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Genetics of Panic Disorder

2013· other· en· W1580359964 on OpenAlexaff
Eduard Maron, Jakov Shlik

Bibliographic record

VenueEncyclopedia of Life Sciences · 2013
Typeother
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCandidate genePanic disorderGenetic associationGenome-wide association studyEndophenotypeBiologyGeneticsGeneSingle-nucleotide polymorphismGenotypingComputational biologyAnxietyBioinformaticsPsychologyNeurosciencePsychiatryGenotypeCognition

Abstract

fetched live from OpenAlex

Abstract The molecular genetic research on panic disorder (PD) has grown tremendously in the past decade. To date, several hundreds of candidate genes have been examined in association studies, but most of the results have been negative, inconsistent or awaiting replication. Perhaps most intriguing have been findings involving the genes of biological systems known to be pertinent to anxiety phenotypes, such as serotonin, cholecystokinin and adenosine. An array of other genes related to hormonal, neurotrophic and intracellular systems has also been implicated in disposition to PD. The recent advances in bioinformatics and genotyping technologies, including genome‐wide association and gene expression methods, promise more comprehensive discovery in PD. Preliminary findings point to a number of novel gene targets with still unknown pathogenetic relationship to PD. The progress in clinical and neurobiological concepts of PD may further guide genetic research through the current ambiguity to more definitive findings. Key Concepts: The linkage and candidate gene association studies have so far showed only weak success to identify reliable and replicated evidence for the genetic substrate of PD. The genetic research in PD to date has been mostly restricted to small phenotypically and ethnically diverse datasets with genotyping of limited numbers of SNPs. An improved understanding of neurobiological pathways of PD could contribute to a more effective identification of candidate genes. Novel conceptual and analytic approaches, such as pathways‐based analyses, may help to advance GWA studies in PD. Laboratory panic challenge models may provide clues to genetic predisposition to PD. The understanding of genetic underpinnings of PD will not be of full value without a conceptual integration of the clinical phenomena of PD with psychological models and neurobiological or molecular substrates underlying its development and course. The course of PD may depend on the balance between pathogenetic and compensatory or recovery processes, accompanied by activation or inhibition of relevant genes. PD might exist in many distinct genetic forms, each with a different set of genes, but also in one form with certain genes reflecting broader vulnerability to panicogenesis. The heterogeneity in PD phenotypes, age of onset, subtypes and severity of panic attacks, gender and familial aggregation were not sufficiently accounted for in most of published studies and should be more carefully addressed in further analyses. Other comprehensive genetic approaches, including GWA, the analysis of copy number variants and the study of regulatory small noncoding RNAs, may lead to uncovering new genomic mechanisms of PD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.330
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2013
Admission routes1
Has abstractyes

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