Endocannabinoid Gene × Gene Interaction Association to Alcohol Use Disorder in Two Adolescent Cohorts.
Endocannabinoid Gene × Gene Interaction Association to Alcohol Use Disorder in Two Adolescent Cohorts.
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DOI：
10.3389/fpsyt.2021.645746
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发表时间：
2021
影响因子：
4.7
通讯作者：
IMAGEN consortium
中科院分区：
文献类型：
--
作者：
Elkrief L;Spinney S;Vosberg DE;Banaschewski T;Bokde ALW;Quinlan EB;Desrivières S;Flor H;Garavan H;Gowland P;Heinz A;Brühl R;Martinot JL;Paillère Martinot ML;Nees F;Papadopoulos Orfanos D;Poustka L;Hohmann S;Millenet S;Fröhner JH;Smolka MN;Walter H;Whelan R;Schumann G;Pausova Z;Paus T;Huguet G;Conrod P;IMAGEN consortium
内源性大麻素系统的遗传标记与大麻使用之外的各种成瘾相关行为有关。在目前的研究中，我们调查了内源性大麻素（eCB）遗传标志物与欧洲青少年（14-18岁）酒精使用障碍（AUD）之间的关系，随后在IMAGEN研究（n = 2，051）中进行了研究，并探讨了加拿大Saguenay青年研究（NAU）（n = 772）中北美青少年队列的复制情况。病例对照状态由酒精使用障碍识别测试（AUDIT）的得分超过7分表示。首先，使用基于集合的测试方法来检查eCB系统和AUDIT病例/对照状态之间是否存在基因水平的关系。仅使用既独立又与病例对照状态显著相关的SNP，我们进行Fisher精确检验以确定与病例对照状态相关的SNP水平比值比，然后进行logistic回归作为事后分析，同时考虑各种协变量。采用广义多因子降维法（GMDR）分析AUDIT筛选阳性的5个eCB基因的最稳健SNP×SNP相互作用。虽然在多次测试校正后没有基因集与AUDIT评分显着相关，但在病例/对照分析中，7个SNP与> 7的AUDIT评分显着相关（p < 0.05; OR<1）。两个SNPs在通过错误发现率（FDR）校正后仍然是显著的：CNR 1中的rs 9343525（pcorrected =0.042，OR = 0.73）和MGLL中的rs 507961（pcorrected = 0.043，OR = 0.78）。Logistic回归分析显示，在校正多个协变量和协变量× SNP的交互作用后，rs 9353525（CNR 1）和rs 507961（MGLL）与阳性AUDIT筛查仍显著相关（p < 0.01; OR < 1）。这一结果在研究队列中没有重复。GMDR模型揭示了一个显着的三SNP相互作用（p = 0.006），涉及rs 484061（MGLL），rs 4963307（DAGLA），和rs7766029（CNR 1）预测病例对照状态后，校正IMAGEN样本中的多个协变量。通过表型对这三种SNP的组合进行二项式逻辑回归，结果与IMAGEN组相同（BETA = 0.501，p = 0.06）。虽然是初步的，但本研究表明，eCB系统可能在青少年AUD的发展中发挥作用。
Genetic markers of the endocannabinoid system have been linked to a variety of addiction-related behaviors that extend beyond cannabis use. In the current study we investigate the relationship between endocannabinoid (eCB) genetic markers and alcohol use disorder (AUD) in European adolescents (14–18 years old) followed in the IMAGEN study (n = 2,051) and explore replication in a cohort of North American adolescents from Canadian Saguenay Youth Study (SYS) (n = 772). Case-control status is represented by a score of more than 7 on the Alcohol Use Disorder Identification Test (AUDIT). First a set-based test method was used to examine if a relationship between the eCB system and AUDIT case/control status exists at the gene level. Using only SNPs that are both independent and significantly associated to case-control status, we perform Fisher's exact test to determine SNP level odds ratios in relation to case-control status and then perform logistic regressions as post-hoc analysis, while considering various covariates. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the most robust SNP×SNP interaction of the five eCB genes with positive AUDIT screen. While no gene-sets were significantly associated to AUDIT scores after correction for multiple tests, in the case/control analysis, 7 SNPs were significantly associated with AUDIT scores of > 7 (p < 0.05; OR<1). Two SNPs remain significant after correction by false discovery rate (FDR): rs9343525 in CNR1 (pcorrected =0.042, OR = 0.73) and rs507961 in MGLL (pcorrected = 0.043, OR = 0.78). Logistic regression showed that both rs9353525 (CNR1) and rs507961 (MGLL) remained significantly associated with positive AUDIT screens (p < 0.01; OR < 1) after correction for multiple covariables and interaction of covariable × SNP. This result was not replicated in the SYS cohort. The GMDR model revealed a significant three-SNP interaction (p = 0.006) involving rs484061 (MGLL), rs4963307 (DAGLA), and rs7766029 (CNR1) predicted case-control status, after correcting for multiple covariables in the IMAGEN sample. A binomial logistic regression of the combination of these three SNPs by phenotype in the SYS cohort showed a result in the same direction as seen in the IMAGEN cohort (BETA = 0.501, p = 0.06). While preliminary, the present study suggests that the eCB system may play a role in the development of AUD in adolescents.
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DOI：
10.1038/npp.2009.200
发表时间：
2010-03
期刊：
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子：
--
作者：
通讯作者：
--
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
11
作者：
Farrell, M. S.;Werge, T.;Sklar, P.;Owen, M. J.;Ophoff, R. A.;O'Donovan, M. C.;Corvin, A.;Cichon, S.;Sullivan, P. F.
通讯作者：
Sullivan, P. F.
影响因子：
13.5
作者：
Hall, F. Scott;Drgonova, Jana;Jain, Siddharth;Uhl, George R.
通讯作者：
Uhl, George R.
影响因子：
3
作者：
Alexander DH;Lange K
通讯作者：
Lange K