ArticleInternational journal of obesity (2005)2026
Revealing genetic foundations underlying cardiometabolic traits: integrating twin and GWAS data in Asian populations.
Article in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTraditional twin studies have reported high heritability estimates for common cardiometabolic traits, yet discrepancies compared to genome-wide association studies (GWAS) derived estimates underscore the effects of "missing heritability", potentially attributed to genetic interactions and environmental influences. However, research exploring potential contributors to missing heritability in cardiometabolic phenotypes remains scarce and largely confined to European populations.
methodsWe analyzed twin data (n = 2548) from the Chinese National Twin Registry (CNTR) and GWAS summary-level data (n = 92,615) from Asian cohorts to estimate the heritability of 10 cardiometabolic traits: BMI, waist-to-hip ratio (WHR), hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Using a twin study design, we quantified dominance genetic effects and explored gene-environment interactions involving age, lifestyle, and psychosocial factors on these traits. Additionally, we leveraged both twin and GWAS data to investigate genetic correlations between cardiometabolic traits, revealing shared genetic underpinnings.
resultsTwin data revealed moderate to high heritability estimates (0.34-0.72) for cardiometabolic traits, whereas GWAS data yielded lower estimates (0.10-0.22). Dominant genetic variance was statistically significant only for BMI (0.42), suggesting a limited role for dominance across most cardiometabolic traits examined in this study. No evidence of non-additive effects was observed in GWAS summary statistics. Gene-environment interaction analyses identified 12 small yet significant interaction effects involving age, smoking, drinking, education, and mental health. Finally, genetic correlations between pairs of cardiometabolic traits ranged from low to high (0.09-0.90), with broadly consistent patterns observed across twin and GWAS approaches.
conclusionsThese findings collectively advance our understanding of the genetic architecture underlying cardiometabolic traits and their interrelationships in Asian populations.
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