Dataset Information

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.

ABSTRACT: The Ontario Brain Institute's "Brain-CODE" is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

SUBMITTER: Vaccarino AL

PROVIDER: S-EPMC8859302 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.

Vaccarino Anthony L AL Beaton Derek D Black Sandra E SE Blier Pierre P Farzan Farnak F Finger Elizabeth E Foster Jane A JA Freedman Morris M Frey Benicio N BN Gilbert Evans Susan S Ho Keith K Javadi Mojib M Kennedy Sidney H SH Lam Raymond W RW Lang Anthony E AE Lasalandra Bianca B Latour Sara S Masellis Mario M Milev Roumen V RV Müller Daniel J DJ Munoz Douglas P DP Parikh Sagar V SV Placenza Franca F Rotzinger Susan S Soares Claudio N CN Sparks Alana A Strother Stephen C SC Swartz Richard H RH Tan Brian B Tartaglia Maria Carmela MC Taylor Valerie H VH Theriault Elizabeth E Turecki Gustavo G Uher Rudolf R Zinman Lorne L Evans Kenneth R KR

Frontiers in psychiatry 20220207

The Ontario Brain Institute's "Brain-CODE" is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple bra ...[more]

PMID: 35197877

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Project description:BackgroundRecently, various evolution-related journals adopted policies to encourage or require archiving of phylogenetic trees and associated data. Such attention to practices that promote sharing of data reflects rapidly improving information technology, and rapidly expanding potential to use this technology to aggregate and link data from previously published research. Nevertheless, little is known about current practices, or best practices, for publishing trees and associated data so as to promote re-use.FindingsHere we summarize results of an ongoing analysis of current practices for archiving phylogenetic trees and associated data, current practices of re-use, and current barriers to re-use. We find that the technical infrastructure is available to support rudimentary archiving, but the frequency of archiving is low. Currently, most phylogenetic knowledge is not easily re-used due to a lack of archiving, lack of awareness of best practices, and lack of community-wide standards for formatting data, naming entities, and annotating data. Most attempts at data re-use seem to end in disappointment. Nevertheless, we find many positive examples of data re-use, particularly those that involve customized species trees generated by grafting to, and pruning from, a much larger tree.ConclusionsThe technologies and practices that facilitate data re-use can catalyze synthetic and integrative research. However, success will require engagement from various stakeholders including individual scientists who produce or consume shareable data, publishers, policy-makers, technology developers and resource-providers. The critical challenges for facilitating re-use of phylogenetic trees and associated data, we suggest, include: a broader commitment to public archiving; more extensive use of globally meaningful identifiers; development of user-friendly technology for annotating, submitting, searching, and retrieving data and their metadata; and development of a minimum reporting standard (MIAPA) indicating which kinds of data and metadata are most important for a re-useable phylogenetic record.

Project description:Background: The hypothalamus-pituitary-thyroid axis coordinates brain development and postdevelopmental function. Thyroid hormone (TH) variations, even within the normal range, have been associated with the risk of developing common psychiatric disorders, although the underlying mechanisms remain poorly understood. Methods: To get new insight into the potentially shared mechanisms underlying thyroid dysfunction and psychiatric disorders, we performed a comprehensive analysis of multiple phenotypic and genotypic databases. We investigated the relationship of thyroid disorders with depression, bipolar disorder (BIP), and anxiety disorders (ANXs) in 497,726 subjects from U.K. Biobank. We subsequently investigated genetic correlations between thyroid disorders, thyrotropin (TSH), and free thyroxine (fT4) levels, with the genome-wide factors that predispose to psychiatric disorders. Finally, the observed global genetic correlations were furthermore pinpointed to specific local genomic regions. Results: Hypothyroidism was positively associated with an increased risk of major depressive disorder (MDD; OR = 1.31, p = 5.29 × 10-89), BIP (OR = 1.55, p = 0.0038), and ANX (OR = 1.16, p = 6.22 × 10-8). Hyperthyroidism was associated with MDD (OR = 1.11, p = 0.0034) and ANX (OR = 1.34, p = 5.99 × 10-⁶). Genetically, strong coheritability was observed between thyroid disease and both major depressive (rg = 0.17, p = 2.7 × 10-⁴) and ANXs (rg = 0.17, p = 6.7 × 10-⁶). This genetic correlation was particularly strong at the major histocompatibility complex locus on chromosome 6 (p < 10-⁵), but further analysis showed that other parts of the genome also contributed to this global effect. Importantly, neither TSH nor fT4 levels were genetically correlated with mood disorders. Conclusions: Our findings highlight an underlying association between autoimmune hypothyroidism and mood disorders, which is not mediated through THs and in which autoimmunity plays a prominent role. While these findings could shed new light on the potential ineffectiveness of treating (minor) variations in thyroid function in psychiatric disorders, further research is needed to identify the exact underlying molecular mechanisms.

Dataset Information

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.

Publications

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.

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