@article {477, title = {A multi-omics data analysis workflow packaged as a FAIR Digital Object}, journal = {GigaScience}, volume = {13}, year = {2024}, month = {01}, pages = {giad115}, abstract = {

Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object.We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub.Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

}, issn = {2047-217X}, doi = {10.1093/gigascience/giad115}, url = {https://doi.org/10.1093/gigascience/giad115}, author = {Niehues, Anna and de~Visser, Casper and Hagenbeek, Fiona A and Kulkarni, Purva and Pool, Ren{\'e} and Karu, Naama and Kindt, Alida S D and Singh, Gurnoor and Vermeiren, Robert R J M and Boomsma, Dorret I and van~Dongen, Jenny and {\textquoteright}t~Hoen, Peter A C and van~Gool, Alain J} } @article {456, title = {Integrative multi-omics analysis of childhood aggressive behavior}, journal = {Behavior Genetics}, volume = {53}, year = {2023}, pages = {101{\textendash}117}, abstract = {

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases\ =\ 42\%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N\ =\ 277, cases\ =\ 42\%) and independent clinical data (N\ =\ 142, cases\ =\ 45\%) ranged from 43 to 57\%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

}, keywords = {childhood aggression, DNA methylation, Genetic nurturing, metabolomics, Multi-omics, polygenic scores}, doi = {10.1007/s10519-022-10126-7}, author = {Hagenbeek, Fiona A and van Dongen, Jenny and Pool, Ren{\'e} and Roetman, Peter J and Harms, Amy C and Hottenga, Jouke Jan and Kluft, Cornelis and Colins, Olivier F and van Beijsterveldt, Catharina E M and Fanos, Vassilios and Ehli, Erik A and Hankemeier, Thomas and Vermeiren, Robert R J M and Bartels, Meike and D{\'e}jean, S{\'e}bastien and Boomsma, Dorret I} } @article {471, title = {Integrative multi-omics analysis of genomic, epigenomic, and metabolomics data leads to new insights for Attention-Deficit/Hyperactivity Disorder}, journal = {American Journal of Medical Genetics Part B: Neuropsychiatric Genetics}, year = {2023}, pages = {e32955}, 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\ =\ 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.

}, keywords = {ADHD, DNA methylation, genetic nurture, metabolites, Multi-omics, polygenic scores}, doi = {10.1002/ajmg.b.32955}, author = {Hubers, Nikki and Hagenbeek, Fiona A and Pool, Ren{\'e} and D{\'e}jean, S{\'e}bastien and Harms, Amy C and Roetman, Peter J and van Beijsterveldt, Catharina E M and Fanos, Vassilios and Ehli, Erik A and Vermeiren, Robert R J M and Bartels, Meike and Hottenga, Jouke Jan and Hankemeier, Thomas and van Dongen, Jenny and Boomsma, Dorret I} } @article {457, title = {Heritability of urinary amines, organic acids, and steroid hormones in children}, journal = {Metabolites}, volume = {12}, year = {2022}, pages = {474}, abstract = {

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7-12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The monozygotic (MZ) and dizygotic (DZ) correlations for the urinary metabolites indicated a role for non-additive genetic factors for 50 amines, 13 organic acids, and 6 steroids. The average broad-sense heritability for these amines, organic acids, and steroids was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81), respectively. For 6 amines, 7 organic acids, and 4 steroids the twin correlations indicated a role for shared environmental factors and the average narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (range; 0.23-0.61), and 0.47 (range: 0.32-0.70) for these amines, organic acids, and steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

}, keywords = {amines, children, classical twin design, heritability, metabolites, organic acids, steroid hormones, urine}, doi = {10.3390/metabo12060474}, author = {Hagenbeek, Fiona A and van Dongen, Jenny and Pool, Ren{\'e} and Harms, Amy C and Roetman, Peter J and Fanos, Vassilios and van Keulen, Britt J and Walker, Brian R and Karu, Naama and Hulshoff Pol, Hilleke E and Rotteveel, Joost and Finken, Martijn J J and Vermeiren, Robert R J M and Kluft, Cornelis and Bartels, Meike and Hankemeier, Thomas and Boomsma, Dorret I} } @article {426, title = {Urinary Amine and organic acid metabolites evaluated as markers for childhood aggression: The ACTION biomarker study}, journal = {Frontiers in Psychiatry}, volume = {11}, year = {2020}, pages = {165}, abstract = {

Biomarkers are of interest as potential diagnostic and predictive instruments in personalized medicine. We present the first urinary metabolomics biomarker study of childhood aggression. We aim to examine the association of urinary metabolites and neurotransmitter ratios involved in key metabolic and neurotransmitter pathways in a large cohort of twins (N = 1,347) and clinic-referred children (N = 183) with an average age of 9.7 years. This study is part of ACTION (Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies), in which we developed a standardized protocol for large-scale collection of urine samples in children. Our analytical design consisted of three phases: a discovery phase in twins scoring low or high on aggression (N = 783); a replication phase in twin pairs discordant for aggression (N = 378); and a validation phase in clinical cases and matched twin controls (N = 367). In the discovery phase, 6 biomarkers were significantly associated with childhood aggression, of which the association of O-phosphoserine ($\beta$ = 0.36; SE = 0.09; p = 0.004), and gamma-L-glutamyl-L-alanine ($\beta$ = 0.32; SE = 0.09; p = 0.01) remained significant after multiple testing. Although non-significant, the directions of effect were congruent between the discovery and replication analyses for six biomarkers and two neurotransmitter ratios and the concentrations of 6 amines differed between low and high aggressive twins. In the validation analyses, the top biomarkers and neurotransmitter ratios, with congruent directions of effect, showed no significant associations with childhood aggression. We find suggestive evidence for associations of childhood aggression with metabolic dysregulation of neurotransmission, oxidative stress, and energy metabolism. Although replication is required, our findings provide starting points to investigate causal and pleiotropic effects of these dysregulations on childhood aggression.

}, keywords = {amines, biomarkers, childhood aggression, metabolites, metabolomics, neurotransmitters, organic acids, oxidative stress}, doi = {10.3389/fpsyt.2020.00165}, author = {Hagenbeek, Fiona A and Roetman, Peter J and Pool, Ren{\'e} and Kluft, Cornelis and Harms, Amy C and van Dongen, Jenny and Colins, Olivier F and Talens, Simone and van Beijsterveldt, Catharina E M and Vandenbosch, Marjolein M L J Z and de Zeeuw, Eveline L and D{\'e}jean, S{\'e}bastien and Fanos, Vassilios and Ehli, Erik A and Davies, Gareth E and Hottenga, Jouke Jan and Hankemeier, Thomas and Bartels, Meike and Vermeiren, Robert R J M and Boomsma, Dorret I} } @article {413, title = {Genomics of human aggression: current state of genome-wide studies and an automated systematic review tool}, journal = {Psychiatric Genetics}, volume = {29}, year = {2019}, pages = {170{\textendash}190}, abstract = {

There are substantial differences, or variation, between humans in aggression, with its molecular genetic basis mostly unknown. This review summarizes knowledge on the genetic contribution to variation in aggression with the following three foci: (1) a comprehensive overview of reviews on the genetics of human aggression, (2) a systematic review of genome-wide association studies (GWASs), and (3) an automated tool for the selection of literature based on supervised machine learning. The phenotype definition \&$\#$39;aggression\&$\#$39; (or \&$\#$39;aggressive behaviour\&$\#$39;, or \&$\#$39;aggression-related traits\&$\#$39;) included anger, antisocial behaviour, conduct disorder, and oppositional defiant disorder. The literature search was performed in multiple databases, manually and using a novel automated selection tool, resulting in 18 reviews and 17 GWASs of aggression. Heritability estimates of aggression in children and adults are around 50\%, with relatively small fluctuations around this estimate. In 17 GWASs, 817 variants were reported as suggestive (P \≤ 1.0E), including 10 significant associations (P \≤ 5.0E). Nominal associations (P \≤ 1E) were found in gene-based tests for genes involved in immune, endocrine, and nervous systems. Associations were not replicated across GWASs. A complete list of variants and their position in genes and chromosomes are available online. The automated literature search tool produced literature not found by regular search strategies. Aggression in humans is heritable, but its genetic basis remains to be uncovered. No sufficiently large GWASs have been carried out yet. With increases in sample size, we expect aggression to behave like other complex human traits for which GWAS has been successful.

}, doi = {10.1097/YPG.0000000000000239}, author = {Odintsova, Veronika V and Roetman, Peter J and Ip, Hill F and Pool, Ren{\'e} and Van der Laan, Camiel M and Tona, Klodiana-Daphne and Vermeiren, Robert R J M and Boomsma, Dorret I} }