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Record W3198368556 · doi:10.3389/fgene.2021.711624

Polygenic Heterogeneity Across Obsessive-Compulsive Disorder Subgroups Defined by a Comorbid Diagnosis

2021· article· en· W3198368556 on OpenAlexaff
Nora I. Strom, Jakob Grove, Sandra Meier, Marie Bækvad‐Hansen, Judith Becker Nissen, Thomas D. Als, Matthew Halvorsen, Merete Nordentoft, Preben Bo Mortensen, David M. Hougaard, Thomas Werge, Ole Mors, Anders D. Børglum, James J. Crowley, Jonas Bybjerg‐Grauholm, Manuel Mattheisen

Bibliographic record

VenueFrontiers in Genetics · 2021
Typearticle
Languageen
FieldPsychology
TopicObsessive-Compulsive Spectrum Disorders
Canadian institutionsDalhousie University
FundersNovo Nordisk FondenNational Institute of Mental HealthAarhus UniversitetNovo NordiskNational Institutes of HealthH. Lundbeck A/SHumboldt-Universität zu BerlinLundbeckfondenHorizon 2020 Framework ProgrammeDeutsche Forschungsgemeinschaft
KeywordsPolygenic risk scoreObsessive compulsiveComorbidityPsychologyPsychiatryClinical psychologyMedicineGeneticsBiologySingle-nucleotide polymorphismGeneGenotype

Abstract

fetched live from OpenAlex

Among patients with obsessive-compulsive disorder (OCD), 65–85% manifest another psychiatric disorder concomitantly or at some other time point during their life. OCD is highly heritable, as are many of its comorbidities. A possible genetic heterogeneity of OCD in relation to its comorbid conditions, however, has not yet been exhaustively explored. We used a framework of different approaches to study the genetic relationship of OCD with three commonly observed comorbidities, namely major depressive disorder (MDD), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). First, using publicly available summary statistics from large-scale genome-wide association studies, we compared genetic correlation patterns for OCD, MDD, ADHD, and ASD with 861 somatic and mental health phenotypes. Secondly, we examined how polygenic risk scores (PRS) of eight traits that showed heterogeneous correlation patterns with OCD, MDD, ADHD, and ASD partitioned across comorbid subgroups in OCD using independent unpublished data from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH). The comorbid subgroups comprised of patients with only OCD ( N = 366), OCD and MDD ( N = 1,052), OCD and ADHD ( N = 443), OCD and ASD ( N = 388), and OCD with more than 1 comorbidity ( N = 429). We found that PRS of all traits but BMI were significantly associated with OCD across all subgroups (neuroticism: p = 1.19 × 10 −32 , bipolar disorder: p = 7.51 × 10 −8 , anorexia nervosa: p = 3.52 × 10 −20 , age at first birth: p = 9.38 × 10 −5 , educational attainment: p = 1.56 × 10 −4 , OCD: p = 1.87 × 10 −6 , insomnia: p = 2.61 × 10 −5 , BMI: p = 0.15). For age at first birth, educational attainment, and insomnia PRS estimates significantly differed across comorbid subgroups ( p = 2.29 × 10 −4 , p = 1.63 × 10 −4 , and p = 0.045, respectively). Especially for anorexia nervosa, age at first birth, educational attainment, insomnia, and neuroticism the correlation patterns that emerged from genetic correlation analysis of OCD, MDD, ADHD, and ASD were mirrored in the PRS associations with the respective comorbid OCD groups. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across OCD comorbid subgroups.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.292
Teacher spread0.280 · 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 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

Citations17
Published2021
Admission routes1
Has abstractyes

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