首页> 外文期刊>Alcoholism: Clinical and experimental research >Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol‐Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort
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Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol‐Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort

机译:多变量分析揭示了与神经元信号传导和免疫相关的生物成分从酗酒队列遗传学的协作研究中介导含有酗酒的个体的脑依赖性个体的异常

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Background The underlying molecular mechanisms associated with alcohol use disorder ( AUD ) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage‐based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms ( EEG ) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach. Methods The current project adopted a bimultivariate data‐driven approach, parallel independent component analysis (para‐ ICA ), to derive and explore significant genotype–phenotype associations in a case–control subset of the Collaborative Study on the Genetics of Alcoholism ( COGA ) dataset. Para‐ ICA subjects comprised N ?=?799 self‐reported European Americans (367 controls and 432 AUD cases), recruited from COGA , who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism ( SNP ) data were preprocessed prior to being subjected to para‐ICA in order to derive genotype–phenotype relationships. Results From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG –genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease‐related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls. Conclusions Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low‐frequency alpha and theta abnormalities in alcohol addiction.
机译:背景技术含有与醇使用障碍(AUD)风险相关的底层分子机制仅使用单变量基因组结合和基于联系的分析等传统方法部分地揭示。因此,我们旨在鉴定与使用多变量方法的篡改与篡改有关的脑电图(EEG)神经生物学表型相关的基因簇。方法采用Bimultivariate数据驱动方法,平行独立分量分析(段),在患者主义(COGA)数据集遗传学遗传学遗传学遗传研究的病例控制子集中获得和探索具有重要基因型 - 表型关联的Bimultivariate的数据驱动方法(PARA-ICA) 。涉及N?=?799自我报告的欧洲人(367个控制和432名AUD案件),从Coga招募,后者经历了休息的脑电图和基因分型。在经过段ICA之前预处理EEG和Genomewide单核苷酸多态性(SNP)数据是为了衍生基因型 - 表型关系。估计数据,4个EEG频率和4个SNP组分的结果,eeg-genetic关系对具有显着相关的eeg-genetic关系对。第一这样对主要代表θ活性,与富含(但不限于)本体/疾病过程的遗传簇负相关,代表细胞信号传导,神经发生,跨膜药物运输,酒精中毒和脂质/胆固醇代谢。第二组分对主要表示α活性,与与第一组分类似富集的本体的遗传簇正相关。该组分的疾病相关的富集揭示了心脏病和自身免疫障碍作为顶部命中。与对照相比,在病例中显着降低了α和θ组分的加载系数。结论我们的数据表明合理的多因素遗传成分,主要富集神经元/突触信号/透射,免疫和神经发生,介导低频α和rena醇成瘾异常。

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