Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS, and impact on esophageal cancer risk in Japanese people

We performed an rs671 genotype-stratified GWAS meta-analysis of alcohol consumption within six Japanese cohorts. To validate the stratified approach, we applied a joint meta-analysis (JMA). Further, to validate the impact of the discovered loci on alcohol-related disease, we performed a meta-analysis of two esophageal cancer case-control studies.  GWAS meta-analysis Study subjects and genotyping: We performed a genome-wide meta-analysis based on the Japanese Consortium of Genetic Epidemiology studies (J-CGE) (1), the Nagahama Study (2), and the BBJ Study (3,4). The J-CGE consisted of the following Japanese population-based and hospital-based studies: the HERPACC Study (5), the J-MICC Study (6,7), the JPHC Study (8), and the TMM Study (9). Individual study descriptions and an overview of the characteristics of the study populations are provided in the Supplementary Information and table S1. Data and sample collection for the participating cohorts were approved by the respective research ethics committees. All participating studies obtained informed consent from all participants by following the protocols approved by their institutional ethical committees.   Phenotype: Information on alcohol consumption was collected by questionnaire in each study. Because the questionnaires were not homogeneous across the studies, we harmonized the two alcohol consumption phenotypes of drinking status (never versus ever drinker) and daily alcohol intake (g/day) in accordance with each study’s criterion. Details are provided in the Supplementary Information.   Quality control and genotype imputation: Quality control for samples and SNPs was performed based on study-specific criteria (table S2). Genotype data in each study were imputed separately based on the 1000 Genomes Project reference panel (Phase 3, all ethnicities) (10). Phasing was performed with the use of SHAPEIT (v2) (11) and Eagle (12), and imputation was performed using minimac3 (13), minimac4, or IMPUTE (v2) (14). Information on the study-specific genotyping, imputation, quality control, and analysis tools is provided in table S2. After genotype imputation, further quality control was applied to each study. SNPs with an imputation quality of r2 < 0.3 for minimac3 or minimac4, info < 0.4 for IMPUTE2 or an MAF of <0.01 were excluded.   Association analysis of SNPs with daily alcohol intake and drinking status: Association analysis of SNPs with daily alcohol intake and drinking status was performed on three different subject groups: the entire population, subjects with the rs671 GG genotype only, and subjects with the rs671 GA genotype only. Because the number of ever drinkers with the rs671 AA genotype was too small (table S3), association analysis in subjects with the rs671 AA genotype only was not conducted. Daily alcohol intake was base-2 log-transformed (log2 (grammes/day + 1)). The association of daily alcohol intake with SNP allele dose for each study was assessed by linear regression analysis with adjustment for age, age2, sex, and the first 10 principal components. For the BBJ Study, the affection status of 47 diseases was further added as covariates. The association of drinking status with SNP allele dose for each study was assessed by logistic regression analysis with adjustment for age, age2, sex, the first 10 principal components, and disease affection status of 47 diseases (for the BBJ Study). The effect sizes and standard errors estimated in the association analysis were used in the subsequent meta-analysis. The association analysis was conducted using EPACTS (http://genome.sph.umich.edu/wiki/EPACTS), SNPTEST (15), or PLINK2 (16). Association analysis, including interaction terms, was performed to evaluate the differential effects of each SNP on daily alcohol intake and drinking status between the GG and GA genotypes of rs671. Carriers of the AA genotype were excluded from the analysis. The effect sizes of the interaction term, ?interaction, and its standard errors estimated in the association analysis were used in the subsequent meta-analysis. The association analysis, including the interaction term, was conducted using PLINK2 (16). To identify studies with inflated GWAS significance, which can result from population stratification, we computed the intercept from LDSC (17). Before the meta-analysis, all study-specific results in the association analysis were corrected by multiplying the standard error of the effect size by the value of intercept from LDSC if the intercept of that study was greater than 1.   Meta-analysis: The meta-analysis was performed with all Japanese subjects in the six cohorts (table S1). The results of association analyses for each SNP across the studies were combined with METAL software (18) by the fixed-effects inverse-variance-weighted method. Heterogeneity of effect sizes was assessed by I2 and Cochran’s Q statistic. The meta-analysis included SNPs for wh ...

# Title of Dataset Full summary statistics of GWAS meta-analysis and JMA for daily alcohol intake and drinking status, alongside aggregated data from individual studies for GWAS, JMA, and esophageal cancer case-control study We performed an rs671 genotype-stratified GWAS meta-analysis of alcohol consumption based on the Japanese Consortium of Genetic Epidemiology studies (J-CGE), the Nagahama Study, and the BBJ Study. The J-CGE consisted of the following Japanese population-based and hospital-based studies: the HERPACC Study, the J-MICC Study, the JPHC Study, and the TMM Study. Individual study descriptions and an overview of the characteristics of the study populations are provided in the Supplementary Information and table S1. To validate the stratified approach, we applied a joint meta-analysis (JMA). Further, to validate the impact of the discovered loci on alcohol-related disease, we performed a meta-analysis of two esophageal cancer case-control studies within the HERPACC Study and the BBJ Study. ## Description of the data and file structure 12 files listed below are included. ├── 1_Alcohol_intake_Unstratified.tsv.gz ├── 2_Alcohol_intake_rs671_GA.tsv.gz ├── 3_Alcohol_intake_rs671_GG.tsv.gz ├── 4_Alcohol_intake_rs671_Interaction.tsv.gz ├── 5_Alcohol_intake_rs671_JMA.tsv.gz ├── 6_Drinking_Unstratified.tsv.gz ├── 7_Drinking_rs671_GA.tsv.gz ├── 8_Drinking_rs671_GG.tsv.gz ├── 9_Drinking_rs671_Interaction.tsv.gz ├── 10_Drinking_rs671_JMA.tsv.gz ├── 11_EC_rs671_StratifiedAnalyses.csv ├── 12_EC_rs671_RERI.csv Each file is tab-delimited, with header information described in ade2780_header_definition.xlsx. Files including 'Alcohol_intake' in their filename contain meta-analysis summary statistics and aggregated data from individual studies on daily alcohol intake. Files with 'Drinking' in their filename include meta-analysis summary statistics and aggregated data from individual studies on drinking status (never vs. ever). Each set is categorized into 'Unstratified', 'rs671_GA', 'rs671_GG', 'Interaction', and 'JMA'. 'Unstratified' refers to the overall analysis not stratified by the rs671 genotype. 'rs671_GA' and 'rs671_GG' are analyses restricted to subjects with rs671 GA and GG genotypes, respectively. 'Interaction' involves evaluating the interaction between each SNP and rs671, excluding subjects with the rs671 AA genotype. 'JMA' pertains to the Joint Meta-Analysis. The values under the header names (SNP, CHR, POS, EA, NEA, EAF, BETA, SE, P, HetP, and N for 'Unstratified', 'rs671_GA', 'rs671_GG', and 'Interaction'; SNP, CHR, POS, EA, NEA, BETA_SNP, SE_SNP, BETA_Int, SE_Int, COV_SNP_Int, Pc, HetP, and N for 'JMA') represent meta-analysis summary statistics. Values under any other header names are aggregated data. Files with 'EC' in their filename consist of aggregated data from individual studies for esophageal cancer case-control studies. Patterns are denoted as 'StratifiedAnalyses' and 'RERI'. 'StratifiedAnalyses' includes unstratified and rs671-stratified analyses, while 'RERI' examines the interaction between each SNP and rs671, excluding subjects with the rs671 AA genotype. ## Sharing/Access information Data and materials availability: The full meta-analysis summary statistics for GWAS and JMA, as well as aggregated data from individual studies for GWAS, JMA, and the esophageal cancer case-control study, are accessible at Dryad (DOI: 10.5061/dryad.tmpg4f546). The individual-level genotype or phenotype data cannot be made available due to restrictions imposed by the ethics approval. ## Code/Software GWAS Quality control and genotype imputation: Quality control for samples and SNPs was performed based on study-specific criteria (table S2). Genotype data in each study were imputed separately based on the 1000 Genomes Project reference panel (Phase 3, all ethnicities) (1). Phasing was performed with the use of SHAPEIT (v2) (2) and Eagle (3), and imputation was performed using minimac3 (4), minimac4, or IMPUTE (v2) (5). Information on the study-specific genotyping, imputation, quality control, and analysis tools is provided in table S2. After genotype imputation, further quality control was applied to each study. SNPs with an imputation quality of r2 < 0.3 for minimac3 or minimac4, info < 0.4 for IMPUTE2 or an MAF of <0.01 were excluded. Association analysis of SNPs with daily alcohol intake and drinking status: Association analysis of SNPs with daily alcohol intake and drinking status was performed on three different subject groups: the entire population, subjects with the rs671 GG genotype only, and subjects with the rs671 GA genotype only. Because the number of ever drinkers with the rs671 AA genotype was too small (table S3), association analysis in subjects with the rs671 AA genotype only was not conducted. Daily alcohol intake was base-2 log-transformed (log2 (grammes/day + 1)). The association of daily alcohol intake with SNP all ...

An East Asian-specific variant on aldehyde dehydrogenase 2 (ALDH2 rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study meta-analysis of alcohol consumption in 175,672 Japanese individuals to explore gene-gene interactions with rs671 behind drinking behavior. The analysis identified three genome-wide significant loci (GCKR, KLB, and ADH1B) in wild-type homozygotes and six (GCKR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, and GOT2) in heterozygotes, with five showing genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Of the discovered loci, four (GCKR, ADH1B, ALDH1A1, and ALDH2) were suggested to interact with rs671 in the risk of esophageal cancer, a representative alcohol-related disease. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.

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