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Record W3168778565 · doi:10.1038/s41398-021-01471-y

Genome wide study of tardive dyskinesia in schizophrenia

2021· article· en· W3168778565 on OpenAlexaff
Keane Lim, Max Lam, Clement C. Zai, Jenny Tay, Nina Karlsson, Smita N. Deshpande, B.K. Thelma, Norio Ozaki, Toshiya Inada, Kang Sim, Siow‐Ann Chong, Todd Lencz, Jianjun Liu, Jimmy Lee

Bibliographic record

VenueTranslational Psychiatry · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersBiomedical Research CouncilNational Medical Research CouncilNational Institute of Mental HealthMedical Research CouncilNational Institutes of HealthAgency for Science, Technology and Research
KeywordsTardive dyskinesiaGenome-wide association studyPolygenic risk scoreSingle-nucleotide polymorphismSchizophrenia (object-oriented programming)Genetic architectureLocus (genetics)DyskinesiaPhenotypeGeneticsPsychiatryBiologyMedicineDiseaseGeneGenotypeInternal medicineParkinson's disease

Abstract

fetched live from OpenAlex

Tardive dyskinesia (TD) is a severe condition characterized by repetitive involuntary movement of orofacial regions and extremities. Patients treated with antipsychotics typically present with TD symptomatology. Here, we conducted the largest GWAS of TD to date, by meta-analyzing samples of East-Asian, European, and African American ancestry, followed by analyses of biological pathways and polygenic risk with related phenotypes. We identified a novel locus and three suggestive loci, implicating immune-related pathways. Through integrating trans-ethnic fine mapping, we identified putative credible causal variants for three of the loci. Post-hoc analysis revealed that SNPs harbored in TNFRSF1B and CALCOCO1 independently conferred three-fold increase in TD risk, beyond clinical risk factors like Age of onset and Duration of illness to schizophrenia. Further work is necessary to replicate loci that are reported in the study and evaluate the polygenic architecture underlying TD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2021
Admission routes1
Has abstractyes

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