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酒精使用障碍多基因评分与家族史及乙醇脱氢酶1B的比较。

Alcohol Use Disorder Polygenic Score Compared With Family History and ADH1B.

作者信息

Lai Dongbing, Zhang Michael, Abreu Marco, Schwantes-An Tae-Hwi, Chan Grace, Dick Danielle M, Kamarajan Chella, Kuang Weipeng, Nurnberger John I, Plawecki Martin H, Rice John, Schuckit Marc, Porjesz Bernice, Liu Yunlong, Foroud Tatiana

机构信息

Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis.

Department of Psychiatry, University of Connecticut School of Medicine, Farmington.

出版信息

JAMA Netw Open. 2024 Dec 2;7(12):e2452705. doi: 10.1001/jamanetworkopen.2024.52705.

Abstract

IMPORTANCE

Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk.

OBJECTIVE

To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry.

DESIGN, SETTING, AND PARTICIPANTS: This genetic association study was conducted between October 1, 2023, and May 21, 2024. A 2-stage design was used. First, the pruning and thresholding method was used to calculate PGSs in the screening stage. Second, the estimability and generalizability of the best PGS was determined using 2 independent samples in the testing stage. Three cohorts ascertained to study AUD were used in the screening stage: the Collaborative Study on the Genetics of Alcoholism (COGA), the Study of Addiction: Genetics and Environment (SAGE), and the Australian Twin-Family Study of Alcohol Use Disorder (OZALC). The All of Us Research Program (AOU), which comprises participants with diverse backgrounds and conditions, and the Indiana Biobank (IB), consisting of Indiana University Health system patients, were used to test the best PGS. For the COGA, SAGE, and OZALC cohorts, cases with AUD were determined using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) or Fifth Edition (DSM-5) criteria; controls did not meet any criteria or did not have any other substance use disorders. For the AOU and IB cohorts, cases with AUD were identified using International Classification of Diseases, Ninth Revision (ICD-9) or International Classification of Diseases, Tenth Revision (ICD-10) codes; controls were aged 21 years or older and did not have AUD.

EXPOSURE

The PGS was calculated using single-nucleotide variants with concordant effects in 3 large-scale genome-wide association studies of AUD-related phenotypes.

MAIN OUTCOMES AND MEASURES

The main outcome was AUD determined with DSM-IV or DSM-5 criteria and ICD-9 or ICD-10 codes. Generalized linear mixed models and logistic regression models were used to analyze related and unrelated samples, respectively.

RESULTS

The COGA, SAGE, and OZALC cohorts included a total of 8799 samples (6323 cases and 2476 controls; 50.6% were men). The AOU cohort had a total of 116 064 samples (5660 cases and 110 404 controls; 60.4% were women). The IB cohort had 6373 samples (936 cases and 5437 controls; 54.9% were women). The 5% of samples with the highest PGS in the AOU and IB cohorts were approximately 2 times more likely to develop AUD (odds ratio [OR], 1.96 [95% CI, 1.78-2.16]; P = 4.10 × 10-43; and OR, 2.07 [95% CI, 1.59-2.71]; P = 9.15 × 10-8, respectively) compared with the remaining 95% of samples; these ORs were comparable to family history of AUD. For the 5% of samples with the lowest PGS in the AOU and IB cohorts, the risk of AUD development was approximately half (OR, 0.53 [95% CI, 0.45-0.62]; P = 6.98 × 10-15; and OR, 0.57 [95% CI, 0.39-0.84]; P = 4.88 × 10-3) compared with the remaining 95% of samples; these ORs were comparable to the protective effect of ADH1B. PGS had similar estimabilities in male and female individuals.

CONCLUSIONS AND RELEVANCE

In this study of AUD risk among populations of European ancestry, PGSs were calculated using concordant single-nucleotide variants and the best PGS was tested in targeted datasets. The findings suggest that the PGS may potentially be used to evaluate AUD risk. More datasets with similar AUD prevalence as in general populations are needed to further test the generalizability of PGS.

摘要

重要性

据报道,识别酒精使用障碍(AUD)高危个体并随后应用预防和干预项目可降低AUD的发病率。多基因评分(PGS)用于衡量个体患某种疾病的遗传易感性,有可能用于评估AUD风险。

目的

在欧洲血统人群中,评估PGS与家族史和ADH1B相比,在评估AUD风险方面的可估计性和普遍性。

设计、背景和参与者:这项基因关联研究于2023年10月1日至2024年5月21日进行。采用两阶段设计。首先,在筛选阶段使用修剪和阈值法计算PGS。其次,在测试阶段使用两个独立样本确定最佳PGS的可估计性和普遍性。筛选阶段使用了三个确定用于研究AUD的队列:酒精中毒遗传学合作研究(COGA)、成瘾:遗传学与环境研究(SAGE)以及澳大利亚酒精使用障碍双生子-家族研究(OZALC)。所有人研究项目(AOU),其参与者具有不同背景和状况,以及印第安纳生物库(IB),由印第安纳大学健康系统的患者组成,用于测试最佳PGS。对于COGA、SAGE和OZALC队列,根据《精神疾病诊断与统计手册》第四版(DSM-IV)或第五版(DSM-5)标准确定AUD病例;对照不符合任何标准或没有任何其他物质使用障碍。对于AOU和IB队列,根据国际疾病分类第九版(ICD-9)或第十版(ICD-10)编码识别AUD病例;对照年龄在21岁及以上且没有AUD。

暴露

使用在3项与AUD相关表型的大规模全基因组关联研究中具有一致效应的单核苷酸变异计算PGS。

主要结局和测量指标

主要结局是根据DSM-IV或DSM-5标准以及ICD-9或ICD-10编码确定的AUD。分别使用广义线性混合模型和逻辑回归模型分析相关和不相关样本。

结果

COGA、SAGE和OZALC队列共有8799个样本(6323例病例和2476例对照;50.6%为男性)。AOU队列共有116064个样本(5660例病例和110404例对照;60.4%为女性)。IB队列有6373个样本(936例病例和5437例对照;54.9%为女性)。与AOU和IB队列中其余95%的样本相比,PGS最高的5%样本发生AUD的可能性大约高2倍(优势比[OR],1.96[95%置信区间,1.78 - 2.16];P = 4.10×10⁻⁴³;以及OR,2.07[95%置信区间,1.59 - 2.71];P = 9.15×10⁻⁸);这些OR与AUD家族史相当。对于AOU和IB队列中PGS最低的5%样本,与其余95%的样本相比,发生AUD的风险大约减半(OR,0.53[95%置信区间,0.45 - 0.62];P = 6.98×10⁻¹⁵;以及OR,0.57[95%置信区间,0.39 - 0.84];P = 4.88×10⁻³);这些OR与ADH1B的保护作用相当。PGS在男性和女性个体中具有相似的可估计性。

结论和相关性

在这项针对欧洲血统人群AUD风险的研究中,使用一致的单核苷酸变异计算PGS,并在目标数据集中测试最佳PGS。研究结果表明,PGS可能潜在地用于评估AUD风险。需要更多与一般人群中AUD患病率相似的数据集来进一步测试PGS的普遍性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0791/11686414/aa45ac6380fa/jamanetwopen-e2452705-g001.jpg

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