Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder

Abstract

The evolving field of multi-omics combines data across omics levels and provides methods for simultaneous analysis. We integrated genomics (transmitted and non-transmitted polygenic scores), epigenomics and metabolomics data in a multi-omics framework to identify biomarkers for ADHD and investigated the connections among omics levels. We trained single- and 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. 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: 0.40-0.57). The multi-omics model selected 30 PGSs, 143 CpGs, and 90 metabolites. We confirmed previous associations with ADHD such as 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. The results highlighted connections between omics levels, with the strongest connections between indirect genetic effects, CpGs, and amino acid levels. Our study shows that multi-omics designs considering interrelated omics levels can help unravel the complex biology underlying ADHD.