Integrative Multi-omics Analysis of Childhood Aggressive Behavior

Achenbach TM, Ivanova MY, Rescorla LA (2017) Empirically based assessment and taxonomy of psychopathology for ages 1½–90+ years: developmental, multi-informant, and multicultural findings. Compr Psychiatry 79:4–18. https://doi.org/10.1016/j.comppsych.2017.03.006 Anderson CA, Bushman BJ (2002) Human aggression. Annu Rev Psychol 53:27–51. https://doi.org/10.1146/annurev.psych.53.100901.135231 Barban N, Jansen R, de Vlaming R et al (2016) Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 48:1462–1472. https://doi.org/10.1038/ng.3698 Bartels M, Hendriks A, Mauri M et al (2018) Childhood aggression and the co-occurrence of behavioural and emotional problems: results across ages 3–16 years from multiple raters in six cohorts in the EU-ACTION project. Eur Child Adolesc Psychiatry 27:1105–1121. https://doi.org/10.1007/s00787-018-1169-1 Baselmans BML, Jansen R, Ip HF et al (2019) Multivariate genome-wide analyses of the well-being spectrum. Nat Genet 51:445–451. https://doi.org/10.1038/s41588-018-0320-8 Bates TC, Maher BS, Medland SE et al (2018) The nature of nurture: using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Res Hum Genet 21:73–83. https://doi.org/10.1017/thg.2018.11 Beck JJ, Hottenga J-J, Mbarek H et al (2019) Genetic similarity assessment of twin-family populations by custom-designed genotyping array. Twin Res Hum Genet 22:210–219. https://doi.org/10.1017/thg.2019.41 Bellair PE, McNulty TL, Piquero AR (2019) Persistent material hardship and childhood physical aggression. Aggress Violent Behav 49:101309. https://doi.org/10.1016/j.avb.2019.07.004 Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300 Benyamin B, Pourcain B, Davis OS et al (2014) Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry 19:253–258. https://doi.org/10.1038/mp.2012.184 Bhak Y, Jeong H, Cho YS et al (2019) Depression and suicide risk prediction models using blood-derived multi-omics data. Transl Psychiatry 9:262. https://doi.org/10.1038/s41398-019-0595-2 Boomsma DI (2015) Aggression in children: unravelling the interplay of genes and environment through (epi) genetics and metabolomics. J Pediatr Neonatal Individ Med 4:e040251. https://doi.org/10.7363/040251 Branje S, Geeraerts S, de Zeeuw EL et al (2020) Intergenerational transmission: theoretical and methodological issues and an introduction to four Dutch cohorts. Dev Cogn Neurosci 45:100835. https://doi.org/10.1016/j.dcn.2020.100835 Braun PR, Tanaka-Sahker M, Chan AC et al (2019) Genome-wide DNA methylation investigation of glucocorticoid exposure within buccal samples. Psychiatry Clin Neurosci 73:323–330. https://doi.org/10.1111/pcn.12835 Carter AR, Sanderson E, Hammerton G et al (2021) Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol 36:465–478. https://doi.org/10.1007/s10654-021-00757-1 Cecil CAM, Walton E, Jaffee SR et al (2018a) Neonatal DNA methylation and early-onset conduct problems: a genome-wide, prospective study. Dev Psychopathol 30:383–397. https://doi.org/10.1017/S095457941700092X Cecil CAM, Walton E, Pingault JB et al (2018b) DRD4 methylation as a potential biomarker for physical aggression: an epigenome-wide, cross-tissue investigation. Am J Med Genet Part B Neuropsychiatr Genet 177:746–764. https://doi.org/10.1002/ajmg.b.32689 Chen X, Xu J, Tang J et al (2020) Dysregulation of amino acids and lipids metabolism in schizophrenia with violence. BMC Psychiatry 20:1–11. https://doi.org/10.1186/s12888-020-02499-y Dawiskiba T, Deja S, Mulak A et al (2014) Serum and urine metabolomic fngerprinting in diagnostics of inflammatory bowel diseases. World J Gastroenterol 20:163–174. https://doi.org/10.3748/wjg.v20.i1.163 Dean KR, Hammamieh R, Mellon SH et al (2019) Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder. Mol Psychiatry. https://doi.org/10.1038/s41380-019-0496-z Demontis D, Walters RK, Martin J et al (2019) Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 51:63–75. https://doi.org/10.1038/s41588-018-0269-7 Duruflé H, Selmani M, Ranocha P et al (2020) A powerful framework for an integrative study with heterogeneous omics data: from univariate statistics to multi-block analysis. Brief Bioinform. https://doi.org/10.1093/bib/bbaa166 Ehli EA, Abdellaoui A, Fedko IO et al (2017) A method to customize population-specific arrays for genome-wide association testing. Eur J Hum Genet 25:267–270. https://doi.org/10.1038/ejhg.2016.152 Fergusson DM, Horwood LJ, Ridder EM (2005) Show me the child at seven: the consequences of conduct problems in childhood for psychosocial functioning in adulthood. J Child Psychol Psychiatry Allied Discip 46:837–849. https://doi.org/10.1111/j.1469-7610.2004.00387.x Galili T (2015) dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics 31:3718–3720. https://doi.org/10.1093/bioinformatics/btv428 Glad CAM, Andersson-Assarsson JC, Berglund P et al (2017) Reduced DNA methylation and psychopathology following endogenous hypercortisolism—a genome-wide study. Sci Rep 7:1–11. https://doi.org/10.1038/srep44445 González I, Cao K-AL, Davis MJ, Déjean S (2012) Visualising associations between paired ‘omics’ data sets. BioData Min 5:19. https://doi.org/10.1186/1756-0381-5-19 Grove J, Ripke S, Als TD et al (2019) Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 51:431–444. https://doi.org/10.1038/s41588-019-0344-8 Guillemin C, Provençal N, Suderman M et al (2014) DNA methylation signature of childhood chronic physical aggression in T cells of both men and women. PLoS ONE 9:e86822. https://doi.org/10.1371/journal.pone.0086822 Gulsun M, Oznur T, Aydemir E et al (2016) Possible relationship between amino acids, aggression and psychopathy. Int J Psychiatry Clin Pract 1501:1–10. https://doi.org/10.3109/13651501.2016.1144771 Hagenbeek FA, Kluft C, Hankemeier T et al (2016) Discovery of biochemical biomarkers for aggression: a role for metabolomics in psychiatry. Am J Med Genet Part B Neuropsychiatr Genet 171:719–732. https://doi.org/10.1002/ajmg.b.32435 Hagenbeek FA, Roetman PJ, Pool R et al (2020) Urinary amine and organic acid metabolites evaluated as markers for childhood aggression: the ACTION biomarker study. Front Psychiatry. https://doi.org/10.3389/fpsyt.2020.00165 Hendriks AM, Finkenauer C, Nivard MG et al (2020) Comparing the genetic architecture of childhood behavioral problems across socioeconomic strata in the Netherlands and the United Kingdom. Eur Child Adolesc Psychiatry 29:353–362. https://doi.org/10.1007/s00787-019-01357-x Hompes T, Izzi B, Gellens E et al (2013) Investigating the influence of maternal cortisol and emotional state during pregnancy on the DNA methylation status of the glucocorticoid receptor gene (NR3C1) promoter region in cord blood. J Psychiatr Res 47:880–891. https://doi.org/10.1016/j.jpsychires.2013.03.009 Ip HF, van der Laan CM, Krapohl EML et al (2021) Genetic association study of childhood aggression across raters, instruments, and age. Transl Psychiatry 11:413. https://doi.org/10.1038/s41398-021-01480-x Jakovljevic M, Jakovljevic I (2019) A Transdisciplinary integrative approach for precision psychiatry. In: Kim YK (ed) Frontiers in psychiatry. Springer, Singapore, pp 399–428 Jansen PR, Watanabe K, Stringer S et al (2019) Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat Genet 51:394–403. https://doi.org/10.1038/s41588-018-0333-3 Jiang S, Postovit L, Cattaneo A et al (2019) Epigenetic modifications in stress response genes associated with childhood trauma. Front Psychiatry 10:1–19. https://doi.org/10.3389/fpsyt.2019.00808 Kassing F, Godwin J, Lochman JE, Coie JD (2019) Using early childhood behavior problems to predict adult convictions. J Abnorm Child Psychol 47:765–778. https://doi.org/10.1007/s10802-018-0478-7 Kong A, Thorleifsson G, Frigge ML et al (2018) The nature of nurture: effects of parental genotypes. Science 359:424–428. https://doi.org/10.1126/science.aan6877 Lê Cao KA, Boitard S, Besse P (2011) Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinform. https://doi.org/10.1186/1471-2105-12-253 Lee JJ, Wedow R, Okbay A et al (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50:1112–1121. https://doi.org/10.1038/s41588-018-0147-3 Ligthart L, van Beijsterveldt CEM, Kevenaar ST et al (2019) The Netherlands twin register: longitudinal research based on twin and twin-family designs. Twin Res Hum Genet 22:623–636. https://doi.org/10.1017/thg.2019.93 Lima AR, Pinto J, Amaro F et al (2021) Advances and perspectives in prostate cancer biomarker discovery in the last 5 years through tissue and urine metabolomics. Metabolites 11:181. https://doi.org/10.3390/metabo11030181 Liu J (2004) Malnutrition at age 3 years and externalizing behavior problems at ages 8, 11, and 17 years. Am J Psychiatry 161:2005–2013. https://doi.org/10.1176/appi.ajp.161.11.2005 Liu M, Jiang Y, Wedow R et al (2019) Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51:237–244 Martínez-Reyes I, Chandel NS (2020) Mitochondrial TCA cycle metabolites control physiology and disease. Nat Commun 11:1–11. https://doi.org/10.1038/s41467-019-13668-3 Masud H, Ahmad MS, Cho KW, Fakhr Z (2019) Parenting styles and aggression among young adolescents: a systematic review of literature. Community Ment Health J 55:1015–1030. https://doi.org/10.1007/s10597-019-00400-0 Meng Y, Groth SW (2018) Fathers count: the impact of paternal risk factors on birth outcomes. Matern Child Health J 22:401–408. https://doi.org/10.1007/s10995-017-2407-8 Miller GM, Tolan PH (2019) The influence of parenting practices and neighborhood characteristics on the development of childhood aggression. J Community Psychol 47:135–146. https://doi.org/10.1002/jcop.22105 Mitjans M, Seidel J, Begemann M et al (2019) Violent aggression predicted by multiple pre-adult environmental hits. Mol Psychiatry 24:1549–1564. https://doi.org/10.1038/s41380-018-0043-3 Moran S, Arribas C, Esteller M (2016) Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics 8:389–399. https://doi.org/10.2217/epi.15.114 Mulligan CJ, D’Errico NC, Stees J, Hughes DA (2012) Methylation changes at NR3C1 in newborns associate with maternal prenatal stress exposure and newborn birth weight. Epigenetics 7:853–857. https://doi.org/10.4161/epi.21180 Odintsova VV, Roetman PJ, Ip HF et al (2019) Genomics of human aggression. Psychiatr Genet 29:170–190. https://doi.org/10.1097/YPG.0000000000000239 Ooi M, Nishiumi S, Yoshie T et al (2011) GC/MS-based profiling of amino acids and TCA cycle-related molecules in ulcerative colitis. Inflamm Res 60:831–840. https://doi.org/10.1007/s00011-011-0340-7 Pinu FR, Beale DJ, Paten AM et al (2019) Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites 9:1–31. https://doi.org/10.3390/metabo9040076 R Core Team: A language and environment for statistical computing. https://www.r-project.org/ Radwan K, Coccaro EF (2020) Comorbidity of disruptive behavior disorders and intermittent explosive disorder. Child Adolesc Psychiatry Ment Health 14:24. https://doi.org/10.1186/s13034-020-00330-w Rist MJ, Roth A, Frommherz L et al (2017) Metabolite patterns predicting sex and age in participants of the karlsruhe metabolomics and nutrition (KarMeN) study. PLoS ONE 12:1–21. https://doi.org/10.1371/journal.pone.0183228 Rogers P, Stoner J (2016) Modification of the sandwich estimator in generalized estimating equations with correlated binary outcomes in rare event and small sample settings. Am J Appl Math Stat 3:243–251 Rohart F, Gautier B, Singh A, Lê Cao KA (2017) mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13:1–20. https://doi.org/10.1371/journal.pcbi.1005752 Sanderson E (2021) Multivariable mendelian randomization and mediation. Cold Spring Harb Perspect Med 11:a038984. https://doi.org/10.1101/cshperspect.a038984 Savage JE, Jansen PR, Stringer S et al (2018) Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50:912–919. https://doi.org/10.1038/s41588-018-0152-6 Schicho R, Shaykhutdinov R, Ngo J et al (2012) Quantitative metabolomic profiling of serum, plasma, and urine by 1H NMR spectroscopy discriminates between patients with inflammatory bowel disease and healthy individuals. J Proteome Res 11:3344–3357. https://doi.org/10.1021/pr300139q Selhub J (1999) Homocystein metabolism. Annu Rev Nutr 19:217–246. https://doi.org/10.1146/annurev.nutr.19.1.217 Siever LJ (2008) Neurobiology of aggression and violence. Am J Psychiatry 165:429–442 Singh A, Shannon CP, Gautier B et al (2019) DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35:3055–3062. https://doi.org/10.1093/bioinformatics/bty1054 Sinke L, van Iterson M, Cats D, et al (2019) DNAmArray: Streamlined workflow for the quality control, normalization, and analysis of Illumina methylation array data (2.1). Zenodo. https://molepi.github.io/DNAmArray_workflow/ Subramanian I, Verma S, Kumar S et al (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights. https://doi.org/10.1177/1177932219899051 Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (methodol) 58:267–288 Van Adrichem DS, Huijbregts SCJ, Van Der Heijden KB et al (2020) Aggressive behavior during toddlerhood: interrelated effects of prenatal risk factors, negative affect, and cognition. Child Neuropsychol 26:982–1004. https://doi.org/10.1080/09297049.2020.1769582 van der Laan CM, Morosoli-García JJ, van de Weijer SGA et al (2021) Continuity of genetic risk for aggressive behavior across the life-course. Behav Genet. https://doi.org/10.1007/s10519-021-10076-6 van Dongen J, Hagenbeek FA, Suderman M et al (2021) DNA methylation signatures of aggression and closely related constructs: a meta-analysis of epigenome-wide studies across the lifespan. Mol Psychiatry. https://doi.org/10.1038/s41380-020-00987-x Vaughn M, Salas-Wright C, Naeger S et al (2016) Childhood reports of food neglect and impulse control problems and violence in adulthood. Int J Environ Res Public Health 13:389. https://doi.org/10.3390/ijerph13040389 Vuoksimaa E, Rose RJ, Pulkkinen L et al (2020) Higher aggression is related to poorer academic performance in compulsory education. J Child Psychol Psychiatry. https://doi.org/10.1111/jcpp.13273 Watanabe K, Stringer S, Frei O et al (2019) A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet 51:1339–1348. https://doi.org/10.1038/s41588-019-0481-0 Weaver ICG, Cervoni N, Champagne FA et al (2004) Epigenetic programming by maternal behavior. Nat Neurosci 7:847–854. https://doi.org/10.1038/nn1276 Whipp AM, Korhonen T, Raevuori A et al (2019) Early adolescent aggression predicts antisocial personality disorder in young adults: a population-based study. Eur Child Adolesc Psychiatry 28:341–350. https://doi.org/10.1007/s00787-018-1198-9 Whipp AM, Vuoksimaa E, Bolhuis K et al (2021a) Teacher-rated aggression and co-occurring behaviors and emotional problems among schoolchildren in four population-based European cohorts. PLoS ONE 16:e0238667. https://doi.org/10.1371/journal.pone.0238667 Whipp AM, Vuoksimaa E, Korhonen T et al (2021b) Ketone body 3-hydroxybutyrate as a biomarker of aggression. Sci Rep 11:5813. https://doi.org/10.1038/s41598-021-84635-6 Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M (2021) Multi-omics integration in biomedical research—a metabolomics-centric review. Anal Chim Acta 1141:144–162. https://doi.org/10.1016/j.aca.2020.10.038 Wray NR, Ripke S, Mattheisen M et al (2018) Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50:668–681. https://doi.org/10.1038/s41588-018-0090-3 Wrigglesworth J, Ancelin ML, Ritchie K, Ryan J (2019) Association between DNA methylation of the KITLG gene and cortisol levels under stress: a replication study. Stress 22:162–168. https://doi.org/10.1080/10253890.2018.1519019 Wu Y, Zhong X, Lin Y et al (2021) Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies. Proc Natl Acad Sci 118:e2023184118. https://doi.org/10.1073/pnas.2023184118 Xiong Z, Yang F, Li M et al (2022) EWAS open platform: integrated data, knowledge and toolkit for epigenome-wide association study. Nucleic Acids Res 50:D1004–D1009. https://doi.org/10.1093/nar/gkab972 Yuan Y, Zhao J, Li T et al (2021) Integrative metabolic profile of myelodysplastic syndrome based on UHPLC–MS. Biomed Chromatogr. https://doi.org/10.1002/bmc.5136