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Genetic Heterogeneity Across Dimensions of Alcohol Use Behaviors

心理学 遗传学 进化生物学 生物 生物化学
作者
Jeanne E. Savage,Peter B. Barr,Tanya N. Phung,Young A Lee,Yingzhe Zhang,Vivia V. McCutcheon,Tian Ge,Jordan W. Smoller,Lea K. Davis,Jacquelyn L. Meyers,Bernice Porjesz,Daniëlle Posthuma,Travis T. Mallard,Sandra Sanchez‐Roige
出处
期刊:American Journal of Psychiatry [American Psychiatric Association Publishing]
卷期号:181 (11): 1006-1017 被引量:5
标识
DOI:10.1176/appi.ajp.20231055
摘要

Objective: Increasingly large samples in genome-wide association studies (GWASs) for alcohol use behaviors (AUBs) have led to an influx of implicated genes, yet the clinical and functional understanding of these associations remains low, in part because most GWASs do not account for the complex and varied manifestations of AUBs. This study applied a multidimensional framework to investigate the latent genetic structure underlying heterogeneous dimensions of AUBs. Methods: Multimodal assessments (self-report, interview, electronic health records) were obtained from approximately 400,000 UK Biobank participants. GWAS was conducted for 18 distinct AUBs, including consumption, drinking patterns, alcohol problems, and clinical sequelae. Latent genetic factors were identified and carried forward to GWAS using genomic structural equation modeling, followed by functional annotation, genetic correlation, and enrichment analyses to interpret the genetic associations. Results: Four latent factors were identified: Problems, Consumption, BeerPref (declining alcohol consumption with a preference for drinking beer), and AtypicalPref (drinking fortified wine and spirits). The latent factors were moderately correlated (rg values, 0.12–0.57) and had distinct patterns of associations, with BeerPref in particular implicating many novel genomic regions. Patterns of regional and cell type–specific gene expression in the brain also differed between the latent factors. Conclusions: Deep phenotyping is an important next step to improve understanding of the genetic etiology of AUBs, in addition to increasing sample size. Further effort is required to uncover the genetic heterogeneity underlying AUBs using methods that account for their complex, multidimensional nature.
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