DOI: 10.5061/dryad.tmpg4f546
title: Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS, and impact on esophageal cancer risk in Japanese people

description: An East Asian-specific variant on aldehyde dehydrogenase 2 (ALDH2 rs671,
 G&gt;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.
description: 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.  <strong>GWAS
 meta-analysis</strong> <strong>Study
 subjects and genotyping: </strong>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.
 <strong> </strong>
 <strong>Phenotype: </strong>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.   <strong>Quality
 control and genotype imputation: </strong>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 r<sup>2</sup> &lt; 0.3 for
 minimac3 or minimac4, info &lt; 0.4 for IMPUTE2 or an MAF of
 &lt;0.01 were excluded.  
 <strong>Association analysis of SNPs with daily alcohol
 intake and drinking status: </strong>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
 (log<sub>2</sub> (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, age<sup>2</sup>,
 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,
 age<sup>2</sup>, 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, ?<sub>interaction</sub>,
 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.  
 <strong>Meta-analysis: </strong>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 <em>I</em><sup>2</sup> and
 Cochran’s <em>Q </em>statistic. The meta-analysis included
 SNPs for which genotype data were available from at least three studies
 with a total sample size of at least 20,000 individuals for unstratified
 GWAS or interaction GWAS or 10,000 individuals for rs671-stratified GWAS.
 The genome-wide significance level α was set to a <em>P</em>
 value &lt;5 × 10<sup>–8</sup>.
 <em>P</em>-values with
 &lt;1.0×10<sup>−</sup><sup>300</sup> was
 calculated with Rmpfr of the R package. To assess the inflation of the
 test statistics for the meta-analysis, we computed the genomic inflation
 factor, l, and intercept from LDSC (19).  
 <strong>JMA</strong> We used the
 JMA approach (20,21). The JMA jointly tests both SNP main effects
 ?<sub>SNP</sub> and SNP × rs671 interaction effects
 ?<sub>interaction</sub> for spherical equivalent with a
 fixed-effects model, using ?<sub>SNP</sub> and
 ?<sub>interaction</sub> and a ?’s covariance matrix from each
 study. To perform the JMA, the same model as the interaction analysis for
 each study described above was analyzed using GEM v1.4 (22), which is
 capable of obtaining robust covariance matrices for
 ?<sub>SNP</sub> and ?<sub>interaction</sub>. To
 control false positives, only SNPs with MAF ≥ 0.05 were analyzed by the
 GEM for each study. The JMA was conducted with the
 fixed effects method using METAL software (version 2010-02-08) (18) and
 patch source code provided by Manning et al. (20). A Wald’s statistic,
 following a ?<sup>2</sup>-distribution with two degrees of
 freedom (d.f.), was used to test the joint significance of the
 ?<sub>SNP</sub> and ?<sub>interaction</sub>. A
 Cochran’s <em>Q</em>-test was used to assess the heterogeneity
 of the ?-coefficients across studies for the ?<sub>SNP</sub>
 and ?<sub>interaction</sub>. The <em>cor</em>
 value was calculated by <em>cor</em> = (IntCov/StdErr ×
 IntStdErr). IntCov is the covariance between ?<sub>SNP</sub>
 and ?<sub>interaction</sub> estimated by the JMA. StdErr and
 IntStdErr are standard errors of ?<sub>SNP</sub> and
 ?<sub>interaction</sub> estimated by the JMA, respectively.
 The JMA included SNPs for which genotype data were available from at least
 three studies with a total sample size of at least 20,000 individuals for
 interaction GWAS. To control false positives, SNPs with evidence of
 between study heterogeneity (Het<em>P</em> &lt; 0.001) and
 <em>cor</em> &lt; 0.7 were excluded (fig. S7). Genomic
 control correction was applied by calculating ? as the ratio of the
 observed and expected (2 d.f.) median ?<sup>2</sup> statistics
 and dividing the observed ?<sup>2</sup> statistics by ?. The
 genome-wide significance level ? for the JMA test was set to a
 <em>P</em> value &lt;5 ×
 10<sup>–8</sup>.  
 <strong>Esophageal cancer case-control
 study</strong> <strong>Study sample:
 </strong>In the HERPACC Study, we included 692 cases and 995 age-
 and sex-matched controls who were selected from participants in the
 HERPACC-2 (2001–2005) (23) and HERPACC-3 (2005–2013) (24). Cases were
 first-visit outpatients at Aichi Cancer Center Hospital who were diagnosed
 with esophageal cancer within -3 to +12 months of the first visit.
 Controls were first-visit outpatients who were confirmed to have no cancer
 or history of neoplasm. The BBJ Study included 416 cases and 86,515
 controls after excluding (1) outliers from the Japanese cluster, as
 estimated by principal component analysis with samples of the 1000 Genomes
 project (10); and (2) closely related individuals estimated by King (25)
 (specifically, King kinship coefficients &gt; 0.09375). Cases were
 diagnosed with esophageal cancer within -3 to +12 months from the date of
 consent. Controls were those confirmed to have no cancer or history of
 neoplasm. In the HERPACC Study, esophageal cancer cases were identified
 using the International Classification of Diseases for Oncology, Third
 Edition (ICD-O-3) (26) topography code C15. As a sensitivity analysis, we
 performed an additional analysis restricted to cases with squamous cell
 carcinoma identified using the ICD-O-3 morphology codes of 8050–8078 and
 8083–8084, resulting in 636 cases. In the BBJ Study, all participants had
 been diagnosed with at least one of 47 target diseases, including
 esophageal cancer, by physicians at the cooperating hospitals. Esophageal
 cancer histology was determined from excised tissue specimens, and missing
 histological data were complemented by cytological specimens, resulting in
 348 cases of squamous cell carcinoma.  
 <strong>Genotyping and imputation procedure:
 </strong>In the HERPACC Study, genomic DNA was extracted from
 peripheral blood using a DNA Blood mini kit (Qiagen, Tokyo, Japan), and
 eight SNPs were genotyped using TaqMan Assays with the 7500 Real-Time PCR
 System (Applied Biosystems, Foster City, CA, USA) or SNPtype assays with
 JUNO and EP1 System (Fluidigm, San Francisco, CA, USA). We confirmed a
 100% match between rs4648328 and rs79463616 genotypes in the 96 selected
 HERPACC samples using Sanger sequencing. The genotyping and imputation
 procedure in the BBJ Study is described in the ‘Details of studies’
 section of the Supplementary Information.  
 <strong>Statistical analysis: </strong>ORs for
 esophageal cancer per 1-allele change in eight SNPs were estimated on
 three different subject groups (entire population, subjects with the rs671
 GG genotype only, and subjects with the rs671 GA genotype only) using a
 logistic regression model adjusted for sex, age, the first 10 principal
 components (for the BBJ Study), and the study version (for the HERPACC
 Study). Study-specific ORs were then pooled using a random-effects model
 (27). We evaluated the extent of between-study heterogeneity by the
 Cochran <em>Q</em>-statistic and
 <em>I<sup>2</sup></em>-statistic (28).
 The interaction between rs671 and each of the variants under
 study was evaluated on both additive and multiplicative scales. Carriers
 of the rs671 AA genotype were excluded from this analysis. First, we
 estimated the study-specific coefficients of each SNP (per 1-allele
 change), rs671 (GA vs. GG), and the product term of each SNP and rs671
 using the logistic regression model. For the HERPACC Study, each SNP was
 coded as follows: the Ref/Ref genotype was coded as 0, the Ref/Alt
 genotype as 1, and the Alt/Alt genotype as 2. For the BBJ Study, each SNP
 was the imputed genotype coded as [0,2] for each SNP. For the rs671
 phenotype, the GG genotype was coded as 0, and the GA genotype was coded
 as 1. We then obtained pooled estimates ?<sub>SNP</sub>,
 ?<sub>rs671</sub> and ?<sub>interaction</sub> of
 the model coefficients corresponding respectively to the effect of the
 SNP, rs671, and their interaction term (the product term for each SNP and
 rs671) using multivariate meta-analysis (29) to account for the fact that
 coefficients estimated from the same study are correlated. Specifically,
 we conducted random-effects multivariate analyses based on likelihood
 maximization using the study-specific coefficients
 ?<sub>SNP</sub>, ?<sub>rs671</sub> and
 ?<sub>interaction</sub> as well as their covariance matrix.
 Multiplicative interaction was measured by the summary OR associated with
 the interaction term with its corresponding 95% confidence interval. As
 the measure of additive interaction, we estimated the relative excess risk
 due to interaction (RERI) (30). Confidence intervals for RERI were
 estimated by the Delta Method (31) and <em>P</em>-values were
 based on the Wald test. Statistical significance was set at the Bonferroni
 corrected threshold of <em>P</em> &lt; 0.05/8 (= 0.00625)
 and suggestive significance was set at <em>P</em>
 &lt;0.05. RERI was considered to achieve suggestive significance
 (<em>P</em> &lt; 0.05) when its confidence interval did
 not include 0. Analyses were performed with R version 4.1.2 (The R
 Foundation for Statistical Computing) or STATA version 17.0 (Stata
 Corporation, College Station, TX, USA).
 <strong> </strong>
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description: No special software are required to open the files.
description: # 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 &lt; 0.3 for minimac3 or
 minimac4, info &lt; 0.4 for IMPUTE2 or an MAF of &lt;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
 (), SNPTEST (6), or PLINK2
 (7). 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 (7). To identify studies with inflated GWAS significance, which can
 result from population stratification, we computed the intercept from LDSC
 (8). 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 (9) 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 which genotype data were available from at least three studies with a
 total sample size of at least 20,000 individuals for unstratified GWAS or
 interaction GWAS or 10,000 individuals for rs671-stratified GWAS. The
 genome-wide significance level α was set to a P value &lt;5 × 10–8.
 P-values with &lt;1.0×10−300 was calculated with Rmpfr of the R package.
 To assess the inflation of the test statistics for the meta-analysis, we
 computed the genomic inflation factor and intercept from LDSC (10). JMA We
 used the JMA approach (11,12). The JMA jointly tests both SNP main effects
 ?SNP and SNP × rs671 interaction effects ?interaction for spherical
 equivalent with a fixed-effects model, using ?SNP and ?interaction and a
 ?’s covariance matrix from each study. To perform the JMA, the same model
 as the interaction analysis for each study described above was analyzed
 using GEM v1.4 (13), which is capable of obtaining robust covariance
 matrices for ?SNP and ?interaction. To control false positives, only SNPs
 with MAF ≥ 0.05 were analyzed by the GEM for each study. The JMA was
 conducted with the fixed effects method using METAL software (version
 2010-02-08) (9) and patch source code provided by Manning et al. (11). A
 Wald’s statistic, following a ?2-distribution with two degrees of freedom
 (d.f.), was used to test the joint significance of the ?SNP and
 ?interaction. A Cochran’s Q-test was used to assess the heterogeneity of
 the ?-coefficients across studies for the ?SNP and ?interaction. The cor
 value was calculated by cor = (IntCov/StdErr × IntStdErr). IntCov is the
 covariance between ?SNP and ?interaction estimated by the JMA. StdErr and
 IntStdErr are standard errors of ?SNP and ?interaction estimated by the
 JMA, respectively. The JMA included SNPs for which genotype data were
 available from at least three studies with a total sample size of at least
 20,000 individuals for interaction GWAS. To control false positives, SNPs
 with evidence of between study heterogeneity (HetP &lt; 0.001) and cor
 &lt; 0.7 were excluded (fig. S7). Genomic control correction was applied
 by calculating ? as the ratio of the observed and expected (2 d.f.) median
 ?2 statistics and dividing the observed ?2 statistics by ?. The
 genome-wide significance level ? for the JMA test was set to a P value
 &lt;5 × 10–8. Esophageal cancer case-control study ORs for esophageal
 cancer per 1-allele change in eight SNPs (rs1260326, rs28712821,
 rs1229984, rs2228093, rs8187929, rs4648328, rs671, rs73550818) were
 estimated on three different subject groups (entire population, subjects
 with the rs671 GG genotype only, and subjects with the rs671 GA genotype
 only) using a logistic regression model adjusted for sex, age, the first
 10 principal components (for the BBJ Study), and the study version (for
 the HERPACC Study). Study-specific ORs were then pooled using a
 random-effects model (14). We evaluated the extent of between-study
 heterogeneity by the Cochran Q-statistic and I2-statistic (15). The
 interaction between rs671 and each of the variants under study was
 evaluated on both additive and multiplicative scales. Carriers of the
 rs671 AA genotype were excluded from this analysis. First, we estimated
 the study-specific coefficients of each SNP (per 1-allele change), rs671
 (GA vs. GG), and the product term of each SNP and rs671 using the logistic
 regression model. For the HERPACC Study, each SNP was coded as follows:
 the Ref/Ref genotype was coded as 0, the Ref/Alt genotype as 1, and the
 Alt/Alt genotype as 2. For the BBJ Study, each SNP was the imputed
 genotype coded as [0,2] for each SNP. For the rs671 phenotype, the GG
 genotype was coded as 0, and the GA genotype was coded as 1. We then
 obtained pooled estimates ?SNP, ?rs671 and ?interaction of the model
 coefficients corresponding respectively to the effect of the SNP, rs671,
 and their interaction term (the product term for each SNP and rs671) using
 multivariate meta-analysis (16) to account for the fact that coefficients
 estimated from the same study are correlated. Specifically, we conducted
 random-effects multivariate analyses based on likelihood maximization
 using the study-specific coefficients ?SNP, ?rs671 and ?interaction as
 well as their covariance matrix. Multiplicative interaction was measured
 by the summary OR associated with the interaction term with its
 corresponding 95% confidence interval. As the measure of additive
 interaction, we estimated the relative excess risk due to interaction
 (RERI) (17). Confidence intervals for RERI were estimated by the Delta
 Method (18) and P-values were based on the Wald test. Statistical
 significance was set at the Bonferroni corrected threshold of P &lt;
 0.05/8 (= 0.00625) and suggestive significance was set at P &lt;0.05. RERI
 was considered to achieve suggestive significance (P &lt; 0.05) when its
 confidence interval did not include 0. Analyses were performed with R
 version 4.1.2 (The R Foundation for Statistical Computing) or STATA
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