for other common diseases, such as cardiovascular diseases, alcohol consumption shows a different effect with CRC risk depending on the level of alcohol consumed. Heavy alcohol intake (>2 standard drinks per day) has been shown to be associated with increased risk of CRC [16, 17, 31] while light-to-moderate drinking (<2 standard drinks per day) may have little effect [18, 19] or reduce risk of CRC [16, 20–22] compared to non-drinkers. Consistent with these previous publications [16–22, 31] we observed an inverse association with CRC risk for light-to-moderate drinkers (OR = 0.91, P = 0.006, Fig 1A) but a positive association for heavy drinkers (OR = 1.22, P = 0.0004, Fig 1B) compared with non-/occasional drinkers. Modeling alcohol using this categorical approach fitted the association between alcohol intake and CRC risk better than the continuous variable based on the Akaike Information Criterion (AIC) which was 12.42 smaller for the model including the two categorical variables compared with the model including the continuous variable (AIC = 23123.72 for continuous alcohol and AIC = 23111.3 for categorical alcohol)[32]. Given the opposite effect of light/moderate alcohol drinking vs. heavy drinking, it is critical that analyses further investigating the impact of alcohol on CRC, such as interaction analysis do this separately for light/moderate and heavy drinking. Ever-smokers and pack-years of cigarette smoking were positively associated with CRC risk (OR = 1.18 for ever vs. never smokers, P = 8.9×10⁻⁹; OR = 1.11 per 10 pack-years increase, P = 7.1×10⁻¹³, Fig 2A and 2B). None of the smoking and alcohol variables showed evidence of heterogeneous associations across studies (P_{heterogeneity}>0.16).

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Fig 1. The association between CRC and alcohol consumption (non-/occasional drinkers [reference group]; light-to-moderate drinkers [a]; and heavy drinkers[b]).

Men and women were analyzed separately in each study and age and study site (if applicable) were adjusted in model. Non-/occasional drinkers: drinking < 1 gram of alcohol per day; light-to-moderate drinkers: drinking 1–28 grams of alcohol per day ([a] alcoholc1-28g/d); and heavy drinkers: drinking >28 grams of alcohol per day ([b] alcoholc>28g/d). OR: odds ratio; N = total number of subjects; case = number of cases. Colon23: Hawaii Colorectal Cancer Studies 2 and 3; DACHS: Darmkrebs: Chancen der Verhütung durch Screening; DALS: Diet, Activity and Lifestyle Study; HPFS: Health Professionals Follow-up Study; HPFS_AD: Health Professionals Follow-up Study for colorectal adenoma; MEC: Multiethnic Cohort Study; NHS: Nurses’ Health Study; NHS_AD: Nurses’ Health Study for colorectal adenoma; PHS: Physicians’ Health Study; PLCO: Prostate, Lung, Colorectal and Ovarian Cancer; Screening Trial; VITAL: VITamins And Lifestyle; WHI: Women’s Health Initiative. het.pval: p value of heterogeneity.

https://doi.org/10.1371/journal.pgen.1006296.g001

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Fig 2. The association between CRC and smoking (ever vs. never smokers [a]; pack-years of smoking [b]).

Never smokers were assigned the value 0 for pack-years of smoking. OR: odds ratio; OR for pack-years of smoking is based on per 20 pack-years increase. Age, sex (if applicable), and study site (if applicable) were adjusted in model. ASTERISK: The French Association STudy Evaluating RISK for sporadic colorectal cancer; CCFR: Colon Cancer Family Registry; Colon23: Hawaii Colorectal Cancer Studies 2 and 3.; DACHS: Darmkrebs: Chancen der Verhütung durch Screening; DALS: Diet, Activity and Lifestyle Study; HPFS:Health Professionals Follow-up Study; HPFS_AD: Health Professionals Follow-up Study for colorectal adenoma; MEC: Multiethnic Cohort Study; NHS: Nurses’ Health Study; NHS_AD: Nurses’ Health Study for colorectal adenoma; OFCCR: Ontario Familial Colorectal Cancer Registry; PMH-CCFR: Postmenopausal Hormone study- Colon Cancer Family Registry; PLCO: Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial; VITAL: VITamins And Lifestyle; WHI: Women’s Health Initiative. CCFR is a collaborating study with GECCO. smk_ever: ever smokers; smk_pkyr20: pack-years of smoking; het.pval: p value of heterogeneity.

https://doi.org/10.1371/journal.pgen.1006296.g002

Using conventional logistic regression including multiplicative interaction terms, we identified genome-wide significant interactions (at P<5×10⁻⁸) between 11 SNPs at the 9q22.32/HIATL1 (Hippocampus Abundant Transcript-Like 1) locus and light-to-moderate drinking with no evidence of heterogeneity across studies (P_{heterogeneity}>0.5 for any of the 11 SNPs) (S3 Table, Fig 3). All 11 SNPs were common variants with minor allele frequency (MAF) between 0.31–0.34 and genotyped or imputed with high accuracy (imputation r²>0.98, S3 Table). The most significant SNP was rs9409565 with P_interaction = 1.76×10⁻⁸; permuted p-value 3.51x10^-8 (Table 1, Fig 4C). The genetic variant was located in an intergenic region (28kb downstream of HIATL1 and 70kb downstream of FBP2, Fig 3). All the other 10 genome-wide significant SNPs were in strong linkage disequilibrium (LD) with rs9409565 (LD r²>0.8, S3 Table, Fig 3) and some of them were located within the gene HIATL1. The observed interaction for rs9409565 was similar in men and women and by cancer site (colon vs rectum) (Fig 4A and 4B, S4 Table). We did not observe any genome-wide significant interaction between any SNP and heavy drinking. No inflation was observed in the genome-wide SNP × alcohol interaction analysis (the inflation factor λ = 0.99 and 1.00 for light-to-moderate drinkers and heavy drinkers, respectively). To evaluate the potential confounding[33] by other lifestyle and environmental risk factors of the interactions between rs9409565 and light-to-moderate alcohol consumption in relation to CRC risk, we adjusted for smoking status (ever vs. never) and BMI (two variables have the highest correlation r = 0.15 and 0.13 with alcohol consumption in our data), as well as exercise, fruit and vegetable consumption in the conventional case-control logistic regression model. Our results did not change (multivariate adjusted interaction p-value = 4.34x10^-8).

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Fig 3. Regional association plot for the interaction analyses between moderate alcohol drinking and SNPs at 9q22.32/HIATL1.

The–log10 of p values (left y-axis) are plotted against the SNP genomic position based on NCBI build 37 (x-axis); the estimated recombination rate from 1000 Genomes Project European populations are on the right y-axis and plotted in blue. The most significant SNP was denoted with purple diamond. SNPs are colored to reflect correlation with the most significant SNP. Gene annotations are from the UCSC genome browser. Gene FAM22F is also known as NUTM2F.

https://doi.org/10.1371/journal.pgen.1006296.g003

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Table 1. Stratification analyses^a by genotypes of rs9409565 for the association between alcohol consumption and CRC.

https://doi.org/10.1371/journal.pgen.1006296.t001

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Fig 4. Forest plot for meta-analysis of interaction analysis for rs9409565 and light-to-moderate drinking among men (a), women (b) and combined (c).

Odds ratios (ORs) and 95% confidence intervals (95% CI) are presented for the multiplicative interaction between each additional copy of the count (or tested) allele (C) and light-to-moderate vs. non/occasional drinkers. The box sizes are proportional in size to the inverse of the variance for each study, and the lines visually depict the confidence interval. Results from the fixed-effects meta-analysis are shown as diamonds. The width of the diamond represents the confidence interval. P value of heterogeneity for (a), (b), and (c) is 0.93, 0.78, and 0.96, respectively.

https://doi.org/10.1371/journal.pgen.1006296.g004

When stratified by genotype rs9409565, light-to-moderate alcohol consumption (compared to non/occasional alcohol consumption) significantly decreased CRC risk in individuals with CT genotype (prevalence, 45% vs 49%; OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10⁻⁴) and TT genotype (prevalence, 42% vs 52%; OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10⁻⁶) but not in those with CC genotype (P = 0.059) (Table 1, S5 Table). The association between alcohol intake and CRC was also not heterogeneous within each genotype strata (p-heterogeneity > 0.73; S1 Fig).

We also estimated absolute risks of CRC based on Surveillance, Epidemiology, and End Results (SEER) age-adjusted incidence rates (Table 2). Compared with non/occasional drinking, light-to-moderate drinking was associated with 14.0 fewer CRC cases per 100,000 individuals carrying the rs9409565-CT genotype per year; 35.5 fewer CRC cases per 100,000 individuals carrying the rs9409565-TT genotype per year.

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Table 2. Absolute risk^a of CRC for alcohol consumption among individuals with different genotypes of rs9409565.

https://doi.org/10.1371/journal.pgen.1006296.t002

Using the Cocktail method as a two-step method that may improve power we did not observe any genome-wide significant SNP×alcohol interactions. Further, we did not observe any genome-wide significant interactions for SNP×smoking (smoking history and pack-years of smoking) using logistic regression or the Cocktail method.

Discussion

We identified a suggestive interaction between variants at 9q22.32/HIATL1 and light-to-moderate alcohol consumption in relation to CRC risk. This is the first genome-wide significant GxE interaction reported for alcohol intake and risk of CRC and warrants replication in independent studies. Evidence for overlap between the discovered 9q22.32/HIATL1 region with VEL as well as gene expression results support the relevance of the 9q22.32/HIATL1 region for CRC risk.

Gene expression analyses indicated that a) SNPs identified in our study impact HIATL1 expression, b) HIATL1 is involved in signaling pathways related to CRC and expression differs between normal and tumor CR tissue, and c) HIATL1 expression in colon tissue differs by alcohol consumption. The most significant variant rs9409565 is correlated with 142 variants (LD r²≥0.5 in Phase 3 1000 Genomes European populations), which spanned across intronic regions and approximately 50kb downstream and 75kb upstream of HIATL1. Nine of these variants (including rs9409550, rs4744345, rs9409546, rs9409778, and rs639276, all with interaction P<5×10⁻⁸) fall within a transcriptionally active region in normal colon, rectal and duodenal mucosa [44] as defined by epigenetic signals.[45] Furthermore, these variants fall in a region of enriched enhancer signal; although we note that currently available ChIP-seq data are not able to identify a putative transcription factor binding site at any of the tagged SNPs (S6 Fig). In support of our findings that HIATL1 expression is higher in tumor than adjacent normal colorectal tissue, ChIP-seq (H3k27ac) enhancer signals suggest that this locus implicates a gained enhancer present in CR tumors that is absent in normal crypt cells (S6 Fig). In summary, multiple data points suggest that the genetic variants we identified to interact with alcohol on CRC risk are located in regulatory regions impacting the expression of HIATL1 and that HIATL1 expression varies by alcohol consumption.

HIATL1 is a member of the solute carrier (SLC) group of membrane transport, which enables the directed movement of substances (such as peptides, amino acids, proteins, metals, and neurotransmitters) into or out of cells and plays an important role in a variety of cellular functions [46, 47]. Although the detailed function of HIATL1 remains elusive, this gene was found to be expressed in a large range of animal species and it is highly evolutionarily conserved [48], suggesting an potentially important functional role. Transporter proteins are commonly upregulated in many cancers [49, 50] and take part in nutrient signaling to the mTOR pathway [51] which is an important signaling pathway in apoptosis and cancer [52–54]. Alcohol may modify the effects of HIATL1 on CRC risk through its influence on the gene expression of HIATL1. Nonetheless, the precise mechanism(s) of the interaction between alcohol and HIATL1 on CRC risk remains unclear and further studies are needed.

Our Cocktail method for detecting G×E interactions did not identify the statistical interaction detected by the conventional logistic regression analysis because rs9409565 did not show strong statistical evidence for association with CRC risk in the marginal association analyses (P = 0.54, OR = 1.014) or with alcohol consumption (P = 0.22). Accordingly, this SNP was ranked low in step 1 of the Cocktail method, resulting in very stringent alpha-threshold for the interaction term in step 2. Although the conventional logistic regression analysis tends to be less powerful overall for genome-wide interaction analysis compared with the Cocktail method [14, 55], it has greater power to detect an association if the marginal association of the SNP on disease or the correlation of the SNP with environmental factor are weak as it was the case for the observed interaction. In addition, no association between rs9409565 and alcohol consumption excluded the possibility that the observed interaction was due to the dependence between them [56]. We also explored the effect of rs9409565 and alcohol using other potentially more powerful single step approaches and observed a similar interaction effect in the Empirical Bayesian analysis[57] and a weaker interaction effect in the case-only analysis[58], which may be explained by the non-significant differential effect of alcohol on CRC in individual carrying the CC genotype (S6 Table).

To investigate if genome-wide interaction may help identifying variants that would be missed we looked up the marginal association of rs9409565 in the largest GWAS[59] which is about twice as large as our study and showed an OR for rs9409565 of 0.975 (95%CI 0.946–1.007, p-value 0.127). Accordingly, the variant by itself showed only weak evidence for association with CRC. This may not be surprising given that it is estimated that the sample sizes required to identify GxE interaction vs. main effects is at least 4x larger[60]. Our study has several strengths, including the large sample size, environmental exposure assessment in well-characterized populations, and standardized harmonization of environmental data across studies. Further, there is no evidence of heterogeneity across studies for our findings, indicating our results are not dominated by one or a few studies and, indeed, represent evidence across all studies. There are also some limitations. Because amassing sufficient study power for genome-wide interaction analysis is a challenge, we combined all studies in the analysis to gain the greatest power[61] instead of dividing studies into discovery and replication sets. Although we do not have a replication set, the consistency of our findings across all studies and the independent evidence from different types of gene expression data and bioinformatics analyses support a novel interaction for CRC risk between alcohol intake and variants in the 9q22.32/HIATL1 region. Our analyses focused on current alcohol consumption, rather than lifetime alcohol use, which may cause misclassification of a certain portion of alcohol users. Both differential and non-differential misclassifications of alcohol consumption levels tend to lead to underestimation of interaction parameters (e.g. leading to non-significant interaction term between SNP and alcohol intake) [62], accordingly, we may have missed some true interactions. However, it is unlikely that this led to false positives for the interactions observed. Because, there is no strong evidence that the type of alcohol (usually defined as wine, beer and hard liquor) has a differential impact on CRC[63] we have not investigated interaction between genetic variants and type of alcohol. As we preformed genome-wide interaction testing for two environmental risk factors (smoking and alcohol consumption), additional adjustment for multiple comparisons may be needed. However, we note that the observed interaction at 9q22.32/HIATL1 would remain borderline significant (alpha threshold = 5×10⁻⁸/2 = 2.5×10⁻⁸). The small numbers of heavy drinkers, particular in women, impeded the reliable estimation of interaction parameters and limited our power to identify significant interaction between SNP and heavy drinking. We focused gene expression analysis on HIATL1 because rs9409565 is located in an intergenic region between HIATL1 and FBP2 and further there is a recombination hotspot lying between rs9409565 and FPB2. If we expand gene expression analyses for all genes 500kb upstream or downstream 500kb of rs9409565 in the 35 pairs of colorectal tumor-normal tissue samples (S2 Text) we observed no significant result after false discovery rate (FDR) correction. The most significant results were for MIRLET7F which has a p value of 0.001 for testing differential gene expression across various levels of lifetime alcohol consumption in normal tissues and PTPDC1 which has a p value of 0.002 for testing differential gene expression across various levels of alcohol consumption at reference time. Further studies are needed to confirm our findings.

Alcohol has a particularly detrimental effect on several cancers, possibly including CRC, in Asian subpopulations with genetic determined alcohol sensitivity[64–66]. However, as we have focused our analysis on European descent populations and did not observe significant differences of the alcohol-CRC association between studies (phet = 0.16–0.76) we do not expect major underlying differences of the effect of alcohol in our study populations.

We did not perform stratification analyses by anatomical sites for our genome-wide GxE interaction analysis because the association of CRC with alcohol consumption (S7 Table) and smoking [23] did not vary according to anatomical site within the large bowel. Although we did observe potential interactions for alcohol consumption, we did not observe statistical evidence for genome-wide SNP x smoking interactions. This may be because smoking has a weaker association with CRC compared with alcohol intake [24, 26, 67], so we may have been underpowered even with more than 10,000 cases and 10,000 controls. We also may not have properly captured the most relevant smoking variables, such as duration of smoking or time since quitting smoking. The association between smoking and CRC risk are strongest for tumors that display certain molecular features such as microsatellite instability (MSI)-high and CpG island methylator phenotype (CIMP)-positive [68, 69]. Because of the lack of MSI or CIMP data in several studies, we cannot perform stratification analysis by tumor characteristics for smoking-related analyses.

We note that it would be too early to make any recommendation on alcohol intake from our findings even after independent replication given that such recommendation need to be considered in context of the effect of alcohol on all diseases. Furthermore, it will be important to investigate the interactions between alcohol and genetic variants in larger studies to comprehensively evaluate the full impact of genetic variation on the effect of alcohol on colorectal cancer risk.

In summary, we identified a tentative novel interaction for CRC risk between alcohol intake and variants at 9q22.32/HIATL1. Further replication and functional studies are required to confirm our findings and understand the biologic implications of the interaction. This, in turn, could provide further insight into CRC etiology and may identify potentially susceptible subpopulations.

Materials and Methods

Supporting Information

Acknowledgments

We thank Dr. Wei Sun for providing eQTL results on TCGA data. We thank Dr. Peter Scacheri for his guidance on the variant enhancer loci analysis.

ASTERISK: We are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students.

DACHS: We thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik and Ursula Eilber for excellent technical assistance.

GECCO: The authors would like to thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. The authors also acknowledge COMPASS (Comprehensive Center for the Advancement of Scientific Strategies) at the Fred Hutchinson Cancer Research Center for their work harmonizing the GECCO epidemiological data set. The authors acknowledge Dave Duggan and team members at TGEN (Translational Genomics Research Institute), the Broad Institute, and the Génome Québec Innovation Center for genotyping DNA samples of cases and controls, and for scientific input for GECCO.

HPFS, NHS and PHS: We would like to acknowledge Patrice Soule and Hardeep Ranu of the Dana Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS, HPFS, and PHS under the supervision of Dr. Immaculata Devivo and Dr. David Hunter, Qin (Carolyn) Guo and Lixue Zhu who assisted in programming for NHS and HPFS, and Haiyan Zhang who assisted in programming for the PHS. We would like to thank the participants and staff of the Nurses' Health Study and the Health Professionals Follow-Up Study, for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

PLCO: The authors thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the Screening Center investigators and staff or the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Mr. Tom Riley and staff, Information Management Services, Inc., Ms. Barbara O’Brien and staff, Westat, Inc., and Drs. Bill Kopp, Wen Shao, and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions to making this study possible. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI.

PMH: The authors would like to thank the study participants and staff of the Hormones and Colon Cancer study.

WHI: The authors thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf