Dataset Information

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders.

ABSTRACT: In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological questionnaires. The sample of our study was 1010 people from different regions of Russia. We created the integrated ICBrainDB database that enables users to easily access, download, and further process information about individual behavioral characteristics and psychophysiological responses along with inherited trait data. The data obtained can be useful in training neural networks and in machine learning construction processes in Big Data analysis. We believe that the existence of such a resource will play an important role in the further search for associations of genetic factors and EEG markers of depression.

SUBMITTER: Ivanov R

PROVIDER: S-EPMC8780210 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders.

Ivanov Roman R Kazantsev Fedor F Zavarzin Evgeny E Klimenko Alexandra A Milakhina Natalya N Matushkin Yury G YG Savostyanov Alexander A Lashin Sergey S

Journal of personalized medicine 20220105 1

In this study, we collected and systemized diverse information related to depressive and anxiety disorders as the first step on the way to investigate the associations between molecular genetics, electrophysiological, behavioral, and psychological characteristics of people. Keeping that in mind, we developed an internet resource including a database and tools for primary presentation of the collected data of genetic factors, the results of electroencephalography (EEG) tests, and psychological qu ...[more]

PMID: 35055368

Similar Datasets

Project description:BackgroundComplexity and noise in expression quantitative trait loci (eQTL) studies make it difficult to distinguish potential regulatory relationships among the many interactions. The predominant method of identifying eQTLs finds associations that are significant at a genome-wide level. The vast number of statistical tests carried out on these data make false negatives very likely. Corrections for multiple testing error render genome-wide eQTL techniques unable to detect modest regulatory effects. We propose an alternative method to identify eQTLs that builds on traditional approaches. In contrast to genome-wide techniques, our method determines the significance of an association between an expression trait and a locus with respect to the set of all associations to the expression trait. The use of this specific information facilitates identification of expression traits that have an expression profile that is characterized by a single exceptional association to a locus. Our approach identifies expression traits that have exceptional associations regardless of the genome-wide significance of those associations. This property facilitates the identification of possible false negatives for genome-wide significance. Further, our approach has the property of prioritizing expression traits that are affected by few strong associations. Expression traits identified by this method may warrant additional study because their expression level may be affected by targeting genes near a single locus.ResultsWe demonstrate our method by identifying eQTL hotspots in Plasmodium falciparum (malaria) and Saccharomyces cerevisiae (yeast). We demonstrate the prioritization of traits with few strong genetic effects through Gene Ontology (GO) analysis of Yeast. Our results are strongly consistent with results gathered using genome-wide methods and identify additional hotspots and eQTLs.ConclusionsNew eQTLs and hotspots found with this method may represent regions of the genome or biological processes that are controlled through few relatively strong genetic interactions. These points of interest warrant experimental investigation.

Project description:IntroductionMental health disorders figure among the many comorbidities of obstructive respiratory diseases. The multisystemic characteristics of chronic respiratory disease and its impact on quality of life could affect depressive and/or anxiety disorders. We aimed to evaluate the association of spirometric indices, ventilatory disorders, and self-reported respiratory diseases with psychiatric disorders considering potential confounders.MethodsWe analysed data from CoLaus|PsyCoLaus, a Swiss population-based cohort study, consisting of 2'774 participants (56% women; mean age: 62.3 (standard deviation = ±9.9) years) who performed spirometry and completed semi-structured psychiatric interviews. We defined ventilatory disorders using GLI-2012 references. Major depressive episode (MDE) and anxiety disorders were defined using the DSM-IV (Diagnostic and Statistical Manual).Results630 subjects (22.7%) presented a recent MDE. Reversible obstructive ventilatory disorders were associated with recent MDE (OR = 1.94, 95% confidence interval (95% CI) 1.10-3.43) and recent anxiety disorders (2.21 [1.16-4.22]) only in unadjusted model. Self-reported chronic obstructive pulmonary (COPD) and asthma were associated with MDE with ORs of 2.49 (95% CI, 1.19-5.27) and 1.56 (95% CI, 1.04-2.35) after adjustment, respectively. Possible restrictive ventilatory impairment was positively associated with recent anxiety disorders (OR = 2.46, 1.10-5.51). Z-scores of FEV1, FVC, and maximal mid-expiratory flow were not associated with psychiatric disorders. There was no association between ventilatory disorders and MDE in adjusted models.ConclusionsIn this cross-sectional population-based study, the association between respiratory disorders and depressive disorders was observed for self-reported COPD and asthma, but not with objective diagnoses based on spirometry. Lung volumes are not associated with psychiatric disorders. Further prospective studies will be necessary to understand the significance of the association.

Dataset Information

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders.

Publications

ICBrainDB: An Integrated Database for Finding Associations between Genetic Factors and EEG Markers of Depressive Disorders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets