Taylor Jacquelyn Y, Ware Erin B, Wright Michelle L, Smith Jennifer A, Kardia Sharon L R
1 New York University, New York, NY, USA.
2 Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
Biol Res Nurs. 2019 May;21(3):279-285. doi: 10.1177/1099800419828486. Epub 2019 Feb 19.
With the rapid advancement of omics-based research, particularly big data such as genome- and epigenome-wide association studies that include extensive environmental and clinical variables, data analytics have become increasingly complex. Researchers face significant challenges regarding how to analyze multifactorial data and make use of the findings for clinical translation. The purpose of this article is to provide a scientific exemplar for use of genetic burden scores as a data analysis method for studies with both genotype and DNA methylation data in which the goal is to evaluate associations with chronic conditions such as metabolic syndrome (MetS). This study included 739 African American men and women from the Genetic Epidemiology Network of Arteriopathy Study who met diagnostic criteria for MetS and had available genetic and epigenetic data. Genetic burden scores for evaluated genes were not significant after multiple testing corrections, but DNA methylation at 2 CpG sites (dihydroorotate dehydrogenase cg22381196 pFDR = .014; CTNNA3 cg00132141 pFDR = .043) was significantly associated with MetS after controlling for multiple comparisons. Interactions between the marginally significant CpG sites and burden scores, however, were not significant. More work is required in this area to identify intermediate biological pathways influenced by environmental, genetic, and epigenetic variation that may explain the high prevalence of MetS among African Americans. This study does serve, however, as an example of the use of the genetic burden score as an alternative data analysis approach for complex studies involving the analysis of genetic and epigenetic data simultaneously.
随着基于组学的研究迅速发展，特别是诸如包含广泛环境和临床变量的全基因组和表观基因组关联研究等大数据，数据分析变得越来越复杂。研究人员在如何分析多因素数据以及如何将研究结果用于临床转化方面面临重大挑战。本文的目的是提供一个科学范例，说明如何使用遗传负担评分作为一种数据分析方法，用于同时包含基因型和DNA甲基化数据的研究，其目标是评估与代谢综合征（MetS）等慢性病的关联。本研究纳入了动脉病遗传流行病学网络研究中的739名非裔美国男性和女性，他们符合MetS的诊断标准且有可用的遗传和表观遗传数据。经过多重检验校正后，评估基因的遗传负担评分不显著，但在控制多重比较后，2个CpG位点（二氢乳清酸脱氢酶cg22381196，pFDR = 0.014；CTNNA3 cg00132141，pFDR = 0.043）的DNA甲基化与MetS显著相关。然而，边缘显著的CpG位点与负担评分之间的相互作用并不显著。该领域需要开展更多工作，以确定受环境、遗传和表观遗传变异影响的中间生物学途径，这些途径可能解释非裔美国人中MetS的高患病率。不过，本研究确实为使用遗传负担评分作为一种替代数据分析方法提供了一个范例，该方法适用于同时涉及遗传和表观遗传数据分析的复杂研究。
