Hillclimb-Causal Inference: a data-driven approach to identify causal pathways among parental behaviors, genetic risk, and externalizing behaviors in children.

IF 4.6 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mengman Wei, Qian Peng
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引用次数: 0

Abstract

Objectives: Externalizing behaviors in children, such as aggression, hyperactivity, and defiance, are influenced by complex interplays between genetic predispositions and environmental factors, particularly parental behaviors. Unraveling these intricate causal relationships can benefit from the use of robust data-driven methods.

Materials and methods: We developed "Hillclimb-Causal Inference," a causal discovery approach that integrates the Hill Climb Search algorithm with a customized Linear Gaussian Bayesian Information Criterion (BIC). This method was applied to data from the Adolescent Brain Cognitive Development (ABCD) Study, which included parental behavior assessments, children's genotypes, and externalizing behavior measures. We performed dimensionality reduction to address multicollinearity among parental behaviors and assessed children's genetic risk for externalizing disorders using polygenic risk scores (PRS), which were computed based on GWAS summary statistics from independent cohorts. Once the causal pathways were identified, we employed structural equation modeling (SEM) to quantify the relationships within the model.

Results: We identified prominent causal pathways linking parental behaviors to children's externalizing outcomes. Parental alcohol misuse and broader behavioral issues exhibited notably stronger direct effects (0.33 and 0.20, respectively) compared to children's PRS (0.07). Moreover, when considering both direct and indirect paths, parental substance misuse (alcohol, drugs, and tobacco) collectively resulted in a total effect exceeding 1.1 on externalizing behaviors. Bootstrap and sensitivity analyses further validated the robustness of these findings.

Discussion and conclusion: Parental behaviors exert larger effects on children's externalizing outcomes than genetic risk, suggesting potential targets for prevention and intervention. The Hillclimb-Causal framework provides a general, data-driven way to map causal pathways in developmental psychiatry and related domains.

爬山-因果推理:一种数据驱动的方法来识别父母行为、遗传风险和儿童外化行为之间的因果途径。
目的:儿童的外化行为,如攻击、多动和反抗,受到遗传倾向和环境因素(尤其是父母行为)之间复杂的相互作用的影响。揭示这些复杂的因果关系可以从使用健壮的数据驱动方法中受益。材料和方法:我们开发了“爬山-因果推理”,这是一种因果发现方法,将爬山搜索算法与定制的线性高斯贝叶斯信息准则(BIC)集成在一起。该方法应用于青少年大脑认知发展(ABCD)研究的数据,包括父母行为评估、儿童基因型和外化行为测量。我们进行了降维,以解决父母行为之间的多重共线性,并使用多基因风险评分(PRS)评估儿童外部性疾病的遗传风险,PRS是基于独立队列的GWAS汇总统计数据计算的。一旦确定了因果关系,我们采用结构方程模型(SEM)来量化模型内的关系。结果:我们确定了将父母行为与儿童外化结果联系起来的重要因果途径。与儿童的PRS(0.07)相比,父母酒精滥用和更广泛的行为问题表现出明显更强的直接影响(分别为0.33和0.20)。此外,当考虑直接和间接途径时,父母物质滥用(酒精,毒品和烟草)共同导致外化行为的总效应超过1.1。Bootstrap和敏感性分析进一步验证了这些发现的稳健性。讨论与结论:父母行为对儿童外化结局的影响大于遗传风险,提示了预防和干预的潜在目标。Hillclimb-Causal框架提供了一种通用的、数据驱动的方法来绘制发展精神病学和相关领域的因果路径。
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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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