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

TitleIntegrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder
Publication TypeJournal Article
Year of Publication2023
AuthorsHubers, N, Hagenbeek, FA, Pool, R, Déjean, S, Harms, AC, Roetman, PJ, van Beijsterveldt, CEM, Fanos, V, Ehli, EA, Vermeiren, RRJM, Bartels, M, Hottenga, JJan, Hankemeier, T, van Dongen, J, Boomsma, DI
JournalAmerican Journal of Medical Genetics Part B: Neuropsychiatric Genetics
Paginatione32955
KeywordsADHD, DNA methylation, genetic nurture, metabolites, Multi-omics, polygenic scores
Abstract

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 = 25

DOI10.1002/ajmg.b.32955