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.

本研究介绍并说明了综合多组学方法在研究儿童攻击行为等复杂特征的潜在生物学方面的潜力。在 645 对双胞胎（病例数 = 42%）中，我们训练了单组学和综合多组学模型来识别亚临床攻击的生物标志物，并研究了这些生物标志物之间的联系。我们的数据包括 15 个性状、78,772 个 CpG 和 90 个代谢物的传播和两个非传播多基因评分 (PGS)。单组学模型选择了 31 个 PGS、1614 个 CpG 和 90 个代谢物，而多组学模型包含 44 个 PGS、746 个 CpG 和 90 个代谢物。这些模型在测试中的预测准确性（N  = 277，病例 = 42%）和独立的临床数据（N = 142，案例 = 45%)，范围从 43% 到 57%。我们观察到 DNA 甲基化、氨基酸和 ADHD、自闭症谱系障碍、智力、吸烟开始和自我报告的健康状况的父母非传播 PGS 之间存在密切联系。与攻击相关的组学特征与已知和新的风险因素有关，包括炎症、致癌物和吸烟。