Blood-based genome-wide DNA methylation correlations across body-fat- and adiposity-related biochemical traits

德纳姆 生物 DNA甲基化 遗传学 二元分析 特质 表观遗传学 基因 统计 数学 计算机科学 基因表达 程序设计语言
作者
Alesha Hatton,Robert F. Hillary,Elena Bernabeu,Daniel L. McCartney,Riccardo E. Marioni,Allan F. McRae
出处
期刊:American Journal of Human Genetics [Elsevier]
卷期号:110 (9): 1564-1573 被引量:1
标识
DOI:10.1016/j.ajhg.2023.08.004
摘要

The recent increase in obesity levels across many countries is likely to be driven by nongenetic factors. The epigenetic modification DNA methylation (DNAm) may help to explore this, as it is sensitive to both genetic and environmental exposures. While the relationship between DNAm and body-fat traits has been extensively studied, there is limited literature on the shared associations of DNAm variation across such traits. Akin to genetic correlation estimates, here, we introduce an approach to evaluate the similarities in DNAm associations between traits: DNAm correlations. As DNAm can be both a cause and consequence of complex traits, DNAm correlations have the potential to provide insights into trait relationships above that currently obtained from genetic and phenotypic correlations. Utilizing 7,519 unrelated individuals from Generation Scotland with DNAm from the EPIC array, we calculated DNAm correlations between body-fat- and adiposity-related traits by using the bivariate OREML framework in the OSCA software. For each trait, we also estimated the shared contribution of DNAm between sexes. We identified strong, positive DNAm correlations between each of the body-fat traits (BMI, body-fat percentage, and waist-to-hip ratio, ranging from 0.96 to 1.00), finding larger associations than those identified by genetic and phenotypic correlations. We identified a significant deviation from 1 in the DNAm correlations for BMI between males and females, with sex-specific DNAm changes associated with BMI identified at eight DNAm probes. Employing genome-wide DNAm correlations to evaluate the similarities in the associations of DNAm with complex traits has provided insight into obesity-related traits beyond that provided by genetic correlations.

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