Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes.

Additive and Interactive Genetically Contextual Effects of HbA1c on cg19693031 Methylation in Type 2 Diabetes.
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DOI:
10.3390/genes13040683
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发表时间:
2022-04-13
期刊:
影响因子:
3.5
通讯作者:
Philibert, Robert
Philibert, Robert
中科院分区:
生物学3区
文献类型:
--
作者:
Dawes, Kelsey;Philibert, Willem;Darbro, Benjamin;Simons, Ronald L.;Philibert, Robert

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2型糖尿病(T2D)具有复杂的遗传和环境结构,是其发展和临床表现的基础。尽管已经确定了与T2D相关的一百多种遗传变异和CpG位点,但尚未开发出可用于预防或预防临床疾病的强大生物标记。基于潜在遗传变异独立或与环境暴露联合影响DNA甲基化(DNAm)的前提,我们评估了局部和远端基因x甲基化(GxMeth)相互作用的能力,以改进非裔美国人队列中预测T2D状态的cg19693031模型。使用来自506名受试者的全基因组遗传数据,我们共确定了1476个与HbA1c值相关的GxMeth术语。GxMeth snp映射到与T2D发展和并发症相关的生物学途径,仅在糖尿病受试者中观察到两个GxMeth snp的甲基化遗传背景差异(rs2390998 AG vs. GG, p = 4.63 × 10−11,Δβ = 13%,效应值= 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10−4,Δβ = 9%,效应值= 0.38 [95% CI = 0.12, 0.56])。使用重复分层k-fold交叉验证方法,构建了一系列随机欠采样的平衡随机森林分类器,以评估在cg19693031模型中添加GxMeth项以区分血糖控制正常与T2D受试者。将结果与仅包含协变量(年龄、性别和BMI)并添加cg19693031的模型所得结果进行比较。我们发现包含10个GxMeth snp和cg19693031的后剪枝分类器可预测T2D状态,阳性目标类的AUC、灵敏度、特异性和精度分别为0.76、0.81、0.70和0.63。相比之下,使用协变量和cg19693031的AUC、敏感性、特异性和精密度分别仅为0.71、0.74、0.67和0.59。总的来说,我们证明纠正遗传混淆的cg19693031提高了其检测2型糖尿病的能力。我们的结论是,综合遗传-表观遗传方法可以为更有效预防和治疗T2D的个性化医疗规划提供信息。
Type 2 diabetes mellitus (T2D) has a complex genetic and environmental architecture that underlies its development and clinical presentation. Despite the identification of well over a hundred genetic variants and CpG sites that associate with T2D, a robust biosignature that could be used to prevent or forestall clinical disease has not been developed. Based on the premise that underlying genetic variation influences DNA methylation (DNAm) independently of or in combination with environmental exposures, we assessed the ability of local and distal gene x methylation (GxMeth) interactive effects to improve cg19693031 models for predicting T2D status in an African American cohort. Using genome-wide genetic data from 506 subjects, we identified a total of 1476 GxMeth terms associated with HbA1c values. The GxMeth SNPs map to biological pathways associated with the development and complications of T2D, with genetically contextual differences in methylation observed only in diabetic subjects for two GxMeth SNPs (rs2390998 AG vs. GG, p = 4.63 × 10−11, Δβ = 13%, effect size = 0.16 [95% CI = 0.05, 0.32]; rs1074390 AA vs. GG, p = 3.93 × 10−4, Δβ = 9%, effect size = 0.38 [95% CI = 0.12, 0.56]. Using a repeated stratified k-fold cross-validation approach, a series of balanced random forest classifiers with random under-sampling were built to evaluate the addition of GxMeth terms to cg19693031 models to discriminate between normoglycemic controls versus T2D subjects. The results were compared to those obtained from models incorporating only the covariates (age, sex and BMI) and the addition of cg19693031. We found a post-pruned classifier incorporating 10 GxMeth SNPs and cg19693031 adjusted for covariates predicted the T2D status, with the AUC, sensitivity, specificity and precision of the positive target class being 0.76, 0.81, 0.70 and 0.63, respectively. Comparatively, the AUC, sensitivity, specificity and precision using the covariates and cg19693031 were only 0.71, 0.74, 0.67 and 0.59, respectively. Collectively, we demonstrate correcting for genetic confounding of cg19693031 improves its ability to detect type 2 diabetes. We conclude that an integrated genetic–epigenetic approach could inform personalized medicine programming for more effective prevention and treatment of T2D.
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发表时间: 2021-02-23
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