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Journal of Epidemiology logoLink to Journal of Epidemiology
. 2011 May 5;21(3):223–235. doi: 10.2188/jea.JE20100139

Profile of Participants and Genotype Distributions of 108 Polymorphisms in a Cross-Sectional Study of Associations of Genotypes With Lifestyle and Clinical Factors: A Project in the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study

Kenji Wakai 1, Nobuyuki Hamajima 1, Rieko Okada 1, Mariko Naito 1, Emi Morita 1, Asahi Hishida 1, Sayo Kawai 1, Kazuko Nishio 1, Guang Yin 1, Yatami Asai 2, Keitaro Matsuo 3, Satoyo Hosono 3, Hidemi Ito 3, Miki Watanabe 3, Takakazu Kawase 3, Takeshi Suzuki 4, Kazuo Tajima 5, Keitaro Tanaka 6, Yasuki Higaki 7, Megumi Hara 6, Takeshi Imaizumi 6, Naoto Taguchi 6, Kazuyo Nakamura 6, Hinako Nanri 6, Tatsuhiko Sakamoto 8, Mikako Horita 6, Koichi Shinchi 9, Yoshikuni Kita 10, Tanvir Chowdhury Turin 10, Nahid Rumana 10, Kenji Matsui 10, Katsuyuki Miura 10, Hirotsugu Ueshima 10,11, Naoyuki Takashima 11, Yasuyuki Nakamura 12, Sadao Suzuki 13, Ryosuke Ando 13, Akihiro Hosono 13, Nahomi Imaeda 13, Kiyoshi Shibata 13, Chiho Goto 13, Nami Hattori 13, Mitsuru Fukatsu 14, Tamaki Yamada 14, Shinkan Tokudome 13, Toshiro Takezaki 15, Hideshi Niimura 15, Kazuyo Hirasada 15, Akihiko Nakamura 15, Masaya Tatebo 15, Shin Ogawa 15, Noriko Tsunematsu 15, Shirabe Chiba 15, Haruo Mikami 16, Suminori Kono 17, Keizo Ohnaka 18, Ryoichi Takayanagi 19, Yoshiyuki Watanabe 20, Etsuko Ozaki 20, Masako Shigeta 20, Nagato Kuriyama 20, Aya Yoshikawa 20, Daisuke Matsui 20, Isao Watanabe 20, Kaoru Inoue 20, Kotaro Ozasa 20, Satoko Mitani 21, Kokichi Arisawa 22, Hirokazu Uemura 22, Mineyoshi Hiyoshi 22, Hidenobu Takami 22, Miwa Yamaguchi 22, Mariko Nakamoto 22, Hideo Takeda 22, Michiaki Kubo 23, Hideo Tanaka 3, for the J-MICC Study Group
PMCID: PMC3899413  PMID: 21467728

Abstract

Background

Most diseases are thought to arise from interactions between environmental factors and the host genotype. To detect gene–environment interactions in the development of lifestyle-related diseases, and especially cancer, the Japan Multi-institutional Collaborative Cohort (J-MICC) Study was launched in 2005.

Methods

We initiated a cross-sectional study to examine associations of genotypes with lifestyle and clinical factors, as assessed by questionnaires and medical examinations. The 4519 subjects were selected from among participants in the J-MICC Study in 10 areas throughout Japan. In total, 108 polymorphisms were chosen and genotyped using the Invader assay.

Results

The study group comprised 2124 men and 2395 women with a mean age of 55.8 ± 8.9 years (range, 35–69 years) at baseline. Among the 108 polymorphisms examined, 4 were not polymorphic in our study population. Among the remaining 104 polymorphisms, most variations were common (minor allele frequency ≥0.05 for 96 polymorphisms). The allele frequencies in this population were comparable with those in the HapMap-JPT data set for 45 Japanese from Tokyo. Only 5 of 88 polymorphisms showed allele-frequency differences greater than 0.1. Of the 108 polymorphisms, 32 showed a highly significant difference in minor allele frequency among the study areas (P < 0.001).

Conclusions

This comprehensive data collection on lifestyle and clinical factors will be useful for elucidating gene–environment interactions. In addition, it is likely to be an informative reference tool, as free access to genotype data for a large Japanese population is not readily available.

Key words: allele frequency, cross-sectional studies, gene–environment interactions, Japan Multi-institutional Collaborative Cohort Study, polymorphism

INTRODUCTION

Although the etiology of many diseases is not completely understood, most are likely to be caused by interactions between hazardous environmental factors and the host genome. Recent advances in genotyping techniques have allowed many epidemiologic studies to investigate gene–environment interactions in chronic diseases.1–4 Cohort and case–control studies focusing on such interactions are ongoing worldwide, and these investigations use DNA from established and new cohorts.5–8 Understanding gene–environment interactions requires long-term cohort studies to clarify the temporality of associations and to avoid information and selection biases that are inevitable in cross-sectional and case–control studies.9 For most multifactorial diseases, such cohort studies must be conducted on a large scale to ensure significant results.

The Japan Multi-institutional Collaborative Cohort (J-MICC) Study is a new cohort study that was launched in 2005 to examine gene–environment interactions in lifestyle-related diseases, especially cancers. It is supported by a research grant for Scientific Research on Special Priority Areas of Cancer from the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT).10,11 The J-MICC Study group is composed of 10 cohorts surveyed by 10 independent research teams.12,13

Although the long-term aim of this study is to elucidate gene–environment interactions in the whole cohort, some of its research objectives will be achieved by cross-sectional studies. In 2009, we started a cross-sectional study to examine correlations between lifestyle and medical factors, as assessed using questionnaires and medical examinations, and the distribution of possible related genotypes. Here we describe the recruitment and profile of the participants, including their genotype analysis, and selected demographic, lifestyle, and medical characteristics.

METHODS

Study participants, data collection, and blood sampling

The participants in this study completed questionnaires on lifestyle factors and diseases and donated blood samples at the time of the baseline survey for the J-MICC Study. The details of the J-MICC Study have been described elsewhere.10 The participants were enrolled in 9 study areas throughout Japan between 2005 and 2008, and in 1 area in 2004, under the supervision of an associate member of the J-MICC Study. The study participants were enrolled from the community by mailing invitation letters or distributing leaflets (3 areas), or by recruiting patients at their first visit to a cancer hospital (1 area) or at health checkups (6 areas). The response rates for the baseline survey were 7.0%, 36.5%, 25.9%, 58.4%, 60.1%, 37.6%, 14.0%, 24.0%, 19.7%, and 65.5% for the Chiba, Shizuoka, Okazaki, Aichi Cancer Center, Takashima, Kyoto, Tokushima, Fukuoka, Saga, and Amami areas, respectively. For cases in which the baseline survey is still ongoing in a cohort, the latest response rate (as of 30 September 2010 or later) was used. Anthropometry, blood pressure, and blood chemistry data obtained from health checkups were available in 8 of the study areas. The subjects for the cross-sectional study comprised 500 to 600 participants enrolled consecutively in each area of the J-MICC Study, except in 2 areas, where fewer participants had been recruited. The recruitment period for the present study, however, was arbitrarily defined by the researchers in each area after the enrollment.

Of the 5108 men and women initially selected, we excluded participants for whom we did not have sufficient DNA (n = 442), appropriate informed consent (n = 8), questionnaire data (n = 9), or local government registration of residence in the study area (n = 7), as well as anyone who had declined follow up (n = 2) or withdrew from the study (n = 1), and the 120 participants who were younger than 35 years or older than 69 years. Thus, our final study group comprised 4519 participants aged 35 to 69 years.

All the participants included in this analysis had provided written informed consent. The ethics committees of Nagoya University School of Medicine (the affiliation of the former principal investigator, Nobuyuki Hamajima) and the other participating institutions approved the protocol for the J-MICC Study.

Genotyping

We chose 107 single nucleotide polymorphisms (SNPs) and 1 insertion/deletion polymorphism for genotyping, based on their potential relevance to the lifestyle and medical factors described in the next section (“Lifestyle and clinical data”). Researchers from all participating cohorts proposed potentially relevant polymorphisms, and those selected for inclusion in the present study were determined through discussion among the members of the J-MICC Study Group.

In all study areas except Fukuoka, buffy coat fractions were prepared from blood samples and stored at −80°C at the central J-MICC Study office. DNA was extracted from all buffy coat fractions using a BioRobot M48 Workstation (Qiagen Group, Tokyo, Japan) at the central study office. For the samples from the Fukuoka area, DNA was extracted locally from samples of whole blood, using an automatic nucleic acid isolation system (NA-3000, Kurabo, Co., Ltd, Osaka, Japan). The buffy coat fractions or DNA samples were anonymized in a linkable manner14 and then sent to the central office.

The selected polymorphisms were genotyped using the multiplex polymerase chain reaction (PCR)-based Invader assay15 (Third Wave Technologies, Madison, WI, USA) at the Laboratory for Genotyping Development, Center for Genomic Medicine, RIKEN.

Lifestyle and clinical characteristics

The lifestyle factors considered were smoking and drinking habits, coffee consumption, sleep, and mental stress, while the clinical characteristics were height, weight, blood pressure, blood glucose, glycated hemoglobin (HbA1c), serum triglyceride, total and high-density lipoprotein (HDL) cholesterol, uric acid, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (γ-GT), C-reactive protein (CRP), creatinine, and bone mineral density. Ages at menarche and menopause were also ascertained.

We used a standard questionnaire in all study areas except the Fukuoka area, where some questions are slightly different from those of other areas. Furthermore, a validated food-frequency questionnaire was used for the dietary assessment.16–19 We were unable to directly control the quality of information from health examinations because most data were obtained at routine health checkups offered by other institutions. However, the J-MICC Study Group is now collecting information on participation in the Japan Medical Association’s quality control program for clinical laboratories and the instruction manuals used for measurement of blood pressure, height, and weight. For the current report, participants whose blood was drawn less than 3 hours after their last meal were excluded from the analysis of serum lipids and blood glucose.

Statistical analysis

We tabulated selected baseline characteristics by sex and 10-year age group or by sex and study area. In this analysis, body mass index (BMI; kg/m2) was calculated on the basis of self-reported height and body weight, as independent measurements were not available in some study areas. In the case of educational attainment, participants from the Fukuoka area were excluded from the analysis because the questionnaire used there had not included this item. Participants who consumed alcohol at least once a week were classified as drinkers. To compare characteristics among participating cohorts, we attempted to adjust for age by using the direct method (for proportions) or the general linear model (for means). The variations among study areas, however, were not significantly altered after adjusting for age. Thus, in this report, we present only crude figures by sex and study area. The difference in the minor allele frequency (MAF) among the cohorts was tested by the chi-square test for contingency tables. The MAF of the ABCC11 Arg180Gly (T/C) polymorphism by study area is not presented here because the inter-area variation in the distribution of this genotype will be reported in a separate article.

Genotypes with distributions that departed from the Hardy–Weinberg equilibrium were assessed using the exact test20 with the genhwi command of Stata version 8.0 (Stata Corp, College Station, TX, USA). Other statistical analyses were performed using Statistical Analysis System version 9.1 (SAS Institute Inc, Cary, NC, USA).21 To compare the allele frequencies of genotypes in our study with those in another Japanese population, we used data from HapMap, which is an open access database that includes allele frequencies for 45 Japanese in Tokyo (HapMap-JPT, http://www.ncbi.nlm.nih.gov/snp). Of the 108 polymorphisms of interest, we made comparisons for 88. The 20 polymorphisms excluded from our analysis showed no minor alleles in our study group (n = 4), were not represented (n = 15), or had invalid data (n = 1, 100% heterozygotes) in the HapMap-JPT data set.

RESULTS

Our analysis included 2124 men (47.0%) and 2395 women (53.0%) with a mean age ± standard deviation at baseline of 55.8 ± 8.9 years (range, 35–69 years). There were considerable differences in the age and sex distributions of different study areas (Table 1). In Fukuoka and Saga, the participants originally enrolled in the J-MICC Study were limited to adults aged 50 years or older and 40 years or older, respectively.

Table 1. Sex and age distribution of study participants by study area.

Study area Men Women
Age (years) Total Age (years) Total
35–39 40–49 50–59 60–69 35–39 40–49 50–59 60–69
n % n % n % n % n % n % n % n % n % n %
Chiba 4 2.7 22 14.8 56 37.6 67 45.0 149 100.0 30 8.4 138 38.7 118 33.1 71 19.9 357 100.0
Shizuoka 21 5.0 122 29.3 175 42.1 98 23.6 416 100.0 16 10.1 35 22.0 70 44.0 38 23.9 159 100.0
Okazaki 13 4.8 29 10.6 66 24.2 165 60.4 273 100.0 12 4.7 45 17.6 85 33.3 113 44.3 255 100.0
Aichi Cancer Center 12 4.4 32 11.6 115 41.8 116 42.2 275 100.0 33 10.9 88 29.0 102 33.7 80 26.4 303 100.0
Takashima 7 4.2 18 10.7 45 26.8 98 58.3 168 100.0 27 7.2 59 15.8 102 27.3 186 49.7 374 100.0
Kyoto 37 30.3 31 25.4 48 39.3 6 4.9 122 100.0 9 23.7 19 50.0 9 23.7 1 2.6 38 100.0
Tokushima 8 11.0 21 28.8 24 32.9 20 27.4 73 100.0 1 4.5 9 40.9 10 45.5 2 9.1 22 100.0
Fukuoka 0 0.0 0 0.0 60 31.9 128 68.1 188 100.0 0 0.0 0 0.0 96 37.5 160 62.5 256 100.0
Saga 0 0.0 31 12.7 82 33.5 132 53.9 245 100.0 0 0.0 64 19.3 127 38.4 140 42.3 331 100.0
Amami 1 0.5 53 24.7 82 38.1 79 36.7 215 100.0 1 0.3 77 25.7 135 45.0 87 29.0 300 100.0
Total 103 4.8 359 16.9 753 35.5 909 42.8 2124 100.0 129 5.4 534 22.3 854 35.7 878 36.7 2395 100.0
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Table 2 summarizes selected demographic, lifestyle, and medical characteristics of the participants by sex and age. Within our sample, 29.1% of men and 7.1% of women were current smokers. More than two thirds (71.4%) of men drank alcoholic beverages at least once a week, as did 27.7% of the women. Table 3 presents data on selected lifestyle and medical variables of the participants by sex and study area. Considerable variations were found among the participating cohorts.

Table 2. Selected demographic, lifestyle, and medical characteristics of participants by sex and age.

  Men Women
  Age (years) Total Age (years) Total
  35–39 40–49 50–59 60–69 35–39 40–49 50–59 60–69
n 103 359 753 909 2124 129 534 854 878 2395
Educational attainment (%)a
 Elementary/junior
​ high school		 1.0 3.1 10.0 20.9 12.6 2.3 2.1 9.8 31.6 14.7
 High school 42.7 37.8 41.9 43.7 41.9 41.9 38.4 50.7 46.4 45.6
 Vocational school 14.6 9.5 6.8 3.2 6.3 15.5 13.9 12.7 10.6 12.5
 Junior college 3.9 4.8 3.9 2.2 3.4 24.8 26.5 16.1 7.4 16.3
 University 33.0 39.5 34.8 27.6 32.7 15.5 17.7 10.2 3.8 10.2
 Postgraduate school 4.9 5.0 2.2 1.7 2.6 0.0 1.5 0.5 0.0 0.6
 Others 0.0 0.3 0.4 0.7 0.5 0.0 0.0 0.0 0.1 0.0
Current smokers (%) 39.8 32.3 34.8 21.9 29.1 11.6 10.3 7.4 4.1 7.1
Ex-smokers (%) 25.2 35.7 41.8 48.6 42.9 5.4 7.9 4.8 2.8 4.8
Current drinkers (%)b 60.2 70.8 75.1 69.9 71.4 32.6 34.6 27.5 22.8 27.7
Exercise ≥1/month (%) 75.7 81.6 79.7 86.8 82.9 62.0 70.4 74.9 82.5 76.0
Body mass
​index ≥25.0 (%)		 37.9 32.2 32.6 24.9 29.5 9.5 16.5 20.0 21.8 19.3
History of disease (%)                    
 Diabetes 1.9 4.5 8.4 13.9 9.7 0.0 0.6 3.4 7.1 3.8
 Hypertension 3.9 8.9 23.2 36.2 25.2 0.0 4.3 17.1 30.5 17.9
 Coronary heart
​ disease		 0.0 1.1 2.1 7.3 4.0 0.8 0.6 2.8 5.3 3.0
 Stroke 0.0 1.1 2.7 3.9 2.8 2.3 0.9 1.6 2.9 2.0
 Cancer 6.1 2.0 7.7 12.6 8.6 2.8 5.0 9.3 6.4 6.9
Blood pressure and blood chemistryc
 Systolic blood
​ pressure (mm Hg)		 120.4 ± 14.3 121.1 ± 15.4 129.7 ± 17.9 135.4 ± 19.3 130.1 ± 18.8 109.6 ± 11.4 116.8 ± 18.3 125.0 ± 18.9 133.4 ± 19.0 126.5 ± 19.9
 Diastolic blood
​ pressure (mm Hg)		 73.6 ± 10.3 77.6 ± 11.7 81.9 ± 12.6 82.1 ± 11.1 80.8 ± 11.9 65.4 ± 7.4 72.0 ± 11.9 76.7 ± 11.6 79.0 ± 10.8 76.4 ± 11.7
 Total cholesterol
​ (mg/dl)		 191.3 ± 28.9 205.3 ± 30.2 206.6 ± 32.5 205.2 ± 33.1 205.2 ± 32.3 185.6 ± 25.2 202.4 ± 31.0 223.3 ± 34.2 223.1 ± 33.5 217.9 ± 34.6
 HDL-cholesterol
​ (mg/dl)		 56.2 ± 16.0 57.6 ± 15.4 59.1 ± 15.8 59.7 ± 16.2 58.9 ± 15.9 73.1 ± 15.7 68.7 ± 15.3 68.9 ± 15.0 66.2 ± 16.0 68.0 ± 15.5
 Triglyceride (mg/dl) 128.6 ± 74.9 146.5 ± 96.5 135.1 ± 93.0 132.0 ± 92.9 135.9 ± 93.0 64.8 ± 28.4 88.0 ± 61.2 104.9 ± 64.2 113.7 ± 69.2 103.4 ± 65.7
 Blood glucose (mg/dl) 98.0 ± 27.2 98.3 ± 15.7 103.2 ± 17.6 103.9 ± 23.3 102.1 ± 20.3 86.8 ± 7.1 92.4 ± 19.7 95.9 ± 16.8 97.1 ± 15.8 95.1 ± 16.9
 HbA1c (%) 4.96 ± 0.32 5.12 ± 0.48 5.32 ± 0.86 5.30 ± 0.75 5.27 ± 0.74 4.86 ± 0.27 4.96 ± 0.36 5.17 ± 0.64 5.26 ± 0.57 5.17 ± 0.57
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Plus-minus values are means ± SDs.

aParticipants in Fukuoka area were excluded from the analysis because they were not asked about educational attainment in the questionnaire.

bIndividuals who drank alcoholic beverages ≥1 day/week.

cNot available in some study areas, as shown in Table 3.

Table 3. Selected lifestyle and medical characteristics of participants by sex and study area.

  n Study area
  Total Chiba Shizuoka Okazaki ACC Takashima Kyoto Tokushima Fukuoka Saga Amami
Men                        
 Current smokers (%) 2123 29.1 23.5 23.6 25.6 31.0 35.7 41.8 31.5 33.0 34.3 23.3
 Ex-smokers (%) 2123 42.9 42.3 48.3 50.2 44.2 33.3 33.6 38.4 39.4 42.9 39.1
 Current drinkers (%)a 2121 71.4 78.5 73.4 62.6 66.9 73.8 68.0 61.6 70.2 71.4 84.1
 Exercise ≥1/month (%) 2109 82.9 87.1 88.7 81.0 83.5 60.1 81.0 94.5 89.9 85.3 75.9
 Body mass index ≥25.0 (%) 2114 29.5 29.1 25.2 26.4 22.6 27.0 23.8 41.1 31.4 27.7 52.8
 History of hypertension (%) 2052 25.2 28.2 14.7 29.5 24.0 28.7 9.9 21.9 44.4 28.6 32.1
 
 Systolic blood pressure (mm Hg) 1698 130.1 ± 18.8 NA 121.9 ± 14.8 128.7 ± 15.2 NA 133.0 ± 19.5 122.8 ± 16.0 119.8 ± 17.6 144.0 ± 19.6 138.3 ± 19.3 132.0 ± 17.7
 Diastolic blood pressure
​ (mm Hg)		 1698 80.8 ± 11.9 NA 76.5 ± 10.5 81.4 ± 9.1 NA 81.2 ± 11.2 74.3 ± 11.9 73.7 ± 12.8 88.0 ± 11.1 86.0 ± 12.8 81.9 ± 11.1
 Total cholesterol (mg/dl) 1296 205.2 ± 32.3 NA 201.3 ± 29.3 205.9 ± 32.0 NA 208.3 ± 35.6 —b 209.0 ± 31.4 209.9 ± 31.5 203.6 ± 33.6 208.0 ± 35.7
 HDL-cholesterol (mg/dl) 1378 58.9 ± 15.9 NA 58.3 ± 15.4 64.1 ± 17.5 NA 59.4 ± 17.0 61.4 ± 17.6 52.5 ± 11.6 54.5 ± 16.1 55.3 ± 14.2 57.7 ± 13.4
 Triglyceride (mg/dl) 1377 135.9 ± 93.0 NA 122.8 ± 68.6 120.8 ± 86.5 NA 116.9 ± 60.0 127.9 ± 85.7 126.7 ± 64.2 181.5 ± 131.9 164.9 ± 93.6 165.7 ± 130.8
 Blood glucose (mg/dl) 1175 102.1 ± 20.3 NA 101.1 ± 14.3 100.0 ± 17.0 NA 97.9 ± 16.3 98.7 ± 25.0 104.2 ± 26.4 NA NA 110.3 ± 28.8
 HbA1c (%) 1354 5.27 ± 0.74 NA 5.27 ± 0.56 5.36 ± 0.64 NA 5.24 ± 0.79 NA 5.17 ± 0.52 5.17 ± 0.82 5.28 ± 1.04 5.31 ± 0.42
Women                        
 Current smokers (%) 2390 7.1 7.6 5.0 6.3 12.4 5.6 15.8 0.0 9.0 5.4 4.4
 Ex-smokers (%) 2390 4.8 6.7 4.4 5.9 8.4 1.3 7.9 4.6 4.7 5.7 1.0
 Current drinkers (%)a 2383 27.7 36.1 27.2 22.4 31.7 24.6 34.2 22.7 25.0 28.2 23.3
 Exercise ≥1/month (%) 2381 76.0 82.8 81.8 83.9 70.9 55.6 44.7 81.8 88.3 83.4 73.1
 Body mass index ≥25.0 (%) 2359 19.3 12.8 15.5 17.3 14.0 21.9 15.8 27.3 19.5 16.5 36.2
 History of hypertension (%) 2289 17.9 10.4 15.8 19.3 12.9 18.5 7.9 18.2 37.9 17.6 22.0
 
 Systolic blood pressure (mm Hg) 1732 126.5 ± 19.9 NA 115.7 ± 16.8 122.7 ± 15.6 NA 124.6 ± 19.5 120.4 ± 19.5 111.5 ± 11.7 137.5 ± 21.9 129.5 ± 19.5 126.8 ± 19.0
 Diastolic blood pressure
​ (mm Hg)		 1732 76.4 ± 11.7 NA 70.3 ± 10.5 76.7 ± 9.5 NA 73.6 ± 11.4 71.8 ± 11.1 68.2 ± 10.3 84.0 ± 11.3 77.9 ± 12.2 76.0 ± 10.4
 Total cholesterol (mg/dl) 1318 217.9 ± 34.6 NA 206.4 ± 31.9 213.5 ± 34.1 NA 222.6 ± 36.5 NA 210.0 ± 35.7 230.1 ± 31.4 220.3 ± 33.5 218.0 ± 33.7
 HDL-cholesterol (mg/dl) 1347 68.0 ± 15.5 NA 72.3 ± 16.1 75.0 ± 16.9 NA 67.0 ± 15.0 69.3 ± 13.7 65.5 ± 12.0 66.1 ± 16.6 64.3 ± 14.1 63.4 ± 12.6
 Triglyceride (mg/dl) 1347 103.4 ± 65.7 NA 80.0 ± 40.9 97.3 ± 60.9 NA 96.8 ± 54.9 68.6 ± 27.8 84.3 ± 41.2 128.0 ± 66.0 129.2 ± 83.4 109.4 ± 74.2
 Blood glucose (mg/dl) 1072 95.1 ± 16.9 NA 91.9 ± 8.3 94.7 ± 13.7 NA 91.0 ± 10.2 89.6 ± 7.1 101.9 ± 31.9 NA NA 101.8 ± 24.3
 HbA1c (%) 1396 5.17 ± 0.57 NA 5.15 ± 0.35 5.32 ± 0.57 NA 5.11 ± 0.54 NA —b 5.16 ± 0.65 5.10 ± 0.59 —b
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Abbreviations: ACC, Aichi Cancer Center; HDL, high-density lipoprotein; NA, not available.

Plus-minus values are means ± SDs.

aIndividuals who drank alcoholic beverages ≥1 day/week.

bData available for fewer than 20 participants.

The genotype distributions and allele frequencies of the analyzed genetic polymorphisms are summarized in Table 4. The call rate ranged from 99.40% to 99.98%. Of the 108 polymorphisms, the 4 SNPs for which we found no different alleles were APOA1 Arg184Pro (G/C), ESR1 IVS1-351A/G (Xba I), LCAT/SLC12A4 Ser232Thr (T/A), and SCARB1 Val135Ile (G/A). For the remaining 104 polymorphisms, the MAF varied from 0.016 (PTGS2(COX2) C-163G) to 0.492 (CETP Ile405Val (A/G)), and most of the variations were common (MAF ≥0.05 for 96 polymorphisms).

Table 4. Genotype distributions and allele frequencies of 108 selected genetic polymorphisms (107 SNPs and 1 insertion/deletion polymorphism).

Gene Polymorphism rs number Genotypea na Frequency (proportion) P for
HWE		 MAF
Observed Expectedb
AA Aa aa AA Aa aa XX AA Aa aa AA Aa aa
ABCA1 C-565T rs2422493 CC CT TT 1634 2137 746 2 0.362 0.473 0.165 0.358 0.481 0.161 0.29 0.402
ABCA1 G-191C rs1800976 GG GC CC 1628 2144 746 1 0.360 0.475 0.165 0.357 0.481 0.162 0.37 0.402
ABCA1 G-17C rs2740483 GG GC CC 2211 1898 409 1 0.489 0.420 0.091 0.489 0.420 0.090 0.94 0.301
ABCA1 Val825Ile (G/A) rs2066715 GG GA AA 1877 2064 577 1 0.415 0.457 0.128 0.415 0.459 0.127 0.80 0.356
ABCA1 Val771Met (G/A) rs2066718 GG GA AA 3945 550 23 1 0.873 0.122 0.005 0.872 0.123 0.004 0.40 0.066
ABCA1 Arg1587Lys (G/A) rs2230808 GG GA AA 1673 2154 689 3 0.370 0.477 0.153 0.371 0.476 0.153 0.95 0.391
ABCC11 Arg180Gly (T/C) rs17822931 TT TC CC 3368 1051 95 5 0.746 0.233 0.021 0.744 0.237 0.019 0.23 0.137
ACE Insertion/Deletion (I/D) rs1799752 I/I I/D D/D 1854 2021 634 10 0.411 0.448 0.141 0.404 0.463 0.133 0.029 0.365
ADD1 Trp460Gly (T/G) rs4961 TT TG GG 1399 2163 956 1 0.310 0.479 0.212 0.301 0.495 0.203 0.026 0.451
ADH1B His47Arg (A/G) rs1229984 AA AG GG 2607 1659 250 3 0.577 0.367 0.055 0.579 0.364 0.057 0.54 0.239
ADH1C Arg272Gln (C/T) rs1693482 CC CT TT 3882 591 43 3 0.860 0.131 0.010 0.856 0.139 0.006 0.0005 0.075
ADIPOQ C-11377G rs266729 CC CG GG 2553 1663 301 2 0.565 0.368 0.067 0.561 0.376 0.063 0.18 0.251
ADIPOQ Gly15Gly (T/G) rs2241766 TT TG GG 2314 1789 415 1 0.512 0.396 0.092 0.504 0.412 0.084 0.011 0.290
ADIPOQ G276T rs1501299 GG GT TT 2498 1674 346 1 0.553 0.371 0.077 0.545 0.387 0.069 0.006 0.262
ADIPOQ IVS-3971A/G rs822396 AA AG GG 3984 517 16 2 0.882 0.114 0.004 0.882 0.114 0.004 1.00 0.061
ADIPOR1 G-8503A rs6666089 GG GA AA 4234 272 7 6 0.938 0.060 0.002 0.938 0.061 0.001 0.22 0.032
ADIPOR1 C5843T rs1342387 CC CT TT 1230 2206 1073 10 0.273 0.489 0.238 0.268 0.499 0.233 0.17 0.483
ADIPOR1 C10224G rs7539542 CC CG GG 2788 1505 225 1 0.617 0.333 0.050 0.614 0.339 0.047 0.24 0.216
ADRB2 Gln27Glu (C/G) rs1042714 CC CG GG 3943 551 20 5 0.874 0.122 0.004 0.873 0.122 0.004 0.81 0.065
ADRB3 Trp64Arg (T/C) rs4994 TT TC CC 2932 1397 182 8 0.650 0.310 0.040 0.648 0.314 0.038 0.34 0.195
AGT Thr174Met (C/T) rs4762 CC CT TT 3615 848 52 4 0.801 0.188 0.012 0.800 0.189 0.011 0.75 0.105
AGT Thr235Met (C/T) rs699 CC CT TT 2989 1372 157 1 0.662 0.304 0.035 0.662 0.304 0.035 1.00 0.187
AGTR1(ATR1) A1166C (at 3′UTR) rs5186 AA AC CC 3777 695 45 2 0.836 0.154 0.010 0.834 0.159 0.008 0.048 0.087
ALDH2 Glu487Lys (G/A) rs671 GG GA AA 2544 1649 321 5 0.564 0.365 0.071 0.557 0.379 0.064 0.018 0.254
APOA1 Ala61Thr (C/T) rs12718465 CC CT TT 4036 463 18 2 0.894 0.103 0.004 0.893 0.104 0.003 0.25 0.055
APOA1 Arg184Pro (G/C) rs5078 GG GC CC 4512 0 0 7 1.000 0.000 0.000 1.000 0.000 0.000 1.00 0.000
APOA5 T-1131C rs662799 TT TC CC 1955 2001 556 7 0.433 0.443 0.123 0.429 0.452 0.119 0.21 0.345
APOA5 Gly185Cys (G553T) rs2075291 GG GT TT 3920 559 32 8 0.869 0.124 0.007 0.867 0.129 0.005 0.020 0.069
APOE T-203G rs405509 TT TG GG 2213 1847 451 8 0.491 0.409 0.100 0.483 0.424 0.093 0.025 0.305
APOE Arg158Cys (C/T) (at exon2) rs7412 CC CT TT 4140 369 9 1 0.916 0.082 0.002 0.916 0.082 0.002 0.72 0.043
APOE Cys112Arg (T/C) (at exon4) rs429358 TT TC CC 3669 782 67 1 0.812 0.173 0.015 0.808 0.182 0.010 0.001 0.101
ARNTL(BMAL1) A/G rs7950226 AA AG GG 1638 2102 773 6 0.363 0.466 0.171 0.355 0.482 0.163 0.028 0.404
ART4(DOK1) Leu208Leu (G/A) rs3088189 GG GA AA 3595 875 47 2 0.796 0.194 0.010 0.797 0.192 0.012 0.48 0.107
ART4(DOK1) Asp265Asn (G/A) rs11276 GG GA AA 3597 872 48 2 0.796 0.193 0.011 0.797 0.191 0.011 0.59 0.107
BHMT Arg239Gln (G742A) rs3733890 GG GA AA 2765 1511 241 2 0.612 0.335 0.053 0.607 0.344 0.049 0.069 0.221
CBS Tyr233Tyr (C699T) rs234706 CC CT TT 4302 212 2 3 0.953 0.047 0.000 0.953 0.047 0.001 1.00 0.024
CD14 T-260C/T-159C rs2569190 TT TC CC 1304 2256 958 1 0.289 0.499 0.212 0.290 0.497 0.213 0.79 0.462
CDKAL1 G/C rs7754840 GG GC CC 1581 2195 740 3 0.350 0.486 0.164 0.352 0.483 0.166 0.64 0.407
CDKN2A/B T/C rs10811661 TT TC CC 1379 2205 934 1 0.305 0.488 0.207 0.302 0.495 0.203 0.34 0.451
CETP A-629C rs1800775 AA AC CC 1384 2242 891 2 0.306 0.496 0.197 0.308 0.494 0.198 0.76 0.445
CETP Ile405Val (A/G) rs5882 AA AG GG 1236 2116 1166 1 0.274 0.468 0.258 0.258 0.500 0.242 <0.0001 0.492
CETP TaqIB (C/T) rs708272 CC CT TT 1603 2174 741 1 0.355 0.481 0.164 0.354 0.482 0.164 0.93 0.405
CETP G/T rs3764261 GG GT TT 2707 1512 298 2 0.599 0.335 0.066 0.588 0.358 0.054 <0.0001 0.233
CHRNB2 G-42A rs2072658 GG GA AA 2837 1518 161 3 0.628 0.336 0.036 0.634 0.324 0.042 0.015 0.204
CHRNB2 C/T (at 3′UTR) rs2072660 CC CT TT 2601 1641 275 2 0.576 0.363 0.061 0.574 0.367 0.059 0.44 0.243
COMT Val158Met (G/A) rs4680 GG GA AA 2006 1983 528 2 0.444 0.439 0.117 0.440 0.446 0.113 0.27 0.336
CYP1A1 Ile462Val (A/G) rs1048943 AA AG GG 2630 1620 268 1 0.582 0.359 0.059 0.580 0.363 0.057 0.39 0.239
CYP1A2 A734C rs762551 AA AC CC 1878 2043 592 6 0.416 0.453 0.131 0.413 0.459 0.128 0.33 0.358
CYP11B2 T-344C rs1799998 TT TC CC 2031 1976 507 5 0.450 0.438 0.112 0.447 0.443 0.110 0.42 0.331
CYP17A1 T-34C rs743572 TT TC CC 1281 2257 977 4 0.284 0.500 0.216 0.285 0.498 0.217 0.79 0.466
ESR1 IVS1-351A/G (Xba I) rs11155816 AA AG GG 4515 0 0 4 1.000 0.000 0.000 1.000 0.000 0.000 1.00 0.000
ESR1 IVS1-397T/C (Pvu II) rs2234693 TT TC CC 1508 2172 834 5 0.334 0.481 0.185 0.330 0.489 0.181 0.30 0.425
ESR2 Val328Val (G1082A) (Rsa I) rs1256049 GG GA AA 2320 1823 373 3 0.514 0.404 0.083 0.512 0.407 0.081 0.58 0.284
FTO T/A rs9939609 TT TA AA 2881 1454 183 1 0.638 0.322 0.041 0.638 0.322 0.041 1.00 0.201
GCK G-30A rs1799884 GG GA AA 2989 1348 181 1 0.662 0.298 0.040 0.657 0.307 0.036 0.065 0.189
GCKR A/G rs780094 AA AG GG 1379 2221 917 2 0.305 0.492 0.203 0.304 0.495 0.201 0.67 0.449
GCKR Leu446Pro (T/C) rs1260326 TT TC CC 1402 2214 902 1 0.310 0.490 0.200 0.308 0.494 0.198 0.61 0.445
GNAS1 T393C rs7121 TT TC CC 1466 2233 817 3 0.325 0.494 0.181 0.327 0.490 0.183 0.52 0.428
IGF2BP2 G/T (at intron) rs4402960 GG GT TT 2215 1866 436 2 0.490 0.413 0.097 0.486 0.422 0.092 0.14 0.303
IL-1B T-31C rs1143627 TT TC CC 1331 2196 990 2 0.295 0.486 0.219 0.289 0.497 0.214 0.14 0.462
IL-2 T-330G rs2069762 TT TG GG 2033 1986 494 6 0.450 0.440 0.109 0.450 0.442 0.109 0.79 0.329
IL-4 T-33C rs2070874 TT TC CC 1997 1989 531 2 0.442 0.440 0.118 0.439 0.447 0.114 0.30 0.338
IL-6 C-634G rs1800796 CC CG GG 2611 1607 298 3 0.578 0.356 0.066 0.572 0.369 0.059 0.019 0.244
IL-8 T-251A rs4073 TT TA AA 2077 1952 463 27 0.462 0.435 0.103 0.462 0.435 0.103 0.89 0.320
IL-10 T-819C rs1800871 TT TC CC 1942 2009 557 11 0.431 0.446 0.124 0.427 0.453 0.120 0.29 0.346
IL-13 C-1111T rs1800925 CC CT TT 3009 1359 147 4 0.666 0.301 0.033 0.667 0.299 0.034 0.69 0.183
KCNJ11 Glu23Lys (C/T) rs5219 CC CT TT 1822 2081 615 1 0.403 0.461 0.136 0.401 0.464 0.134 0.59 0.366
LCAT/SLC12A4 Ser232Thr (T/A) rs4986970 TT TA AA 4516 0 0 3 1.000 0.000 0.000 1.000 0.000 0.000 1.00 0.000
LIPC T-514C rs1800588 TT TC CC 1178 2255 1085 1 0.261 0.499 0.240 0.260 0.500 0.240 0.93 0.490
LIPC Val95Met (G/A) rs6078 GG GA AA 2659 1606 252 2 0.589 0.356 0.056 0.587 0.358 0.055 0.65 0.234
MPO G-463A rs2333227 GG GA AA 3612 852 52 3 0.800 0.189 0.012 0.800 0.189 0.011 0.81 0.106
MTHFD1 Arg134Lys (C401T) rs1950902 CC CT TT 2773 1517 228 1 0.614 0.336 0.050 0.611 0.341 0.048 0.28 0.218
MTHFD1 Arg653Gln (G1958A) rs2236225 GG GA AA 2340 1790 384 5 0.518 0.397 0.085 0.514 0.406 0.080 0.12 0.283
MTHFR Ala222Val (C677T) rs1801133 CC CT TT 1763 2060 694 2 0.390 0.456 0.154 0.382 0.472 0.146 0.023 0.382
MTHFR Glu429Ala (A1298C) rs1801131 AA AC CC 3023 1323 169 4 0.670 0.293 0.037 0.666 0.300 0.034 0.11 0.184
MTR Asp919Gly (A/G) rs1805087 AA AG GG 2883 1446 185 5 0.639 0.320 0.041 0.638 0.321 0.040 0.82 0.201
MTRR Ile22Met (A66G) rs1801394 AA AG GG 2155 1925 436 3 0.477 0.426 0.097 0.477 0.428 0.096 0.83 0.310
NOS3 T-786C rs2070744 TT TC CC 3602 863 48 6 0.798 0.191 0.011 0.799 0.190 0.011 0.70 0.106
PPARD T-842C (at exon3) rs2267668 TT TC CC 2922 1421 172 4 0.647 0.315 0.038 0.647 0.315 0.038 1.00 0.195
PPARD T-48444C (at exon3) rs6902123 TT TC CC 4343 173 2 1 0.961 0.038 0.000 0.961 0.038 0.000 0.69 0.020
PPARD Asn163Asn (A65G) (at exon7) rs2076167 AA AG GG 2780 1530 204 5 0.616 0.339 0.045 0.617 0.337 0.046 0.76 0.215
PPARG Pro12Ala (C/G) rs1801282 CC CG GG 4236 274 5 4 0.938 0.061 0.001 0.938 0.061 0.001 0.80 0.031
PPARG His477His (C161T) rs3856806 CC CT TT 3231 1198 86 4 0.716 0.265 0.019 0.720 0.257 0.023 0.043 0.152
PPARGC1A Thr394Thr (C/T) rs2970847 CC CT TT 2755 1539 221 4 0.610 0.341 0.049 0.609 0.343 0.048 0.76 0.219
PPARGC1A Gly482Ser (G/A) rs8192678 GG GA AA 1317 2247 952 3 0.292 0.498 0.211 0.292 0.497 0.211 0.93 0.460
PRKAA2 IVS4+961T/C rs1418442 TT TC CC 2677 1581 251 10 0.594 0.351 0.056 0.591 0.355 0.053 0.38 0.231
PRKAA2 IVS7+81T/C rs932447 TT TC CC 2679 1585 252 3 0.593 0.351 0.056 0.591 0.356 0.053 0.38 0.231
PRKAA2 A/T (at 3′UTR) rs1342382 AA AT TT 2739 1556 218 6 0.607 0.345 0.048 0.607 0.344 0.049 0.90 0.221
PTGS2(COX2) G-765C rs20417 GG GC CC 4260 247 6 6 0.944 0.055 0.001 0.943 0.056 0.001 0.27 0.029
PTGS2(COX2) C-163G rs5270 CC CG GG 4379 136 3 1 0.969 0.030 0.001 0.969 0.031 0.000 0.10 0.016
PTPN11 G33861A rs2301756 GG GA AA 2926 1418 174 1 0.648 0.314 0.039 0.647 0.314 0.038 0.89 0.195
RETN C-420G rs1862513 CC CG GG 1956 1977 585 1 0.433 0.438 0.129 0.425 0.454 0.121 0.015 0.348
SCARB1 Val135Ile (G/A) rs5891 GG GA AA 4518 0 0 1 1.000 0.000 0.000 1.000 0.000 0.000 1.00 0.000
SCARB1 Ala350Ala (C1119T) rs5888 CC CT TT 2678 1619 221 1 0.593 0.358 0.049 0.596 0.352 0.052 0.25 0.228
SCARB1 G/A (at intron) rs3782287 GG GA AA 2676 1574 268 1 0.592 0.348 0.059 0.588 0.358 0.055 0.074 0.234
SERPINC1 C/G rs677 CC CG GG 2762 1527 227 3 0.612 0.338 0.050 0.609 0.342 0.048 0.41 0.219
SHMT1 Leu435Phe (C1420T) rs1979277 CC CT TT 3755 722 41 1 0.831 0.160 0.009 0.830 0.162 0.008 0.36 0.089
SLC19A(RFC1) His27Arg (A80G) rs1051266 AA AG GG 1536 2187 793 3 0.340 0.484 0.176 0.339 0.486 0.175 0.76 0.418
SLC30A8 Arg325Trp (C/T) rs13266634 CC CT TT 1543 1992 976 8 0.342 0.442 0.216 0.317 0.492 0.191 <0.0001 0.437
SRD5A2 Leu89Val (C/G) rs523349 CC CG GG 1334 2286 896 3 0.295 0.506 0.198 0.301 0.495 0.204 0.15 0.452
TAS2R38 Pro49Ala (C/G) rs713598 CC CG GG 1399 2219 898 3 0.310 0.491 0.199 0.309 0.494 0.198 0.74 0.445
TAS2R38 Ala262Val (C/T) rs1726866 CC CT TT 1401 2218 899 1 0.310 0.491 0.199 0.309 0.494 0.198 0.70 0.444
TAS2R38 Val296Ile (C/T) rs10246939 CC CT TT 1401 2218 898 2 0.310 0.491 0.199 0.309 0.494 0.197 0.72 0.444
TCF7L2 C/T (at intron) rs7903146 CC CT TT 4174 333 11 1 0.924 0.074 0.002 0.923 0.075 0.002 0.11 0.039
TNF-A T-1031C rs1799964 TT TC CC 3147 1244 127 1 0.697 0.275 0.028 0.696 0.277 0.027 0.75 0.166
TNF-A C-857T rs1799724 CC CT TT 2990 1365 162 2 0.662 0.302 0.036 0.661 0.304 0.035 0.70 0.187
USF1 C7131T rs3737787 CC CT TT 2793 1506 218 2 0.618 0.333 0.048 0.616 0.338 0.046 0.43 0.215
VDR Met1Thr (Fok I) (C/T) rs2228570 CC CT TT 1768 2105 643 3 0.391 0.466 0.142 0.390 0.469 0.141 0.68 0.375
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Abbreviations: HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; SNP, single nucleotide polymorphism.

aAA, Aa, aa, and XX indicate homozygotes of major alleles, heterozygotes, homozygotes of minor alleles, and samples for which the genotype could not be determined, respectively.

bBased on the Hardy–Weinberg equilibrium.

The P value for departures from the Hardy–Weinberg equilibrium was less than 0.05 for 19 polymorphisms. However, the only genotypes for which the difference between the observed and expected frequencies exceeded 3% were the CETP Ile405Val (A/G) heterozygote and the SLC30A8 Arg325Trp (C/T) heterozygote. As shown in Table 5, some polymorphisms demonstrated a considerable difference in MAF among the participating cohorts; for 32 of the 108 polymorphisms, including ABCC11 Arg180Gly (T/C), there was a highly significant difference in MAF among study areas (P < 0.001).

Table 5. Minor allele frequencies of 107 selected genetic polymorphisms (106 SNPs and 1 insertion/deletion polymorphism) by study area.

Gene Polymorphism rs number Minor allele frequency by study area Pa
Total Chiba Shizuoka Okazaki ACC Takashima Kyoto Tokushima Fukuoka Saga Amami
ABCA1 C-565T rs2422493 0.402 0.371 0.429 0.406 0.426 0.401 0.394 0.468 0.404 0.405 0.355 0.005
ABCA1 G-191C rs1800976 0.402 0.370 0.429 0.406 0.425 0.403 0.403 0.468 0.408 0.405 0.356 0.006
ABCA1 G-17C rs2740483 0.301 0.363 0.296 0.300 0.271 0.294 0.300 0.247 0.313 0.313 0.271 0.0002
ABCA1 Val825Ile (G/A) rs2066715 0.356 0.354 0.358 0.331 0.354 0.350 0.347 0.353 0.360 0.358 0.389 0.51
ABCA1 Val771Met (G/A) rs2066718 0.066 0.067 0.060 0.067 0.059 0.061 0.034 0.047 0.073 0.068 0.088 0.041
ABCA1 Arg1587Lys (G/A) rs2230808 0.391 0.393 0.391 0.384 0.392 0.394 0.347 0.326 0.369 0.388 0.441 0.027
ACE Insertion/Deletion (I/D) rs1799752 0.365 0.373 0.369 0.346 0.363 0.365 0.313 0.353 0.357 0.341 0.425 0.003
ADD1 Trp460Gly (T/G) rs4961 0.451 0.454 0.436 0.424 0.455 0.430 0.463 0.463 0.429 0.464 0.509 0.006
ADH1B His47Arg (A/G) rs1229984 0.239 0.220 0.232 0.200 0.247 0.220 0.250 0.184 0.266 0.223 0.318 <0.0001
ADH1C Arg272Gln (C/T) rs1693482 0.075 0.053 0.068 0.054 0.178 0.056 0.053 0.058 0.065 0.053 0.072 <0.0001
ADIPOQ C-11377G rs266729 0.251 0.245 0.239 0.254 0.238 0.252 0.300 0.279 0.257 0.250 0.255 0.60
ADIPOQ Gly15Gly (T/G) rs2241766 0.290 0.285 0.304 0.285 0.309 0.301 0.316 0.295 0.287 0.284 0.251 0.18
ADIPOQ G276T rs1501299 0.262 0.290 0.284 0.282 0.147 0.266 0.263 0.289 0.286 0.308 0.236 <0.0001
ADIPOQ IVS-3971A/G rs822396 0.061 0.053 0.049 0.069 0.057 0.079 0.050 0.047 0.073 0.057 0.056 0.063
ADIPOR1 G-8503A rs6666089 0.032 0.029 0.040 0.034 0.028 0.027 0.044 0.026 0.029 0.024 0.040 0.29
ADIPOR1 C5843T rs1342387 0.483 0.484 0.473 0.476 0.481 0.480 0.478 0.426 0.495 0.483 0.504 0.78
ADIPOR1 C10224G rs7539542 0.216 0.226 0.230 0.201 0.202 0.223 0.219 0.263 0.233 0.215 0.194 0.21
ADRB2 Gln27Glu (C/G) rs1042714 0.065 0.073 0.051 0.061 0.066 0.093 0.081 0.079 0.072 0.063 0.039 0.0001
ADRB3 Trp64Arg (T/C) rs4994 0.195 0.184 0.173 0.195 0.184 0.195 0.147 0.168 0.193 0.218 0.240 0.001
AGT Thr174Met (C/T) rs4762 0.105 0.104 0.106 0.101 0.106 0.092 0.094 0.095 0.123 0.105 0.115 0.68
AGT Thr235Met (C/T) rs699 0.187 0.181 0.183 0.208 0.189 0.195 0.219 0.211 0.173 0.178 0.170 0.31
AGTR1(ATR1) A1166C (at 3′UTR) rs5186 0.087 0.089 0.093 0.083 0.088 0.076 0.081 0.089 0.080 0.082 0.104 0.61
ALDH2 Glu487Lys (G/A) rs671 0.254 0.232 0.274 0.316 0.298 0.254 0.226 0.300 0.269 0.267 0.112 <0.0001
APOA1 Ala61Thr (C/T) rs12718465 0.055 0.051 0.049 0.056 0.061 0.055 0.088 0.016 0.046 0.043 0.079 0.0005
APOA1 Arg184Pro (G/C) rs5078 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  
APOA5 T-1131C rs662799 0.345 0.355 0.336 0.322 0.337 0.326 0.338 0.342 0.342 0.337 0.412 0.002
APOA5 Gly185Cys (G553T) rs2075291 0.069 0.084 0.062 0.057 0.073 0.068 0.063 0.074 0.064 0.079 0.065 0.36
APOE T-203G rs405509 0.305 0.289 0.297 0.294 0.302 0.256 0.306 0.295 0.318 0.308 0.383 <0.0001
APOE Arg158Cys (C/T) (at exon2) rs7412 0.043 0.045 0.048 0.048 0.035 0.042 0.050 0.042 0.044 0.048 0.030 0.49
APOE Cys112Arg (T/C) (at exon4) rs429358 0.101 0.105 0.105 0.101 0.123 0.101 0.078 0.068 0.109 0.102 0.075 0.026
ARNTL(BMAL1) A/G rs7950226 0.404 0.424 0.405 0.420 0.395 0.402 0.391 0.347 0.427 0.431 0.344 0.002
ART4(DOK1) Leu208Leu (G/A) rs3088189 0.107 0.114 0.114 0.130 0.084 0.113 0.078 0.068 0.105 0.108 0.109 0.022
ART4(DOK1) Asp265Asn (G/A) rs11276 0.107 0.114 0.113 0.130 0.084 0.113 0.078 0.068 0.105 0.108 0.109 0.023
BHMT Arg239Gln (G742A) rs3733890 0.221 0.235 0.236 0.241 0.229 0.203 0.209 0.226 0.243 0.217 0.164 0.0005
CBS Tyr233Tyr (C699T) rs234706 0.024 0.027 0.033 0.026 0.020 0.021 0.028 0.011 0.034 0.017 0.017 0.073
CD14 T-260C/T-159C rs2569190 0.462 0.460 0.467 0.467 0.439 0.499 0.428 0.395 0.468 0.448 0.471 0.088
CDKAL1 G/C rs7754840 0.407 0.428 0.390 0.430 0.392 0.411 0.363 0.374 0.424 0.435 0.367 0.009
CDKN2A/B T/C rs10811661 0.451 0.465 0.450 0.466 0.446 0.470 0.453 0.411 0.441 0.452 0.419 0.42
CETP A-629C rs1800775 0.445 0.445 0.463 0.442 0.436 0.441 0.469 0.405 0.462 0.422 0.458 0.52
CETP Ile405Val (A/G) rs5882 0.492 0.537 0.376 0.452 0.523 0.498 0.509 0.458 0.495 0.556 0.506 <0.0001
CETP TaqIB (C/T) rs708272 0.405 0.404 0.401 0.419 0.419 0.384 0.375 0.363 0.378 0.443 0.398 0.067
CETP G/T rs3764261 0.233 0.183 0.340 0.295 0.203 0.188 0.209 0.158 0.204 0.223 0.240 <0.0001
CHRNB2 G-42A rs2072658 0.204 0.236 0.214 0.185 0.215 0.222 0.203 0.253 0.202 0.203 0.140 <0.0001
CHRNB2 C/T (at 3′UTR) rs2072660 0.243 0.258 0.225 0.264 0.224 0.241 0.216 0.237 0.214 0.233 0.292 0.001
COMT Val158Met (G/A) rs4680 0.336 0.310 0.328 0.336 0.323 0.359 0.303 0.295 0.336 0.315 0.406 <0.0001
CYP1A1 Ile462Val (A/G) rs1048943 0.239 0.222 0.235 0.256 0.222 0.244 0.250 0.268 0.222 0.225 0.274 0.069
CYP1A2 A734C rs762551 0.358 0.336 0.349 0.356 0.344 0.367 0.352 0.384 0.351 0.363 0.391 0.38
CYP11B2 T-344C rs1799998 0.331 0.334 0.318 0.339 0.289 0.294 0.346 0.284 0.329 0.325 0.434 <0.0001
CYP17A1 T-34C rs743572 0.466 0.453 0.460 0.449 0.485 0.492 0.500 0.353 0.467 0.452 0.482 0.018
ESR1 IVS1-351A/G (Xba I) rs11155816 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  
ESR1 IVS1-397T/C (Pvu II) rs2234693 0.425 0.436 0.422 0.422 0.416 0.452 0.334 0.421 0.430 0.438 0.415 0.062
ESR2 Val328Val (G1082A) (Rsa I) rs1256049 0.284 0.268 0.281 0.297 0.272 0.308 0.272 0.284 0.314 0.293 0.250 0.054
FTO T/A rs9939609 0.201 0.172 0.195 0.220 0.190 0.188 0.169 0.158 0.206 0.186 0.277 <0.0001
GCK G-30A rs1799884 0.189 0.193 0.179 0.183 0.186 0.182 0.138 0.147 0.304 0.173 0.159 <0.0001
GCKR A/G rs780094 0.449 0.428 0.436 0.440 0.439 0.425 0.350 0.453 0.450 0.445 0.562 <0.0001
GCKR Leu446Pro (T/C) rs1260326 0.445 0.430 0.434 0.439 0.438 0.416 0.353 0.421 0.447 0.432 0.559 <0.0001
GNAS1 T393C rs7121 0.428 0.448 0.427 0.408 0.463 0.422 0.469 0.342 0.454 0.418 0.391 0.002
IGF2BP2 G/T (at intron) rs4402960 0.303 0.328 0.306 0.292 0.298 0.296 0.309 0.268 0.300 0.304 0.306 0.81
IL-1B T-31C rs1143627 0.462 0.444 0.463 0.477 0.439 0.454 0.491 0.484 0.477 0.462 0.475 0.52
IL-2 T-330G rs2069762 0.329 0.330 0.336 0.316 0.311 0.323 0.309 0.305 0.350 0.321 0.365 0.21
IL-4 T-33C rs2070874 0.338 0.317 0.337 0.315 0.333 0.291 0.313 0.284 0.336 0.337 0.457 <0.0001
IL-6 C-634G rs1800796 0.244 0.247 0.206 0.250 0.234 0.226 0.225 0.232 0.223 0.240 0.338 <0.0001
IL-8 T-251A rs4073 0.320 0.334 0.321 0.318 0.320 0.304 0.331 0.266 0.339 0.329 0.306 0.56
IL-10 T-819C rs1800871 0.346 0.329 0.346 0.320 0.340 0.313 0.356 0.268 0.354 0.359 0.425 <0.0001
IL-13 C-1111T rs1800925 0.183 0.182 0.178 0.197 0.185 0.178 0.213 0.158 0.172 0.190 0.176 0.73
KCNJ11 Glu23Lys (C/T) rs5219 0.366 0.386 0.364 0.382 0.343 0.374 0.363 0.316 0.361 0.366 0.368 0.52
LCAT/SLC12A4 Ser232Thr (T/A) rs4986970 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  
LIPC T-514C rs1800588 0.490 0.522 0.478 0.496 0.484 0.497 0.522 0.484 0.502 0.503 0.429 0.006
LIPC Val95Met (G/A) rs6078 0.234 0.232 0.229 0.221 0.269 0.259 0.247 0.237 0.208 0.253 0.182 <0.0001
MPO G-463A rs2333227 0.106 0.107 0.105 0.107 0.102 0.113 0.100 0.100 0.106 0.093 0.119 0.84
MTHFD1 Arg134Lys (C401T) rs1950902 0.218 0.214 0.216 0.218 0.212 0.229 0.184 0.195 0.217 0.239 0.215 0.66
MTHFD1 Arg653Gln (G1958A) rs2236225 0.283 0.271 0.273 0.269 0.265 0.280 0.284 0.279 0.278 0.301 0.331 0.036
MTHFR Ala222Val (C677T) rs1801133 0.382 0.387 0.402 0.388 0.411 0.393 0.375 0.442 0.410 0.364 0.288 <0.0001
MTHFR Glu429Ala (A1298C) rs1801131 0.184 0.195 0.189 0.202 0.193 0.193 0.225 0.191 0.147 0.222 0.105 <0.0001
MTR Asp919Gly (A/G) rs1805087 0.201 0.188 0.210 0.186 0.183 0.179 0.184 0.232 0.193 0.176 0.299 <0.0001
MTRR Ile22Met (A66G) rs1801394 0.310 0.292 0.314 0.298 0.315 0.296 0.306 0.337 0.318 0.297 0.346 0.24
NOS3 T-786C rs2070744 0.106 0.111 0.125 0.117 0.112 0.098 0.119 0.122 0.109 0.102 0.068 0.004
PPARD T-842C (at exon3) rs2267668 0.195 0.175 0.199 0.201 0.183 0.205 0.213 0.221 0.192 0.190 0.210 0.55
PPARD T-48444C (at exon3) rs6902123 0.020 0.015 0.020 0.030 0.025 0.017 0.019 0.016 0.019 0.019 0.012 0.15
PPARD Asn163Asn (A65G) (at exon7) rs2076167 0.215 0.190 0.212 0.231 0.206 0.221 0.225 0.234 0.209 0.205 0.236 0.30
PPARG Pro12Ala (C/G) rs1801282 0.031 0.031 0.027 0.028 0.037 0.047 0.022 0.011 0.029 0.032 0.025 0.064
PPARG His477His (C161T) rs3856806 0.152 0.153 0.159 0.143 0.149 0.167 0.159 0.147 0.177 0.166 0.099 0.0002
PPARGC1A Thr394Thr (C/T) rs2970847 0.219 0.229 0.223 0.237 0.219 0.222 0.209 0.237 0.208 0.243 0.168 0.005
PPARGC1A Gly482Ser (G/A) rs8192678 0.460 0.472 0.447 0.444 0.431 0.448 0.463 0.442 0.471 0.438 0.538 <0.0001
PRKAA2 IVS4+961T/C rs1418442 0.231 0.216 0.228 0.232 0.206 0.217 0.184 0.237 0.205 0.237 0.318 <0.0001
PRKAA2 IVS7+81T/C rs932447 0.231 0.216 0.228 0.232 0.207 0.218 0.184 0.237 0.205 0.239 0.318 <0.0001
PRKAA2 A/T (at 3′UTR) rs1342382 0.221 0.238 0.220 0.200 0.250 0.225 0.204 0.211 0.233 0.197 0.212 0.072
PTGS2(COX2) G-765C rs20417 0.029 0.028 0.033 0.031 0.033 0.027 0.025 0.053 0.026 0.030 0.017 0.22
PTGS2(COX2) C-163G rs5270 0.016 0.017 0.023 0.017 0.020 0.015 0.009 0.011 0.009 0.011 0.016 0.32
PTPN11 G33861A rs2301756 0.195 0.194 0.199 0.172 0.167 0.185 0.184 0.205 0.173 0.159 0.320 <0.0001
RETN C-420G rs1862513 0.348 0.351 0.328 0.330 0.343 0.330 0.334 0.316 0.340 0.366 0.411 0.002
SCARB1 Val135Ile (G/A) rs5891 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  
SCARB1 Ala350Ala (C1119T) rs5888 0.228 0.240 0.212 0.226 0.228 0.225 0.253 0.205 0.240 0.240 0.213 0.59
SCARB1 G/A (at intron) rs3782287 0.234 0.225 0.223 0.243 0.227 0.249 0.297 0.189 0.207 0.219 0.263 0.008
SERPINC1 C/G rs677 0.219 0.220 0.197 0.209 0.238 0.223 0.238 0.211 0.220 0.227 0.217 0.58
SHMT1 Leu435Phe (C1420T) rs1979277 0.089 0.072 0.089 0.071 0.093 0.092 0.084 0.095 0.084 0.091 0.117 0.026
SLC19A(RFC1) His27Arg (A80G) rs1051266 0.418 0.415 0.435 0.412 0.426 0.423 0.397 0.395 0.430 0.454 0.352 0.001
SLC30A8 Arg325Trp (C/T) rs13266634 0.437 0.439 0.449 0.443 0.438 0.418 0.472 0.453 0.434 0.451 0.411 0.53
SRD5A2 Leu89Val (C/G) rs523349 0.452 0.437 0.468 0.430 0.458 0.458 0.391 0.399 0.449 0.471 0.464 0.13
TAS2R38 Pro49Ala (C/G) rs713598 0.445 0.448 0.439 0.419 0.427 0.454 0.434 0.479 0.445 0.435 0.491 0.080
TAS2R38 Ala262Val (C/T) rs1726866 0.444 0.449 0.438 0.419 0.427 0.454 0.434 0.479 0.444 0.435 0.491 0.078
TAS2R38 Val296Ile (C/T) rs10246939 0.444 0.448 0.438 0.419 0.427 0.454 0.434 0.479 0.444 0.435 0.491 0.079
TCF7L2 C/T (at intron) rs7903146 0.039 0.051 0.039 0.042 0.043 0.037 0.034 0.042 0.025 0.034 0.043 0.27
TNF-A T-1031C rs1799964 0.166 0.175 0.148 0.165 0.157 0.153 0.153 0.237 0.151 0.180 0.188 0.024
TNF-A C-857T rs1799724 0.187 0.172 0.171 0.173 0.166 0.161 0.194 0.184 0.203 0.199 0.255 <0.0001
USF1 C7131T rs3737787 0.215 0.229 0.223 0.220 0.234 0.217 0.263 0.216 0.196 0.204 0.178 0.020
VDR Met1Thr (Fok I) (C/T) rs2228570 0.375 0.344 0.393 0.368 0.380 0.381 0.391 0.437 0.402 0.377 0.343 0.048
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Abbreviations: ACC, Aichi Cancer Center; SNP, single nucleotide polymorphism.

aP for difference among study areas.

The Figure shows a comparison of the allele frequencies in our study population and the HapMap-JPT data set. Among 88 polymorphisms, only 5 (ABCA1 rs2230808, COMT rs4680, IL-6 rs1800796, NOS3 rs2070744, and VDR rs2228570) showed a difference in allele frequencies of more than 0.1 between the 2 populations.

Figure. Scatter plot of allele frequencies for 88 polymorphisms in the data set from the Japan Multi-institutional Collaborative Cohort (J-MICC) Study and the HapMap-JPT data set registered at the US National Library of Medicine (http://www.ncbi.nlm.nih.gov/snp). Points on the identity (dotted) line represent allele frequencies that are identical in the 2 populations.

Figure.

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DISCUSSION

The present report describes the profiles of participants in a cross-sectional study within the J-MICC Study data set and the allele frequencies of 108 polymorphisms, with potential relevance to lifestyle and clinical factors, in their genomes. The allele frequencies for most polymorphisms in our study population were comparable to those in the HapMap-JPT data set.

It has been suggested that polymorphisms for APOA1 184Pro (C), ESR1 IVS1-351G, LCAT/SLC12A4 232Thr (A), and SCARB1 135Ile (A) do not exist in the Japanese population (http://www.ncbi.nlm.nih.gov/snp and personal communication); however, we included them in the present study to test this notion in a large sample (>4000 people). Our results confirmed that these minor alleles were indeed absent among Japanese.

Of the remaining 104 polymorphisms, 19 showed departures from the Hardy–Weinberg equilibrium with P values <0.05. In most cases, however, the absolute differences between the actual and expected frequencies were minimal. Thus, these apparently small P values could be accounted for by the large sample size and the multiple tests used in our study, and any errors in genotyping seemed unlikely to have resulted in substantial misclassification.

Although genotype data for only 45 people, at most, are available in the HapMap-JPT data set, the allele frequencies in the HapMap-JPT population and our study population were remarkably similar for most of the polymorphisms examined (Figure). For 45 individuals, the 95% confidence intervals were 0.047 to 0.181, 0.208 to 0.406, and 0.393 to 0.607 for MAF values of 0.1, 0.3, and 0.5, respectively, based on a binomial distribution.

A major strength of the current study was that it provided a comprehensive collection of data on lifestyle and clinical factors. Because it is not easy to gain access to data on genotype distributions in a large Japanese population, our data might also be useful as a reference tool. However, because the participants in this study were recruited from various sources throughout Japan, associations of genotypes with lifestyle and clinical factors might vary between populations. There might also have been differences between institutions in terms of the measurement methods used in the clinical examinations, because we could not directly control the quality of the health examinations. These differences must be taken into consideration when analyzing and interpreting the data. In addition, some polymorphisms showed a substantial difference in MAF among the participating cohorts (Table 5). Yamaguchi-Kabata et al suggested that individuals from the Ryukyu Islands, including the Amami Islands, had genetic characteristics that differed considerably from those of individuals from the main islands of Japan,22 which was consistent with our present results. Genetic variations among study areas should be taken into account in the data analysis. Furthermore, the generalizability of the study findings should be considered because the response rates were low in some areas. In most cases, however, the underlying biological mechanisms are unlikely to differ between the respondents and members of the general population. The low response rate might have been due to the recruitment methods (mailing invitation letters or distributing leaflets to the general populations of 3 areas) or the strict procedures used to obtain informed consent.

In conclusion, this comprehensive data collection on lifestyle and clinical factors will be useful in elucidating gene–environment interactions and could provide an informative reference tool, particularly because free access to genotype data for a large Japanese population is not readily available.

ACKNOWLEDGMENTS

The authors thank Kyota Ashikawa, Tomomi Aoi, and the other members of the Laboratory for Genotyping Development, Center for Genomic Medicine, RIKEN, for support with genotyping, Yoko Mitsuda, Keiko Shibata, and Etsuko Kimura at the Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Yasushi Yatabe at the Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Hospital, Fusako Katsurada at the Department of Health Science, Shiga University of Medical Science, and Mitsuhiko Matsushita and Yasunobu Sagara at the Tokushima Prefecture Health Examination Center for their cooperation, technical assistance, and valuable comments. This study was supported, in part, by Grants-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science and Technology (Nos. 17015018 and 221S0001).

Conflicts of interest: None declared.

REFERENCES

  • 1.Yang CX , Matsuo K , Ito H , Hirose K , Wakai K , Saito T , et al. . Esophageal cancer risk by ALDH2 and ADH2 polymorphisms and alcohol consumption: exploration of gene-environment and gene-gene interactions . Asian Pac J Cancer Prev. 2005;6:256–62 [PubMed] [Google Scholar]
  • 2.Taioli E Gene-environment interaction in tobacco-related cancers . Carcinogenesis. 2008;29:1467–74 10.1093/carcin/bgn062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Andreassi MG Metabolic syndrome, diabetes and atherosclerosis: influence of gene-environment interaction . Mutat Res. 2009;667:35–43 10.1016/j.mrfmmm.2008.10.018 [DOI] [PubMed] [Google Scholar]
  • 4.Vercelli D Gene-environment interactions in asthma and allergy: the end of the beginning? Curr Opin Allergy Clin Immunol. 2010;10:145–8 10.1097/ACI.0b013e32833653d7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Collins FS The case for a US prospective cohort study of genes and environment . Nature. 2004;429:475–7 10.1038/nature02628 [DOI] [PubMed] [Google Scholar]
  • 6.Potter JD Toward the last cohort . Cancer Epidemiol Biomarkers Prev. 2004;13:895–7 [PubMed] [Google Scholar]
  • 7.Ollier W , Sprosen T , Peakman T. UK Biobank: from concept to reality . Pharmacogenomics. 2005;6:639–46 10.2217/14622416.6.6.639 [DOI] [PubMed] [Google Scholar]
  • 8.Manolio TA , Bailey-Wilson JE , Collins FS. Genes, environment and the value of prospective cohort studies . Nat Rev Genet. 2006;7:812–20 10.1038/nrg1919 [DOI] [PubMed] [Google Scholar]
  • 9.Rothman KJ. Modern Epidemiology, 1st ed. Boston: Little, Brown and Company; 1986. [Google Scholar]
  • 10.The J-MICC Study Group The Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer . Asian Pac J Cancer Prev. 2007;8:317–23 [PubMed] [Google Scholar]
  • 11.Naito M , Eguchi H , Okada R , Ishida Y , Nishio K , Hishida A , et al. . Controls for monitoring the deterioration of stored blood samples in the Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) . Nagoya J Med Sci. 2008;70:107–15 [PubMed] [Google Scholar]
  • 12.Asai Y , Naito M , Suzuki M , Tomoda A , Kuwabara M , Fukada Y , et al. . Baseline data of Shizuoka area in the Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) . Nagoya J Med Sci. 2009;71:137–44 [PMC free article] [PubMed] [Google Scholar]
  • 13.Hara M , Higaki Y , Imaizumi T , Taguchi N , Nakamura K , Nanri H , et al. . Factors influencing participation rate in a baseline survey of a genetic cohort in Japan . J Epidemiol. 2010;20:40–5 10.2188/jea.JE20090062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hamajima N , Atsuta Y , Niwa Y , Nishio K , Tanaka D , Yamamoto K , et al. . Precise definition of anonymization in genetic polymorphism studies . Asian Pac J Cancer Prev. 2004;5:83–8 [PubMed] [Google Scholar]
  • 15.Ohnishi Y , Tanaka T , Ozaki K , Yamada R , Suzuki H , Nakamura Y. A high-throughput SNP typing system for genome-wide association studies . J Hum Genet. 2001;46:471–7 10.1007/s100380170047 [DOI] [PubMed] [Google Scholar]
  • 16.Tokudome S , Goto C , Imaeda N , Tokudome Y , Ikeda M , Maki S. Development of a data-based short food frequency questionnaire for assessing nutrient intake by middle-aged Japanese . Asian Pac J Cancer Prev. 2004;5:40–3 [PubMed] [Google Scholar]
  • 17.Tokudome Y , Goto C , Imaeda N , Hasegawa T , Kato R , Hirose K , et al. . Relative validity of a short food frequency questionnaire for assessing nutrient intake versus three-day weighed diet records in middle-aged Japanese . J Epidemiol. 2005;15:135–45 10.2188/jea.15.135 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Goto C , Tokudome Y , Imaeda N , Takekuma K , Kuriki K , Igarashi F , et al. . Validation study of fatty acid consumption assessed with a short food frequency questionnaire against plasma concentration in middle-aged Japanese people . Scand J Nutr. 2006;50:77–82 10.1080/11026480600568720 [DOI] [Google Scholar]
  • 19.Imaeda N , Goto C , Tokudome Y , Hirose K , Tajima K , Tokudome S. Reproducibility of a short food frequency questionnaire for Japanese general population . J Epidemiol. 2007;17:100–7 10.2188/jea.17.100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wigginton JE , Cutler DJ , Abecasis GR. A note on exact tests of Hardy-Weinberg equilibrium . Am J Hum Genet. 2005;76:887–93 10.1086/429864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.SAS Institute Inc. SAS/STAT 9.1 User’s Guide. Cary, NC: SAS Institute Inc; 2004. [Google Scholar]
  • 22.Yamaguchi-Kabata Y , Nakazono K , Takahashi A , Saito S , Hosono N , Kubo M , et al. . Japanese population structure, based on SNP genotypes from 7003 individuals compared to other ethnic groups: effects on population-based association studies . Am J Hum Genet. 2008;83:445–56 10.1016/j.ajhg.2008.08.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

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