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. 2023 Jan 27;14(2):188–198. doi: 10.1007/s13340-023-00618-0

Comparison of the loci associated with HbA1c and blood glucose levels identified by a genome-wide association study in the Japanese population

Takuya Sakashita 1,2,✉, Yasuyuki Nakamura 1,3, Yoichi Sutoh 4, Atsushi Shimizu 4, Tsuyoshi Hachiya 4, Yayoi Otsuka-Yamasaki 4, Naoyuki Takashima 1,5,13, Aya Kadota 1,6, Katsuyuki Miura 1,6, Yoshikuni Kita 1,7, Hiroaki Ikezaki 8,9, Jun Otonari 10, Keitaro Tanaka 11, Chisato Shimanoe 12, Teruhide Koyama 13, Isao Watanabe 13, Sadao Suzuki 14, Hiroko Nakagawa-Senda 14, Asahi Hishida 15, Takashi Tamura 15, Yasufumi Kato 15, Rieko Okada 15, Kiyonori Kuriki 16, Sakurako Katsuura-Kamano 17, Takeshi Watanabe 17, Shiroh Tanoue 18, Chihaya Koriyama 18, Isao Oze 19, Yuriko N Koyanagi 20, Yohko Nakamura 21, Miho Kusakabe 21, Masahiro Nakatochi 22, Yukihide Momozawa 23, Kenji Wakai 15, Keitaro Matsuo 19,24
PMCID: PMC10113415  PMID: 37090135

Abstract

Aims

Hemoglobin A1c (HbA1c) levels are widely employed to diagnose diabetes. However, estimates of the heritability of HbA1c and glucose levels are different. Therefore, we explored HbA1c- and blood glucose-associated loci in a non-diabetic Japanese population.

Methods

We conducted a two-stage genome-wide association study (GWAS) on variants associated with HbA1c and blood glucose levels in a Japanese population. In the initial stage, data of 4911 participants of the Japan Multi-Institutional Collaborative Cohort (J-MICC) were subjected to discovery analysis. In the second stage, two datasets from the Tohoku Medical Megabank project, with 8175 and 40,519 participants, were used for the replication study. Association of the imputed variants with HbA1c and blood glucose levels was determined via linear regression analyses adjusted for age, sex, body mass index (BMI), smoking, and genetic principal components (PC1–PC10). Moreover, we performed a BMI-stratified GWAS on HbA1c levels in the J-MICC. The discovery analysis and BMI-stratified GWAS results were validated with re-analyses of normalized HbA1c levels adjusted for site in addition to the above, and blood glucose adjusted for fasting time as an additional covariate.

Results

Genetic variants associated with HbA1c levels were identified in KCNQ1 and TMC6. None of the genetic variants associated with blood glucose levels in the discovery analysis were replicated. Association of rs2299620 in KCNQ1 with HbA1c levels showed heterogeneity between individuals with BMI ≥ 25 kg/m2 and BMI < 25 kg/m2.

Conclusions

The variant rs2299620 in KCNQ1 might affect HbA1c levels differentially based on BMI grouping in the Japanese population.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13340-023-00618-0.

Keywords: Blood glucose, Body mass index, Diabetes, Genome-wide association study, HbA1c, Japanese population

Introduction

Hemoglobin A1c (HbA1c) is formed via a non-enzymatic reaction between glucose and hemoglobin. Once formed, HbA1c is not deglycated and accumulates primarily in erythrocytes. HbA1c levels reflect the average blood glucose levels over approximately 3 months prior to measurement. Moreover, HbA1c levels have lower interindividual variability than the fasting glucose level or 2 h post-challenge glucose level in an oral glucose tolerance test, and their measurements do not require fasting [1, 2]. To date, the largest genome-wide association study (GWAS) of variants associated with HbA1c levels was a meta-analysis of non-diabetic individuals of European ancestry (n = 46,368 participants) [3]. The second-largest similar GWAS was a meta-analysis of a non-diabetic East Asian population (n = 21,026 participants) [4]. In Japan, two studies had identified genetic loci associated with HbA1c levels [5, 6]. GWASs have improved our understanding of genetic predisposition to diabetes owing to glycemic and nonglycemic factors [7]. HbA1c levels are associated with variants of genes known to be important for glucose homeostasis, such as SLC30A8, TCF7L2, G6PC2, GCK, and CDKAL [3, 8]. HbA1c-associated variants were also found in HFE, TMPRSS6, ANK1, SPTA1, and HK1, which play important roles in the development and functioning of erythrocyte [9, 10]. A variant of TMPRSS6 is known to cause hereditary anemia [11]. Therefore, the influence of these variants on HbA1c levels may be related to their effects on erythrocyte half-life. In addition, variants near FN3K may act via their effects on protein deglycation [12].

Heritability of impaired glucose tolerance varies and is represented by the following indices: HbA1c (47–59%), fasting glucose (34–56%), and glucose (33%) levels as determined by 2 h post-challenge glucose after an oral glucose tolerance test [13]. Moreover, classifications of individuals based on genetic variants associated with HbA1c and fasting glucose levels are significantly different [3, 4]. Nevertheless, HbA1c is widely used as a biomarker for diagnosing diabetes without considering genetic factors in the population-level screening. According to Japanese GWAS databanks jMorp supplied by the Tohoku Medical Megabank (TMM) [14, 15] and BioBank Japan (BBJ) [16, 17], KCNQ1, SLC30AB, and AL359922.1 are among the ten genes that are substantially associated with both HbA1c and blood glucose levels. The transmembrane channel-like 6 (TMC6) gene was found to be associated with only HbA1c levels, whereas the glucokinase regulator (GCKR) gene was associated with only blood glucose levels according to Japanese GWAS databanks. The existence of genetic factors differentially associated with HbA1c and blood glucose levels has not been fully examined yet. Therefore, this study aimed to explore and compare the HbA1c and blood glucose-associated loci to elucidate how genetic factors influence the diabetes-related indices in the Japanese population and explain the underlying mechanisms. Additionally, we attempted to interpret the relationship between genetic factors associated with HbA1c levels and body mass index (BMI), since a large-scale Japanese GWAS for type 2 diabetes had previously reported odds ratio differences for some genetic variants between groups with BMI ≥ 25 kg/m2 (BMI ≥ 25) and those with BMI < 25 kg/m2 (BMI < 25) [18].

Materials and methods

Study population

A cross-sectional GWAS was performed using samples from participants aged 35–69 years of the Japan Multi-Institutional Collaborative Cohort (J-MICC) study. Participants in the J-MICC study were recruited from 12 different locations in Japan between 2004 and 2016. The J-MICC study began in 2005 with the intent to examine the interactions between genes and environmental factors in the context of lifestyle-related diseases. Details of the J-MICC study have been described in previous reports [19, 20]. Briefly, participants answered a questionnaire regarding lifestyle and medical information and provided a blood specimen at the baselines. The J-MICC study participants included community citizens, first-visit patients at a cancer institution, and health checkup examinees. Written informed consent was obtained from all participants. The study protocol was approved by the Ethics Committees of the Aichi Cancer Center (Nagoya, Aichi, Japan), Nagoya University Graduate School of Medicine (Nagoya, Aichi, Japan), and other institutions involved in the J-MICC study. The study was conducted according to the principles of the World Medical Association Declaration of Helsinki.

In this study, J-MICC dataset ver. 20,180,111 was used, and 14,539 randomly selected J-MICC study participants from the 12 areas (Chiba, Sakuragaoka, Shizuoka-Daiko, Okazaki, Aichi, Takashima, Kyoto, Tokushima, Fukuoka, Saga, Kagoshima, and Kyushu-KOPS [Kyushu Okinawa Population Study]), where the J-MICC study had been conducted, were included. Of the 14,539 participants, 448 were excluded based on the GWAS screening described in the “Genotyping and quality control filtering” section. Of the remaining 14,091 participants, 7490, 1472, and 218 were excluded owing to the lack of blood glucose and HbA1c data and self-reported diabetes, respectively. Finally, we analyzed the data of 4911 participants.

Questionnaire and measurements

The J-MICC study questionnaire consisted of questions regarding medical history, weight, height, and smoking habits. Anthropometric measurements and blood sampling were performed as part of health examinations or for research purposes at the institutions participating in the J-MICC study [19]. Body weights and heights were measured to the nearest 0.1 kg and 0.1 cm, respectively. BMI was calculated by dividing body weight in kilograms by the height in meters squared. HbA1c levels were measured at each site of the J-MICC institutions, and the percentage was determined using a latex aggregation immunoassay (Japan Diabetes Society [JDS] value); it was estimated from the National Glycohemoglobin Standardization Program (NGSP) equivalent percentage obtained using the following formula: HbA1c (NGSP [%]) = 1.02 × HbA1c (JDS [%]) + 0.25% [21]. We analyzed blood glucose values as “non-fasting” because fasting data was not thorough. While 2,783 participants experienced ≥ 10 post-prandial hours, 1043 participants experienced < 10 post prandial hours; the fasting time data of 1085 participants were not available.

Genotyping and quality control filtering

Buffy coat fractions were prepared from blood specimens and stored at − 80 °C in the central study office (Nagoya, Aichi, Japan) for use in the J-MICC study. DNA was extracted from all buffy coat fractions using a BioRobot M48 Workstation (Qiagen Group, Tokyo, Japan) located in the central study office. For specimens obtained from Fukuoka and Kyushu-KOPS, DNA was extracted locally from whole blood specimens using an automatic nucleic acid isolation system (NA-3000, Kurabo, Co., Ltd, Osaka, Japan). A total of 14,539 study participants from the 12 locations of the J-MICC study were genotyped at the RIKEN Center for Integrative Medicine Sciences using a HumanOmniExpressExome-8 v1.2 BeadChip array (Illumina Inc., San Diego, CA, USA). Inconsistent sex information was detected for 26 participants owing to differences between that determined by genotyping and the information from the questionnaire, and these participants were excluded from analyses. Using the identity-by-descent method implemented in the PLINK 1.9 software [22], we identified 388 close relationship pairs (pi-hat > 0.1875), and one sample from each pair of the 388 relationships was excluded. Genetic principal component analysis (PCA) was performed using the SMARTPCA program implemented in EIGENSOFT v6.0.1 [23]. PCA with a 1000 Genomes reference panel (phase 3) [24, 25] identified 34 participants whose estimated ancestries were outside of the Japanese ancestry [26]; hence, these participants were excluded from the study. The remaining 14,091 samples met the sample-wise genotype call rate criterion (≥ 0.99). Single nucleotide polymorphisms (SNPs) with a genotype call rate < 0.98, a Hardy–Weinberg equilibrium exact test P-value < 1 × 10−6, and/or a minor allele frequency (MAF) < 0.01 were removed. Quality control excluded 448 individuals, resulting in the inclusion of 570,162 autosomal SNPs.

Genotype imputation

Genotype imputation was conducted using SHAPEIT [27] and Minimac3 [28] software based on the 1000 Genomes reference panel (phase 3) [25]. Strict quality control filters were applied after genotype imputation (i.e., variants with R2 < 0.3 were excluded), resulting in 12,617,547 variants. Finally, 4,336,771 variants with MAF < 0.01 were removed, resulting in 8,280,776 variants that were used for further analysis. We used the DosageConvertor software [29] to convert the dosage files in the VCF format from Minimac3 to the PLINK format.

Analyses of the association of variants with HbA1c and blood glucose levels

For the discovery analyses, all imputed variants associated with HbA1c and blood glucose levels were analyzed after adjustments for age, sex, BMI, smoking status, and PCs 1–10 calculated using the PLINK 1.9 software (Fig. 1). Variants achieving genome-wide significance (P < 5 × 10−8) were regarded as genome-wide significant (GWS) variants for HbA1c and blood glucose levels. The R/Bioconductor package, GWASTools, was used to create quantile–quantile (Q–Q) plots [30]. The qqman package was used for Manhattan plots of P-values derived from genome-wide scan results for HbA1c and blood glucose levels [31]. The LocusZoom program was used to visualize the regions of interest [32]. For sensitivity analysis, we analyzed the association between variants and blood glucose levels adjusted for fasting time (in minutes), the data for which was available for 3826 out of 4911 participants, in addition to a set of covariates such as age, sex, BMI, smoking status, and PCs 1–10.

Fig. 1.

Fig. 1

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Outline of the present study. GWASs in the J-MICC as discovery analyses. Replication studies in the TMM10K and TMM67K_JPAV2 datasets. Meta-analysis of the GWS variants with J-MICC, TMM10K, and TMM67K_JPAV2. GWS genome-wide significant

Genome-wide association study of normalized HbA1c and blood glucose levels

Because the skewness of the distributions of HbA1c and that of blood glucose levels among the 4911 participants was 4.93 and 5.71, indicating non-normal distributions, we re-analyzed normalized HbA1c and blood glucose levels using the rank-based inverse normal transformation, which changed the skewness of the distributions of HbA1c and blood glucose levels to 0.0069 and 0.0011, respectively. We adjusted for the site, in addition to the set of covariates, during the re-analysis of the normalized HbA1c levels. Additionally, we adjusted for fasting time (in minutes), in addition to the site and the set of covariates, during the re-analysis of the normalized blood glucose levels (Fig. 1).

Replication study in an independent Japanese cohort

For the replication study, we used data from the TMM Project cohort, recruited from May 2013 to March 2016, including 66,047 participants residing in Iwate and Miyagi prefectures along the Pacific coast of the Tohoku region of Japan [33]. Of these, 9721 participants enrolled in 2013 were genotyped using the HumanOmniExpressExome BeadChip Array (Illumina Inc., San Diego, CA, USA), and the dataset was denoted as the TMM10K dataset [34]. The remaining 52,936 participants were genotyped with the customized genotyping array designed by the TMM Project based on the Axiom platform (Thermo Fisher Scientific, Waltham, MA, USA), namely, Japonica Array Version 2 (JPAv2), and the information was denoted as the TMM67K_JPAV2 dataset [35]. Genotype imputation was conducted using the SHAPEIT [26] and Impute2 [36] software. ToMMo 2KJPN and ToMMo 3.5KJPNv2, comprising whole-genome sequencing data, were used as reference panels for TMM10K and TMM67K_JPAV2, respectively [37, 38]. SNPs with MAF < 0.01 or imputation quality score (info) < 0.3 were excluded from analysis using PLINK 2.0.

Quality control tests for phenotype assignment were performed following the method used in the discovery analysis. Data from 8175 to 40,519 patients in the TMM10K and TMM67K_JPAV2 datasets, respectively, were finally used for replication analysis using the same tools and covariates as in the discovery analysis (Fig. 1). No duplicate participants between TMM10K and TMM67K_JPAV2 remained in the final datasets. The variants achieving GWS (P < 5 × 10−8) were regarded as replicated variants associated with HbA1c and blood glucose levels.

Meta-analysis of the GWS variants

We performed meta-analyses of the GWS variants associated with HbA1c and blood glucose levels in the discovery analyses of the J-MICC, TMM10K, and TMM67K_JPAV2 datasets using the METAL software [39], except for the variants with no available data in the TMM datasets (Fig. 1). The variants with an association P-value lower than the Bonferroni threshold were considered HbA1c- and blood glucose-associated variants.

GWAS of heterogeneous association of replicated variants with HbA1c levels in BMI-stratified cohorts

We conducted another GWAS in the J-MICC, stratified by BMI, to assess the heterogeneity of association of replicated variants with HbA1c levels. The J-MICC participants with HbA1c level data were divided into BMI ≥ 25 and BMI < 25 groups (n = 2033 and 6167, respectively), excluding participants with self-reported diabetes and those undergoing treatment. Phenotype quality control was performed following the method used in the discovery analysis. Data from 2033 participants in the BMI ≥ 25 group and 6167 participants in the BMI < 25 group were finally used for the GWAS using the same tools and covariates as in the discovery analysis. Heterogeneity between the BMI ≥ 25 and BMI < 25 groups was analyzed using the METAL software [39]. Variants with a heterogeneity P-value < 0.05 were considered significant for heterogeneity. The skewness of the distributions of HbA1c levels among the BMI ≥ 25 and BMI < 25 groups was 4.00 and 6.24, which indicates non-normal distributions. We re-analyzed normalized HbA1c levels, which changed the skewness of the distributions of HbA1c levels to 0.0073 and 0.0054, adjusted for the site in addition to the set of covariates during the re-analyses.

Functional annotations

We examined genomic locations of the replicated variants associated with HbA1c levels, identified in this study, using the UCSC [40] and Ensembl [41] genome browsers. Cis-expression quantitative trait locus (eQTL) pairs of variants and genes were obtained from GTEx [42], which contains significant eQTL variant-gene pairs from 54 tissues.

Assessment of linkage disequilibrium

We assayed linkage disequilibrium (LD) in the region of interest using the program HAPLOVIEW [43] based on phase 3 JPT data of the1000 Genomes Project (n = 104). We calculated Dʹ and r2 for all pair-wise comparisons of variants.

Results

Characteristics of study participants

The characteristics of study participants in the J-MICC and TMM cohorts are presented in Table 1.

Table 1.

Background characteristics of the study participants from the J-MICC, TMM10K, and TMM67K_JPAV2

J-MICC TMM10K TMM67K_JPAV2
n 4911 8175 40,519
Female, % 52.9 66.5 62.8
Age, year (mean ± SD) 53.7 ± 9.7 60.3 ± 11.1 60.4 ± 11.0
BMI, kg/m2 (mean ± SD) 23.1 ± 3.3 23.4 ± 3.5 23.4 ± 3.5
Smoking status, current, former, never, other, % 18.3, 22.3, 59.3, 0.1 12.7, 20.3, 60.3, 6.7 13.1, 21.5, 59.0, 6.4
Blood glucose, mg/dL (mean ± SD) 95.9 ± 18.2 102.2 ± 23.2 95.6 ± 21.1
HbA1c, % (mean ± SD) 5.5 ± 0.5 5.5 ± 0.5 5.6 ± 0.5
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J-MICC Japan multi-institutional collaborative cohort, TMM Tohoku Medical Megabank, BMI body mass index, HbA1c glycated hemoglobin

Genome-wide association study

In the discovery analysis, among the 8,280,776 variants that were adjusted for age, sex, BMI, smoking status, and PCs 1–10, we found 63 and 133 variants that were significantly associated with HbA1c and blood glucose levels (P < 5 × 10−8), respectively (Table S1). The Q–Q plots of the observed P-values are shown in Fig. S1. The mean inflation factors of the genome-wide scan were 1.0221 for HbA1c and 0.9939 for blood glucose levels, indicating that the population structures were slightly inflated and deflated, respectively. Fig. S2 depicts the Manhattan plots of P-values obtained from the genome-wide scan results for HbA1c and blood glucose levels. Most of the results of sensitivity analysis were similar to those shown in Table S1.

Genome-wide association study of normalized HbA1c and blood glucose levels

We compared the results of the re-analysis of normalized HbA1c and blood glucose levels with the most significant variants in each gene region and four GWS variants in TMC6, the results of which shown in Table S1. The results of re-analysis of normalized HbA1c levels showed that the P-values of rs2299620 in KCNQ1 and those of rs2748427, rs3834968, rs2748425, and rs2748424 in TMC6 were 2.11 × 10−7, 1.25 × 10−14, 1.37 × 10−16, 5.80 × 10−17, and 6.54 × 10−17, respectively (Table S2). No significant P-value was obtained upon the re-analysis of normalized blood glucose levels.

Replication study in the TMM Project cohort

We investigated the GWS variants associated with HbA1c and blood glucose levels in the TMM datasets as a replication study. None of the 133 GWS variants associated with blood glucose levels were replicated. Five out of the 63 variants associated with HbA1c levels were replicated in either the TMM10K or TMM67K_JPAV2 dataset, or in both (Table S3). Of the loci, KCNQ1 and TMC6 regions in the discovery analysis were depicted using the LocusZoom (Fig. S3). Replication was observed for rs2299620 in the KCNQ1 gene and for rs2748427, rs3834968, rs2748425, and rs2748424 in the TMC6 gene. The most significant variants among them are presented as lead variants in Table 2.

Table 2.

Replicated lead variants associated with HbA1c levels

SNP Chr Genea Positionb EA NEA Discovery (J-MICC) Replication (TMM10K) Replication (TMM67K_JPAV2)
FRQ β SE P FRQ β SE P FRQ β SE P
rs2299620 11 KCNQ1 2,858,295 T C 0.4208 − 0.0551 0.01 4.219E-08 0.4409 − 0.0231 0.0073 0.00156 0.4276 − 0.0377 0.0034 3.736E-28
rs2748427 17 TMC6 76,121,864 G A 0.1966 0.0708 0.0122 7.647E-09 0.1806 0.077 0.0096 9.2E-16 0.1824 0.046 0.0044 4.972E-26
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We identified significant variants associated with HbA1c levels in J-MICC and TMM datasets. Two replicated lead variants are presented

SNP single nucleotide polymorphism, Chr. Chromosome, EA effect allele, NEA non-effect allele, FRQ effect allele frequency, β the regression coefficient for EA, SE standard error of effect estimate, P association test P-value for each dataset

aGenes have a positional relationship with SNP

bSNP positions are based on the NCBI human genome reference sequence to build GRCh37/hg19

Meta-analysis of the GWS variants

Meta-analysis of the GWS variants in the extended discovery set combining the J-MICC, TMM10K, and TMM67K_JPAV2 datasets revealed that the three variants associated with HbA1c levels, including the two lead variants and rs117299721 in the IGFBP3, LOC730338 gene, were significant (P < 3.1 × 10−3). No variant significantly associated with blood glucose levels was detected in the meta-analysis. The data are listed in Table S4.

Replication analysis of variants previously reported to be associated with HbA1c and blood glucose levels in the BBJ study

We conducted replication analyses to determine whether the previously reported variants associated with HbA1c and blood glucose levels in the BBJ study [16, 17] were significant in the J-MICC. The BBJ had conducted 220 deep-phenotype genome-wide association studies (including diseases, biomarkers, and medication usage) by incorporating past medical history and text-mining of electronic medical records (n = 179,000). Replication analyses were performed for the previously reported variants that ranked among the 10 most significant variants associated with HbA1c and blood glucose levels in the BBJ study, using the J-MICC samples, except for the variants located in the X chromosome. Six out of ten previously reported variants associated with HbA1c levels and three out of ten variants associated with blood glucose levels were confirmed to have significant association (P < 2.5 × 10−3). The variant rs2237897 in KCNQ1 was significantly associated with both HbA1c and blood glucose levels, whereas rs2748427 in TMC6 was significantly associated with only HbA1c levels. The results are presented in Table S5. These results were supported even when the normalized HbA1c and blood glucose levels were re-analyzed.

GWAS of the heterogeneous association of replicated variants with HbA1c levels in BMI-stratified cohorts

Heterogeneity analysis revealed that the two replicated variants in KCNQ1 were differentially associated with HbA1c levels in the BMI ≥ 25 and BMI < 25 groups (P < 5 × 10−2); no such heterogeneous association was noted for the variants in TMC6 (Table 3). These results were found to be valid even when the normalized HbA1c levels were re-analyzed.

Table 3.

GWAS of heterogeneous association of the replicated variants with HbA1c levels in the BMI ≥ 25 and the BMI < 25 groups in J-MICC

SNP Chr Genea Positionb EA NEA BMI ≥ 25 BMI < 25 Directionc HetPVald
EA FRQ β SE P EA FRQ β SE P
rs2299620 11 KCNQ1 2858295 T C 0.4307 − 0.0994 0.0204 1.23E-06 0.4162 − 0.0333 0.0094 0.0003908 − −  0.01465
rs2237897 11 KCNQ1 2858546 T C 0.3795 − 0.0896 0.0205 1.34E-05 0.3738 − 0.0291 0.0094 0.00204 − − 0.02511
rs2748427 17 TMC6 76121864 G A 0.191 0.0828 2.54E-02 0.001111 0.1949 0.0446 0.0115 0.0001114  +  +  0.3662
rs3834968 17 TMC6 76124810 AG A 0.1478 0.1059 2.89E-02 0.0002533 0.1514 0.0582 0.0131 9.44E-06  +  +  0.3333
rs2748425 17 TMC6 76124846 C G 0.1366 0.1137 3.08E-02 0.0002252 0.1411 0.0599 0.0139 1.61E-05  +  +  0.2930
rs2748424 17 TMC6 76124865 G C 0.1353 0.1142 3.08E-02 0.00021 0.1391 0.0603 0.0139 1.46E-05  +  +  0.2909
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Significant HetPVal was lower than 0.05

SNP single nucleotide polymorphism, Chr. Chromosome, EA effect allele, NEA non-effect allele, FRQ frequency, β regression coefficient for EA, SE standard error of effect estimate, P association test P-value in the BMI ≥ 25 and BMI < 25 groups

aGenes have a positional relationship with SNPs

bSNP positions are based on the NCBI human genome reference sequence to build GRCh37/hg19

cSummary of effect direction for each study with one “ + ’ or”−’ per study

dHeterogeneity P-value

Functional annotation

We investigated the eQTL of HbA1c-associated and replicated variants from the GTEx database [42] and found that the variants located at 17q25.3 were significantly associated with TMC6 expression levels in the heart and with TMC8 expression levels in the testes and whole blood (Table S6).

Assessment of LD

The LD degree of the replicated variants in KCNQ1 and TMC6 was assessed using LD maps presented within narrowed regions of chromosomes 11 and 17. Figure S4 shows that all variants were in high LD with others in the region of each gene.

Discussion

One GWS variant in KCNQ1 gene and four GWS variants in TMC6, associated with HbA1c levels in the discovery analysis, were replicated in the TMM datasets, but the variant in KCNQ1 had suggestive P-value in the re-analysis of normalized HbA1c levels. Of those, rs2299620 in KCNQ1 and rs2748427 in TMC6 were significant in the meta-analysis. All GWS variants associated with blood glucose levels in the discovery analysis could be false positives due to non-significance in the re-analysis of normalized blood glucose levels adjusted for fasting time, non-replication, lack of significance in the meta-analysis, and low allele frequency. The significant variant in the meta-analysis, rs117299721 in the IGFBP3, LOC730338 gene associated with HbA1c levels, could also be a false positive owing to non-significance in the re-analysis of normalized HbA1c levels, non-replication, and low allele frequency. Although rs2237897 in KCNQ1 was not GWS in the discovery analysis, it was found to be statistically significant in the replication analysis of previously reported variants associated with both HbA1c and blood glucose levels. This variant also had a GWS association with both HbA1c and blood glucose levels in the TMM databank. The variants rs2237897 in KCNQ1 and rs2748427 in TMC6 were reportedly associated with HbA1c levels in the TMM and BBJ datasets. Although rs2299620 in KCNQ1 was reportedly not associated with HbA1c levels, the high degree of LD between rs2237897 and rs2299620 suggested that they could be coinherited. The absence of an identical GWS variant associated with both HbA1c and blood glucose levels in the discovery analyses could be attributed to the effect of diet on blood glucose levels and/or insufficient statistical power owing to the limited number of subjects with available data on HbA1c and blood glucose levels.

KCNQ1 encodes potassium voltage-gated channel subfamily Q member 1, which is required for the repolarization phase of the cardiac action potential. KCNQ1 is an established type 2 diabetes susceptibility gene. In the Japanese population, rs2299620 in KCNQ1 is associated with type 2 diabetes [44]; however, the mechanism underlying the association has not yet been fully elucidated. In this regard, the maternally derived C allele of rs2299620 in KCNQ1 could be associated with a 28% decrease in insulin secretion (P = 0.002) because of imprinting [45]. Conversely, in the discovery analyses in the present study, the T allele of rs2299620 had a negative association with both HbA1c and blood glucose levels, with effect sizes of − 0.055% and − 1.646 mg/dL, respectively, even though the association of the variant with blood glucose levels was not GWS, only suggestive (P = 2.94 × 10−6 in the discovery analysis of non-normalized blood glucose levels and P = 6.36 × 10−7 in the re-analysis of normalized blood glucose levels adjusted fasting time).

The variation of KCNQ1 rs2237897 genotype has been reported to affect insulin secretion; individuals with the T allele have higher fasting insulin levels compared to those with the C allele [46]. Additionally, the C allele of rs2237897 showed the strongest type-2 diabetes odds ratio difference between BMI ≥ 25 (odds ratio = 1.22, P = 9.94 × 10−10) and BMI < 25 groups (odds ratio = 1.43, P = 1.28 × 10−63) in a large-scale GWAS on Japanese individuals [18]. Correspondingly, the T alleles of rs2237897 and rs2299620 SNPs in KCNQ1 were heterogeneously associated with HbA1c levels between the BMI ≥ 25 and BMI < 25 groups in the J-MICC, whereas no such heterogeneity was observed for any of the TMC6 variants. The decrease in insulin secretion in individuals carrying the C allele of rs2299620 might be because the allele is inherited from the mothers and is affected by imprinting. Insulin stimulates fatty acid and triacylglycerol synthesis and increases the uptake of triglycerides from blood into adipose tissue [47]. Therefore, participants inheriting the T allele of rs2299620 might have a high BMI and low HbA1c levels owing to the high insulin level, whereas participants inheriting the C allele of rs2299620 might have low BMI and high HbA1c levels owing to the low insulin level. The same might apply to the T/C allele variation of rs2237897 in KCNQ1. According to the BioBank datasets, the T allele of rs2237897 was associated with an increased BMI [48].

TMC6 encodes a transmembrane channel-like protein with 10 transmembrane domains and two leucine zipper motifs [49]. The lead TMC6 SNP rs2748427, associated with HbA1c levels, is related to the glycation gap [5]. Transcriptome data showed that rs2748427 was not associated with TMC6 and TMC8 expression levels in purified CD4+ T cells and monocytes and affected arylformamidase (AFMID) gene expression [50]. The transcription start site of AFMID is located at a distance of approximately 62 kb from the lead variant, indicating that the latter might affect AFMID expression. The G allele of rs2748427, which is associated with increased HbA1c levels, was found to be associated with decreased AFMID expression levels [5]. Although Afmid knockout mice showed reduced Afmid gene expression and impaired glucose tolerance, their insulin sensitivity remained unchanged compared to that of wild-type mice. This phenotype resulted from a defect in glucose-stimulated insulin secretion, and the mice showed reduced islet mass in old age [51]. Similarly, GWAS results of the combined J-MICC, TMM, and BBJ datasets showed the G allele of rs2748427 in TMC6 to be significantly associated with increased HbA1c levels, but not blood glucose levels, in the J-MICC (P = 0.742 in the discovery analysis of non-normalized blood glucose levels and P = 0.402 in the re-analysis of normalized blood glucose levels adjusted for fasting time.).

The present study has some limitations. First, the number of participants with available HbA1c and blood glucose data was relatively small in the J-MICC. Second, patients with treated and untreated diabetes were excluded from the cohorts. However, the determination of diabetes and treatment status were self-reported. Third, we analyzed non-fasting blood glucose levels, which may have been affected to a greater extent by diet than fasting blood glucose levels because fasting was not thorough when the blood samples were collected.

In summary, no GWS variant associated with both HbA1c and blood glucose levels was found in the J-MICC. However, rs2237897 in KCNQ1 was found to be significantly associated with both HbA1c and blood glucose levels in the replication analyses of previously reported variants in Japanese cohorts. The G allele of rs2748427 in TMC6 was significantly associated only with HbA1c levels, although it might influence both HbA1c and blood glucose levels differently. The T allele of rs2299620 in KCNQ1 may affect HbA1c levels differently, depending on the BMI. Further investigations are required to clarify the reason underlying the difference in genetic factors regulating HbA1c and blood glucose levels.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 423 KB) (422.8KB, docx)

Acknowledgements

The authors thank the staff of the Laboratory for Genotyping Development, Center for Integrative Medical Sciences at RIKEN, and the staff of the BioBank Japan project. We also thank Dr. Nobuyuki Hamajima and Dr. Hideo Tanaka, the previous principal investigators of the J-MICC study, for their continued support of the current study. This work was supported by Grants-in-Aid for Scientific Research for Priority Areas of Cancer [grant number 17015018], Innovative Areas [grant number 221S0001], and JSPS KAKENHI [grant numbers 17390186, 20390184, 24390165, 16H06277, and 19H03902] from the Japanese Ministry of Education, Culture, Sports, Science, and Technology. This study was also supported, in part, by the BioBank Japan Project of the Japan Agency for Medical Research and Development since April 2015 and the Ministry of Education, Culture, Sports, Science and Technology from April 2003 to March 2015.

Author contributions

Conceptualization, TS and YN; methodology, TS and YN; validation, YS, AS, TH and YO-Y; formal analysis, TS, YN, YS, YO-Y and MN; writing—original draft, TS and YN; writing—review and editing; YN and YS; visualization, YN and MN; supervision, YN; investigation and resources, YN, YS, AS, TH, YO-Y, NT, AK, KM, YK, HI, JO, KT, CS, TK, IW, SS, HN-S, AH, TT, YK, RO, KK, SK-K, TW, ST, CK, IO, YNK, YN, MK, MN, YM, KW and KM. All authors read and approved the final manuscript.

Data availability

Data are available upon reasonable request. Details can be found on the J-MICC Study website (http://www.jmicc.com/).

Declarations

Conflict of interest

Takuya Sakashita is an employee of Takara Bio, Inc., Japan. Dr. Hachiya is a board member of Genome Analytics Japan Inc. Dr. Nakatochi reports receiving grants from Boehringer Ingelheim outside the submitted work. All other authors declare that they have no conflicts of interest associated with this study.

Human rights statement and informed consent

All procedures were in accordance with the technical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1964 and later revisions. Informed consent was obtained from all participants before inclusion into the J-MICC study. The main study protocol of the J-MICC study was approved on July 20, 2005 by the Ethics Committee at Nagoya University School of Medicine (approval number 253).

Footnotes

Publisher's Note

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 423 KB) (422.8KB, docx)

Data Availability Statement

Data are available upon reasonable request. Details can be found on the J-MICC Study website (http://www.jmicc.com/).

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