Environmental, Psychiatric, and Genetic Predictors of Alcohol Use Disorder Criterion Count: Analyses of African and European Ancestries

酒精使用障碍诊断标准计数的环境、精神病学和遗传预测因素:非洲和欧洲血统的分析

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Abstract

OBJECTIVE: Quantifying the contribution of environmental and genetic factors to alcohol use disorder (AUD) criterion count across different ancestries may provide insight into the biopsychosocial etiology of AUD. Although polygenic risk prediction represents an important future application, its utility may be enhanced when considered in the context of environmental predictors. The authors used large African- and European-ancestry samples to examine how genetic, psychiatric, and environmental factors predict AUD criterion count. METHODS: The authors analyzed data from 11,021 individuals in the Yale-Penn Study sample: 5,843 of African ancestry (AFR) and 5,178 of European ancestry (EUR). Polygenic risk scores (PRSs) were generated from genome-wide association studies of problematic alcohol use (PAU). Generalized linear regression and relative importance analyses determined the independent and interactive effects of environmental and genetic factors on AUD criterion count. RESULTS: PRSs for PAU were positively associated with AUD criterion count in both ancestries and predicted 1.9% of AUD criterion count in the EUR sample and 1.3% in the AFR sample. A combination of education, substance use in the household before age 13, annual household income, and male sex explained 73.1% of the variance in AUD criterion count in the AFR sample and 58.9% in the EUR sample. Among examined psychiatric disorders, posttraumatic stress disorder explained the most variance (10.0% in AFR, 9.4% in EUR), followed by anxiety disorders (3.4% in AFR, 6.2% in EUR) and major depressive disorder (1.3% in AFR, 2.1% in EUR). In the EUR sample, education level moderated the relationship between PRS for PAU and AUD criterion count. CONCLUSIONS: In both African and European ancestry groups, environmental factors explained the majority of the variance in AUD criterion count, but polygenic risk was also a statistically significant predictor. These findings may help inform clinical, research, and policy efforts to mitigate AUD risk.

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