Hubers Nikki, Hagenbeek Fiona A, Pool René, Déjean Sébastien, Harms Amy C, Roetman Peter J, van Beijsterveldt Catharina E M, Fanos Vassilios, Ehli Erik A, Vermeiren Robert R J M, Bartels Meike, Hottenga Jouke Jan, Hankemeier Thomas, van Dongen Jenny, Boomsma Dorret I
Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands.
Am J Med Genet B Neuropsychiatr Genet. 2024 Mar;195(2):e32955. doi: 10.1002/ajmg.b.32955. Epub 2023 Aug 3.
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.
不断发展的多组学领域整合了数据，并提供了跨多个组学层面进行同步分析的方法。在此，我们将基因组学（传递和非传递多基因评分[PGS]）、表观基因组学和代谢组学数据整合到一个多组学框架中，以识别注意力缺陷多动障碍（ADHD）的生物标志物，并研究这三个组学层面之间的联系。我们首先训练了单组学和接下来的多组学模型，以区分来自荷兰双胞胎登记处（NTR）的596对双胞胎（病例=14.8%）中的病例和对照，通过交叉验证证明样本内预测合理。多组学模型选择了30个PGS、143个CpG和90种代谢物。我们证实了ADHD与糖皮质激素暴露以及跨膜蛋白家族TMEM的先前关联，表明与ADHD相关的MAD1L1基因的DNA甲基化与父母吸烟行为有关，并呈现了新的发现，包括间接遗传效应与STAP2基因的CpG之间的关联。然而，在NTR参与者（N = 258，病例= 14.3%）和临床样本（N = 145，病例= 51%）中的样本外预测表现不佳（错误分类范围为[0.40, 0.57]）。结果突出了组学层面之间的联系，其中非传递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层面的多组学设计有助于揭示ADHD背后复杂的生物学机制。
