Twin study: genotype-dependent epigenetic factors associated with HbA1c levels

BACKGROUND: Hemoglobin A1c (HbA1c) is a biomarker for diabetes mellitus. Twin studies suggest substantial heritability, but the molecular basis of non-genetic variation remains unclear. We aimed to explore genetic and epigenetic factors associated with HbA1c through multi-omics analysis of monozygotic twins. MATERIALS AND METHODS: A total of 285 monozygotic and 27 dizygotic twin pairs were enrolled in Japan. Genome-wide single-nucleotide variant (SNV) genotyping, DNA methylation profiling, and RNA sequencing were performed. Based on HbA1c levels, twin pairs were classified as high concordant, low concordant, or discordant. Structural equation modeling estimated heritability, and GWAS, within-pair methylation comparisons, and expression-methylation correlation analyses were conducted. RESULTS: Our analysis estimated that genetic factors accounted for 68% of the phenotypic variance in covariate-adjusted HbA1c. Within-pair methylation comparisons revealed several suggestive CpG sites associated with HbA1c variation. GWAS revealed one suggestive SNV associated with higher HbA1c and three SNVs potentially associated with susceptibility to HbA1c variation. Genotype-stratified analyses showed exploratory genetic background-dependent methylation changes at CpG sites, some of which correlated with gene expression. CONCLUSION: This study estimated the heritability of adjusted HbA1c and revealed preliminary genetic and epigenetic signals in Japanese twins. Further studies are needed to confirm their biological and clinical significance.

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Journal
Epigenomics
Published
2026-09-29
DOI
https://doi.org/10.1080/17501911.2026.2738328
Primary Topic
Genetic Associations and Epidemiology
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article
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article

Twin study: genotype-dependent epigenetic factors associated with HbA1c levels

Mika Hasegawa, Ritsuko Ozaki, Yuya Arakawa, Shiho Kato et al.
Epigenomics
Genetic Associations and Epidemiology
article

Twin study: genotype-dependent epigenetic factors associated with HbA1c levels

Mika Hasegawa, Ritsuko Ozaki, Yuya Arakawa, Shiho Kato, Hinako Hashimoto, Mikio Watanabe, Hiromichi Ueda, Saho Mori, Saki Yoshioka, Osaka Twin Research Group
article en

Abstract

BACKGROUND: Hemoglobin A1c (HbA1c) is a biomarker for diabetes mellitus. Twin studies suggest substantial heritability, but the molecular basis of non-genetic variation remains unclear. We aimed to explore genetic and epigenetic factors associated with HbA1c through multi-omics analysis of monozygotic twins. MATERIALS AND METHODS: A total of 285 monozygotic and 27 dizygotic twin pairs were enrolled in Japan. Genome-wide single-nucleotide variant (SNV) genotyping, DNA methylation profiling, and RNA sequencing were performed. Based on HbA1c levels, twin pairs were classified as high concordant, low concordant, or discordant. Structural equation modeling estimated heritability, and GWAS, within-pair methylation comparisons, and expression-methylation correlation analyses were conducted. RESULTS: Our analysis estimated that genetic factors accounted for 68% of the phenotypic variance in covariate-adjusted HbA1c. Within-pair methylation comparisons revealed several suggestive CpG sites associated with HbA1c variation. GWAS revealed one suggestive SNV associated with higher HbA1c and three SNVs potentially associated with susceptibility to HbA1c variation. Genotype-stratified analyses showed exploratory genetic background-dependent methylation changes at CpG sites, some of which correlated with gene expression. CONCLUSION: This study estimated the heritability of adjusted HbA1c and revealed preliminary genetic and epigenetic signals in Japanese twins. Further studies are needed to confirm their biological and clinical significance.

Epigenomics
The University of Osaka (JP)
Good health and well-being
Openalex Percentile: Top 12%
Genetic Associations and Epidemiology
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Twin study: genotype-dependent epigenetic factors associated with HbA1c levels — Mika Hasegawa, Ritsuko Ozaki, et al. · Epigenomics (2026) | TGRS Research Map | TGRS