The evolving field of multi-omics combines data and provides methods for simultaneous analysis across several omics levels. Here, we integrated genomics (transmitted and non-transmitted polygenic scores [PGSs]), epigenomics, and metabolomics data in a multi-omics framework to identify biomarkers for Attention-Deficit/Hyperactivity Disorder (ADHD) and investigated the connections among the three omics levels. We first trained single- and next multi-omics models to differentiate between cases and controls in 596 twins (cases = 14.8%) from the Netherlands Twin Register (NTR) demonstrating reasonable in-sample prediction through cross-validation. The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations of ADHD with glucocorticoid exposure and the transmembrane protein family TMEM, show that the DNA methylation of the MAD1L1 gene associated with ADHD has a relation with parental smoking behavior, and present novel findings including associations between indirect genetic effects and CpGs of the STAP2 gene. However, out-of-sample prediction in NTR participants (N = 258, cases = 14.3%) and in a clinical sample (N = 145, cases = 51%) did not perform well (range misclassification was [0.40, 0.57]). The results highlighted connections between omics levels, with the strongest connections between non-transmitted PGSs, CpGs, and amino acid levels and show that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.

中文翻译：

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对基因组、表观基因组和代谢组学数据的综合多组学分析为注意力缺陷/多动障碍提供了新的见解

不断发展的多组学领域结合了数据，并提供了跨多个组学级别同时分析的方法。在这里，我们将基因组学（传播性和非传播性多基因评分 [PGS]）、表观基因组学和代谢组学数据整合到多组学框架中，以确定注意力缺陷/多动障碍 (ADHD) 的生物标志物，并研究了三个组学之间的联系水平。我们首先训练单组学和下一个多组学模型，以区分来自荷兰双胞胎登记处 (NTR) 的 596 例双胞胎（病例 = 14.8%）的病例和对照，通过交叉验证证明了合理的样本内预测。多组学模型选择了 30 个 PGS、143 个 CpG 和 90 个代谢物。我们证实了先前 ADHD 与糖皮质激素暴露和跨膜蛋白家族TMEM 的关联，表明与ADHD 相关的MAD1L1基因的 DNA 甲基化与父母吸烟行为有关，并提出了新的发现，包括间接遗传效应与 CpG 之间的关联STAP2基因。然而，NTR 参与者（N  = 258，病例 = 14.3%）和临床样本（N  = 145，病例 = 51%）的样本外预测表现不佳（范围错误分类为 [0.40, 0.57]） 。结果强调了组学水平之间的联系，其中非传播性 PGS、CpG 和氨基酸水平之间的联系最强，并表明考虑相互关联的组学水平的多组学设计可以帮助揭示 ADHD 背后的复杂生物学。