社会経済的地位と消化器がんの関係について : 症例対照研究

Although socioeconomic status (SES) has been associated with cancer risk, little research on this association has been done in Japan. To evaluate the association between SES and digestive tract cancer risk, we conducted a case-control study for head and neck, esophageal, stomach, and colorectal cancers in 3188 cases and the same number of age- and sex-matched controls within the framework of the Hospital-based Epidemiological Research Program at Aichi Cancer Center III (HERPACC III). We employed the education level and areal deprivation index (ADI) as SES indicators. The association was evaluated with odds ratios (ORs) and 95% confidence intervals (CIs) by conditional logistic models adjusted for potential confounders. Even after allowance for known cancer risk factors, the education level showed linear inverse associations with head and neck, stomach, and colorectal cancers. Compared to those educated to junior high school, those with higher education showed statistically significantly lower risks of cancer (0.43 (95% CI: 0.27–0.68) for head and neck, 0.52(0.38–0.69) for stomach, and 0.52(0.38–0.71) for colorectum). Consistent with these results for the educational level, the ADI in quintiles showed positive associations with head and neck, esophageal, and stomach cancers (p-trend: p = 0.035 for head and neck, p = 0.02 for esophagus, and p = 0.013 for stomach). Interestingly, the positive association between ADI and stomach cancer risk disappeared in the additional adjustment for Helicobacter pylori infection and/or atrophic gastritis status. In conclusion, a lower SES was associated with an increased risk of digestive cancers in Japan and should be considered in cancer prevention policies for the target population.An association between socioeconomic status (SES) and cancer risk has been reported, but little is known in Asia. We revealed an association between SES, including education level and areal deprivation index (ADI), and digestive tract cancers in Japan. Lower SES was associated with an increased risk of digestive cancers. For stomach cancer, the positive association with ADI disappeared following an additional adjustment of Helicobacter pylori infection and/or atrophic gastritis status. Cancer prevention policy should consider both individual and regional perspectives by the integration of SES in the target population

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

<p><span>An East Asian-specific variant on <em>aldehyde</em> <em>dehydrogenase</em> <em>2</em> (<em>ALDH2</em> rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study (GWAS) meta-analysis in up to 175,672 Japanese individuals to uncover additional loci associated with alcohol consumption in an rs671-dependent manner. Three loci (<em>GCKR</em>, <em>KLB</em>, and <em>ADH1B</em>)</span> <span>satisfied the genome-wide significance threshold in wild-type homozygotes (GG), whereas six loci (<em>GCKR</em>, <em>ADH1B</em>, <em>ALDH1B1</em>, <em>ALDH1A1</em>, <em>ALDH2</em>, and <em>GOT2</em>) did so in heterozygotes (GA). Of these, five loci showed genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Subsequent polygenic risk scoring depicted interactions highlighted by stratified GWAS. Further, most discovered loci showed significant effects on risk of esophageal cancer, a representative alcohol-related disease, and multiple other phenotypes. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.</span></p><p>No special software are required to open the files.</p><p>Funding provided by: Takeda Science Foundation<br>Crossref Funder Registry ID: https://ror.org/02y123g31<br>Award Number: </p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP16ck0106095</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ck0106266</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105001</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105002</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105003</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP16H06277</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP26253041</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP20K10463</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP19KK0418</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP20K10471</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP20km0105004</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP16ek0109070h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP18kk0205008h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP18kk0205001s0703</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ek0109283h0003</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP19ek0109348h0002</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP21gm4010006</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22km0405211</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22ek0410075</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22km0405217</p><p>Funding provided by: Japan Agency for Medical Research and Development<br>Crossref Funder Registry ID: https://ror.org/004rtk039<br>Award Number: JP22ek0109594</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 2022-A-20</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 17015018</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 221S0001</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 17015018</p><p>Funding provided by: Ministry of Education, Culture, Sports, Science and Technology<br>Crossref Funder Registry ID: https://ror.org/048rj2z13<br>Award Number: 221S0001</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 16H06277</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H04923</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP17K07255</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: JP17KT0125</p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H00476</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJMS2021</p><p>Funding provided by: Japan Science and Technology Agency<br>Crossref Funder Registry ID: https://ror.org/00097mb19<br>Award Number: JPMJMS2024</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 28-A-19</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 31-A-18</p><p>Funding provided by: Yamagiwa Yoshida Memorial UICC International Cancer Study Grants<br>Award Number: </p><p>Funding provided by: Kobayashi Foundation for Cancer Research<br>Crossref Funder Registry ID: https://ror.org/02qjq5342<br>Award Number: </p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 23-A-31</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 26-A-2</p><p>Funding provided by: National Cancer Centre Japan<br>Crossref Funder Registry ID: https://ror.org/0025ww868<br>Award Number: 29-A-4</p><p>Funding provided by: Princess Takamatsu Cancer Research Fund<br>Crossref Funder Registry ID: https://ror.org/00q3q5393<br>Award Number: </p><p>Funding provided by: Osake-no-Kagaku Foundation*<br>Crossref Funder Registry ID: <br>Award Number: </p><p>Funding provided by: Japan Society for the Promotion of Science<br>Crossref Funder Registry ID: https://ror.org/00hhkn466<br>Award Number: 22H03350</p><p class="MsoNormal"><strong><span>Summary statistics for drinking amount and ever drinking four types of analysis, ALDH2 rs671 GG only, GA only, All and interaction with GA.</span></strong></p> <p class="MsoNormal"><span>These summary statistics were obtained from the analyses described below.</span></p> <p class="MsoNormal"><strong><span>Study subjects and genotyping</span></strong></p> <p class="MsoNormal"><span>We performed a genome-wide meta-analysis based on the Japanese Consortium of Genetic Epidemiology studies (J-CGE) (Suzuki S et al. Cancer Sci 2021), the Nagahama Study (Funada S et al. J Urol 2018), and the BBJ Study (Hirata et al. J Epidemiol 2017<em>, </em>Nagai A et al. J Epidemiol 2017). The J-CGE consisted of the following Japanese population-based and hospital-based studies: the HERPACC Study (Hamajima N et al. Asian Pac J Cancer Prev 2001), the J-MICC Study (Hamajima N et al. Asian Pac J Cancer Prev 2007, Wakai et al. J Epidemiol 2011), the JPHC Study (Tsugane S et al. Jpn J Clin Oncol 2014), and the TMM Study (Hozawa A et al. J Epidemiol 2021). Individual study descriptions and an overview of the characteristics of the study populations are provided in the Supplementary Information and Supplementary Table 1.</span></p> <p class="MsoNormal"><strong><span>Quality control and genotype imputation</span></strong></p> <p class="MsoNormal"><span>Quality control for samples and SNPs was performed based on study-specific criteria (Supplemental Table 2). Genotype data in each study were imputed separately based on the 1000 Genomes Project reference panel (Phase 3, all ethnicities) (The 1000 Genomes Project Consortium Nature 2015). Phasing was performed with the use of SHAPEIT (v2) (Delaneau O et al. Nat Methos 2013) and Eagle (Loh PR et al. Nat Genet 2016), and imputation was performed using minimac3 (Das S et al. Nat Genet 2016), minimac4, or IMPUTE (v2) (Howie BN et al. PLoS Genet 2009). Information on the study-specific genotyping, imputation, quality control, and analysis tools is provided in Supplementary Table 2. After genotype imputation, further quality control was applied to each study. SNPs with an imputation quality of r<sup>2</sup> < 0.3 for minimac3 or minimac4, info < 0.4 for IMPUTE2 or an MAF of <0.01 were excluded. </span></p> <p class="MsoNormal"><strong><span>Association analysis of SNPs with daily alcohol intake and drinking status</span></strong></p> <p class="MsoNormal"><span>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 drinkers with the rs671 AA genotype was too small (Supplementary Table 3), 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 (Marchini J et al. Nat Genet 2007), or PLINK2 (Chang CC et al. GigaScience 2015).</span></p> <p class="MsoNormal"><span>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. In the interaction analysis for daily alcohol intake, the linear regression models were fit as the formula described in the Materials and Methods section. The GG genotype is coded as 0, and the GA genotype is coded as 1. Carriers of the AA genotype were excluded from the analysis. x<sub>snp</sub> is the imputed genotype coded as [0,2] for each SNP. c<sub>k</sub> is a covariate composed of age, age<sup>2</sup>, sex, the first 10 principal components, and 47 disease affection statuses (for the BBJ Study). The effect sizes of the interaction term, ß <sub>interaction</sub>, and its standard errors estimated in the association analysis were used in the subsequent meta-analysis. In the interaction analysis for drinking status, the logistic regression model was fit as the formula described in the Materials and Methods section. Other variables and procedures are as above. The association analysis, including the interaction term, was conducted using PLINK2 (Chang CC et al. GigaScience 2015). In this study, we employed rs671 genotypes directly extracted from SNP genotyping data, and no imputed data were used. With respect to concerns regarding genotype error, we further genotyped rs671 using TaqMan Assays with the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in all HERPACC samples in this study (<em>n</em> = 4,958). Results confirmed a 99.96% (<em>n</em> = 4,956) match of rs671 genotypes between the SNP microarray- and TaqMan-based data. The BBJ Study, the biggest data source in this study, also guaranteed a 100% concordance of rs671 genotyping between the SNP microarray and their in-house whole-genome sequencing (WGS) data (<em>n</em> = 2,798) in their previous study (Matoba N et al. Nat Hum Behav 2020). All the other cohorts, accounting for 20% of the data in this study, also used the <em>Illumina</em> genotyping platform (Supplementary Table 2), indicating that we can be assured of the accuracy of rs671 genotypes in these studies.</span></p> <p class="MsoNormal"><span>To identify studies with inflated GWAS significance, which can result from population stratification, we computed the intercept from LDSC (Bulik-Sullivan BK et al. Nat Genet 2015). 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. </span></p> <p class="MsoNormal"><strong><span>Meta-analysis</span></strong></p> <p class="MsoNormal"><span>The meta-analysis was performed with all Japanese subjects in the six cohorts (Supplementary Table 1). The results of association analyses for each SNP across the studies were combined with METAL software (Willer CJ et al. Bioinformatics 2010) 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 <5 × 10<sup>–8</sup>. <em>P</em>-values with <1.0×10<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 (Freedman ML et al. Nat Genet 2004).</span></p&gt