Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam (Savage, Phung, Posthuma); Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York (Barr, Meyers, Porjesz); VA New York Harbor Healthcare System, Brooklyn, New York (Barr, Meyers); Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine (Lee, Zhang, Ge, Smoller, Mallard), and Center for Precision Psychiatry (Ge, Smoller), Massachusetts General Hospital, Boston; Department of Psychiatry, Harvard Medical School, Boston (Lee, Ge, Smoller, Mallard); Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge (Lee, Zhang, Ge, Smoller, Mallard); Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston (Zhang); Department of Psychiatry, Washington University School of Medicine, St. Louis (McCutcheon); Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville (Davis, Sanchez-Roige); Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, VU University Medical Center, Amsterdam (Posthuma); Department of Psychiatry and Institute for Genomic Medicine, University of California San Diego, La Jolla (Sanchez-Roige).
Am J Psychiatry. 2024 Nov 1;181(11):1006-1017. doi: 10.1176/appi.ajp.20231055. Epub 2024 Oct 9.
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.
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.
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 (r 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.
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.
越来越多的全基因组关联研究(GWAS)样本用于研究饮酒行为(AUBs),导致牵连基因大量增加,但这些关联的临床和功能理解仍然很低,部分原因是大多数 GWAS 没有考虑到 AUBs 的复杂和多样表现。本研究应用多维框架研究了 AUBs 异质维度的潜在遗传结构。
从大约 400,000 名英国生物库参与者中获得了多模态评估(自我报告、访谈、电子健康记录)。对 18 种不同的 AUB 进行了 GWAS,包括消费、饮酒模式、酒精问题和临床后遗症。使用基因组结构方程建模识别潜在的遗传因素,并将其向前推进到 GWAS,然后进行功能注释、遗传相关和富集分析以解释遗传关联。
确定了四个潜在因素:问题、消费、BeerPref(随着对啤酒的偏好,酒精消费下降)和 AtypicalPref(饮用强化葡萄酒和烈酒)。潜在因素中度相关(r 值为 0.12-0.57),具有不同的关联模式,特别是 BeerPref 牵连了许多新的基因组区域。大脑中区域和细胞类型特异性基因表达的模式也因潜在因素而异。
除了增加样本量外,深入表型分析是提高对 AUB 遗传病因理解的重要下一步。需要进一步努力,使用考虑到其复杂、多维性质的方法来揭示 AUB 背后的遗传异质性。