Davis Christal N, Khan Yousef, Toikumo Sylvanus, Jinwala Zeal, Boomsma Dorret I, Levey Daniel F, Gelernter Joel, Kember Rachel L, Kranzler Henry R
Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Psychol Med. 2025 May 8;55:e138. doi: 10.1017/S0033291725000856.
There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP).
We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in individuals genetically similar to European reference panels (EUR-like; n = 16,400 to 1,074,629). Traits included clinical (e.g. major depressive disorder, alcohol use disorder) and subclinical measures (e.g. risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors.
A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r = 0.37, SE = 0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE = 3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits.
The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.
外化性（EXT）和内化性（INT）精神病理学之间存在相当多的共病情况。了解这些谱系的共同遗传基础对于推进对其生物学基础的认识以及为诸如研究领域标准（RDoC）和精神病理学层次分类法（HiTOP）等实证模型提供信息至关重要。
我们将基因组结构方程模型应用于与欧洲参考面板基因相似的个体（类欧洲人；n = 16400至1074629）中16种EXT和INT特征的汇总统计数据。特征包括临床指标（如重度抑郁症、酒精使用障碍）和亚临床指标（如风险承受能力、易怒性）。我们测试了五个验证性因素模型，以确定最佳拟合且最简约的遗传结构，然后对所得潜在因素进行多变量全基因组关联研究（GWAS）。
一个代表EXT和INT谱系的双因素相关模型对数据拟合最佳。EXT和INT之间存在中等程度的遗传相关性（r = 0.37，SE = 0.02），双变量因果混合模型显示两个谱系的因果变异存在广泛重叠（94.64%，SE = 3.27）。多变量GWAS确定了409个EXT的主要遗传变异、85个INT的主要遗传变异以及256个共享特征的主要遗传变异。
此处确定的EXT和INT的共同遗传负荷有助于描述这些经常共病的精神病理学形式背后的遗传结构。这些发现为未来旨在理解精神病理学背后共同和独特生物学机制的研究提供了一个框架，这将有助于完善精神疾病分类系统，并可能为治疗方法提供参考。
