シシャ ト オンドク ニヨル サクブン ガクシュウ ノ コウカ : ダイガクインセイ オ タイショウ トシタ ジレイ ケンキュウ オ トオシテ

Multiple studies have showed that a learning method using copying is one of the effective learning methods to improve learner’s writing ability. But they were not long−term research or empirical studies. In this study, a graduate student had copied and read the famous author’s anthology for ten months. Then, I examined whether his writing ability was improved or not. As a result, I found that his writing ability was improved. And, from the results of the interview, I found that he had learned a variety of things in the process of copying and reading the famous author’s anthology. The results of this study show that it is effective to introduce the learning method, that is copying and reading the famous author’s anthology, to education

ガクセイ ノ ジュギョウ ジッセンリョク コウジョウ オ メザシタ Reask モデル ノ コウチク

筆者らの属する教員養成のコースにおいては，授業力向上のために模擬授業による授業実践の演習の他，マイクロティーチングを複数の教員による集団指導体制の合同ゼミで行っている。それらの演習やマイクロティーチングを通して，全体的な授業力の向上は見られるものの，教師主導の授業から脱しきれない状況がある。そこで，本研究では，児童の思考に寄り添った授業を行う力を育成することをめざすReaskモデル構築のための考察を行う。Reaskモデルとは児童の意見に対してその意見の背景や根拠，経験などを再度問うことであり，それにより児童の思考の深化を図ると共にリアリティのある学習を実現することができると考える。In this research the authors tried to construct the Reask model which aims to develop pre-service teachers’ teaching competencies, especially the competency to conduct thought of schoolchildren and deepen their learning. Reask in the model means re-asking on background, reason, and related experience of the children’s responses to and opinions about asking by a teacher. It is assumed that Reask would deepen children’s thinking and make their learning to the one with actuality. In special teacher training course which is one section of the graduate school of Naruto University of Education, the authors give lectures, exercises, and seminars to graduate students who aim to be a teacher. For their professional development, the authors give students microteaching lessons in which a team of members of the course teach, as well as exercises where students practice trial lessons. Protocols of students’ lesson showed that a series of microteaching lessons developed their teaching competencies as a whole, but that they tended to give a one-way lecture without opportunities for children to think deeply. The authors analyzed the protocols and elaborated the Reask model by considering expected re-asking in student’ classes

Body mass index and colorectal cancer risk : A Mendelian randomization study

Traditional observational studies have reported a positive association between higher body mass index (BMI) and the risk of colorectal cancer (CRC). However, evidence from other approaches to pursue the causal relationship between BMI and CRC is sparse. A two-sample Mendelian randomization (MR) study was undertaken using 68 single nucleotide polymorphisms (SNPs) from the Japanese genome-wide association study (GWAS) and 654 SNPs from the GWAS catalogue for BMI as sets of instrumental variables. For the analysis of SNP-BMI associations, we undertook a meta-analysis with 36 303 participants in the Japanese Consortium of Genetic Epidemiology studies (J-CGE), comprising normal populations. For the analysis of SNP-CRC associations, we utilized 7636 CRC cases and 37 141 controls from five studies in Japan, and undertook a meta-analysis. Mendelian randomization analysis of inverse-variance weighted method indicated that a one-unit (kg/m2) increase in genetically predicted BMI was associated with an odds ratio of 1.13 (95% confidence interval, 1.06-1.20; P value <.001) for CRC using the set of 68 SNPs, and an odds ratio of 1.07 (1.03-1.11, 0.001) for CRC using the set of 654 SNPs. Sensitivity analyses robustly showed increased odds ratios for CRC for every one-unit increase in genetically predicted BMI. Our MR analyses strongly support the evidence that higher BMI influences the risk of CRC. Although Asians are generally leaner than Europeans and North Americans, avoiding higher BMI seems to be important for the prevention of CRC in Asian populations

Body mass index and colorectal cancer risk: A Mendelian randomization study

Traditional observational studies have reported a positive association between higher body mass index (BMI) and the risk of colorectal cancer (CRC). However, evidence from other approaches to pursue the causal relationship between BMI and CRC is sparse. A two-sample Mendelian randomization (MR) study was undertaken using 68 single nucleotide polymorphisms (SNPs) from the Japanese genome-wide association study (GWAS) and 654 SNPs from the GWAS catalogue for BMI as sets of instrumental variables. For the analysis of SNP-BMI associations, we undertook a meta-analysis with 36 303 participants in the Japanese Consortium of Genetic Epidemiology studies (J-CGE), comprising normal populations. For the analysis of SNP-CRC associations, we utilized 7636 CRC cases and 37 141 controls from five studies in Japan, and undertook a meta-analysis. Mendelian randomization analysis of inverse-variance weighted method indicated that a one-unit (kg/m2) increase in genetically predicted BMI was associated with an odds ratio of 1.13 (95% confidence interval, 1.06-1.20; P value <.001) for CRC using the set of 68 SNPs, and an odds ratio of 1.07 (1.03-1.11, 0.001) for CRC using the set of 654 SNPs. Sensitivity analyses robustly showed increased odds ratios for CRC for every one-unit increase in genetically predicted BMI. Our MR analyses strongly support the evidence that higher BMI influences the risk of CRC. Although Asians are generally leaner than Europeans and North Americans, avoiding higher BMI seems to be important for the prevention of CRC in Asian populations

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

&lt;p&gt;&lt;span&gt;An East Asian-specific variant on &lt;em&gt;aldehyde&lt;/em&gt; &lt;em&gt;dehydrogenase&lt;/em&gt; &lt;em&gt;2&lt;/em&gt; (&lt;em&gt;ALDH2&lt;/em&gt; rs671, G&gt;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 (&lt;em&gt;GCKR&lt;/em&gt;, &lt;em&gt;KLB&lt;/em&gt;, and &lt;em&gt;ADH1B&lt;/em&gt;)&lt;/span&gt; &lt;span&gt;satisfied the genome-wide significance threshold in wild-type homozygotes (GG), whereas six loci (&lt;em&gt;GCKR&lt;/em&gt;, &lt;em&gt;ADH1B&lt;/em&gt;, &lt;em&gt;ALDH1B1&lt;/em&gt;, &lt;em&gt;ALDH1A1&lt;/em&gt;, &lt;em&gt;ALDH2&lt;/em&gt;, and &lt;em&gt;GOT2&lt;/em&gt;) 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.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;No special software are required to open the files.&lt;/p&gt;&lt;p&gt;Funding provided by: Takeda Science Foundation&lt;br&gt;Crossref Funder Registry ID: https://ror.org/02y123g31&lt;br&gt;Award Number: &lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP16ck0106095&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP19ck0106266&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP20km0105001&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP20km0105002&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP20km0105003&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP16H06277&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP26253041&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP20K10463&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP19KK0418&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP20K10471&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP20km0105004&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP16ek0109070h0003&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP18kk0205008h0003&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP18kk0205001s0703&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP19ek0109283h0003&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP19ek0109348h0002&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP21gm4010006&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP22km0405211&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP22ek0410075&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP22km0405217&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Agency for Medical Research and Development&lt;br&gt;Crossref Funder Registry ID: https://ror.org/004rtk039&lt;br&gt;Award Number: JP22ek0109594&lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 2022-A-20&lt;/p&gt;&lt;p&gt;Funding provided by: Ministry of Education, Culture, Sports, Science and Technology&lt;br&gt;Crossref Funder Registry ID: https://ror.org/048rj2z13&lt;br&gt;Award Number: 17015018&lt;/p&gt;&lt;p&gt;Funding provided by: Ministry of Education, Culture, Sports, Science and Technology&lt;br&gt;Crossref Funder Registry ID: https://ror.org/048rj2z13&lt;br&gt;Award Number: 221S0001&lt;/p&gt;&lt;p&gt;Funding provided by: Ministry of Education, Culture, Sports, Science and Technology&lt;br&gt;Crossref Funder Registry ID: https://ror.org/048rj2z13&lt;br&gt;Award Number: 17015018&lt;/p&gt;&lt;p&gt;Funding provided by: Ministry of Education, Culture, Sports, Science and Technology&lt;br&gt;Crossref Funder Registry ID: https://ror.org/048rj2z13&lt;br&gt;Award Number: 221S0001&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: 16H06277&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: 22H04923&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP17K07255&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: JP17KT0125&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: 22H00476&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Science and Technology Agency&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00097mb19&lt;br&gt;Award Number: JPMJMS2021&lt;/p&gt;&lt;p&gt;Funding provided by: Japan Science and Technology Agency&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00097mb19&lt;br&gt;Award Number: JPMJMS2024&lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 28-A-19&lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 31-A-18&lt;/p&gt;&lt;p&gt;Funding provided by: Yamagiwa Yoshida Memorial UICC International Cancer Study Grants&lt;br&gt;Award Number: &lt;/p&gt;&lt;p&gt;Funding provided by: Kobayashi Foundation for Cancer Research&lt;br&gt;Crossref Funder Registry ID: https://ror.org/02qjq5342&lt;br&gt;Award Number: &lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 23-A-31&lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 26-A-2&lt;/p&gt;&lt;p&gt;Funding provided by: National Cancer Centre Japan&lt;br&gt;Crossref Funder Registry ID: https://ror.org/0025ww868&lt;br&gt;Award Number: 29-A-4&lt;/p&gt;&lt;p&gt;Funding provided by: Princess Takamatsu Cancer Research Fund&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00q3q5393&lt;br&gt;Award Number: &lt;/p&gt;&lt;p&gt;Funding provided by: Osake-no-Kagaku Foundation*&lt;br&gt;Crossref Funder Registry ID: &lt;br&gt;Award Number: &lt;/p&gt;&lt;p&gt;Funding provided by: Japan Society for the Promotion of Science&lt;br&gt;Crossref Funder Registry ID: https://ror.org/00hhkn466&lt;br&gt;Award Number: 22H03350&lt;/p&gt;&lt;p class="MsoNormal"&gt;&lt;strong&gt;&lt;span&gt;Summary statistics for drinking amount and ever drinking four types of analysis, ALDH2 rs671 GG only, GA only, All and interaction with GA.&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;These summary statistics were obtained from the analyses described below.&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;strong&gt;&lt;span&gt;Study subjects and genotyping&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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&lt;em&gt;, &lt;/em&gt;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.&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;strong&gt;&lt;span&gt;Quality control and genotype imputation&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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&lt;sup&gt;2&lt;/sup&gt; &lt; 0.3 for minimac3 or minimac4, info &lt; 0.4 for IMPUTE2 or an MAF of &lt;0.01 were excluded. &lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;strong&gt;&lt;span&gt;Association analysis of SNPs with daily alcohol intake and drinking status&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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&lt;sub&gt;2&lt;/sub&gt; (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&lt;sup&gt;2&lt;/sup&gt;, 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&lt;sup&gt;2&lt;/sup&gt;, 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).&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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&lt;sub&gt;snp&lt;/sub&gt; is the imputed genotype coded as [0,2] for each SNP. c&lt;sub&gt;k&lt;/sub&gt; is a covariate composed of age, age&lt;sup&gt;2&lt;/sup&gt;, sex, the first 10 principal components, and 47 disease affection statuses (for the BBJ Study). The effect sizes of the interaction term, ß &lt;sub&gt;interaction&lt;/sub&gt;, 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 (&lt;em&gt;n&lt;/em&gt; = 4,958). Results confirmed a 99.96% (&lt;em&gt;n&lt;/em&gt; = 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 (&lt;em&gt;n&lt;/em&gt; = 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 &lt;em&gt;Illumina&lt;/em&gt; genotyping platform (Supplementary Table 2), indicating that we can be assured of the accuracy of rs671 genotypes in these studies.&lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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. &lt;/span&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;strong&gt;&lt;span&gt;Meta-analysis&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt; &lt;p class="MsoNormal"&gt;&lt;span&gt;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 &lt;em&gt;I&lt;/em&gt;&lt;sup&gt;2&lt;/sup&gt; and Cochran's &lt;em&gt;Q &lt;/em&gt;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 &lt;em&gt;P&lt;/em&gt; value &lt;5 × 10&lt;sup&gt;–8&lt;/sup&gt;. &lt;em&gt;P&lt;/em&gt;-values with &lt;1.0×10&lt;sup&gt;−300&lt;/sup&gt; 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).&lt;/span&gt;&lt;/p&gt