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Record W2757824812 · doi:10.1101/127712

De novo damaging coding mutations are strongly associated with obsessive-compulsive disorder and overlap with autism

2017· preprint· en· W2757824812 on OpenAlexaff
Carolina Cappi, Melody Oliphant, Zsanett Péter, Gwyneth Zai, Catherine Sullivan, Abha R. Gupta, Ellen J. Hoffman, Manmeet Virdee, A. Jeremy Willsey, Roseli Gedanke Shavitt, Eurı́pedes Constantino Miguel, James L. Kennedy, Margaret A. Richter, Thomas Fernandez

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMental Health Research CanadaUniversity of TorontoSunnybrook Health Science CentreCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthNational Institutes of HealthH. Lundbeck A/SSimons FoundationAllison Family FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São PauloEli Lilly and Company
KeywordsProbandExome sequencingGeneticsAutismBiologyGeneAutism spectrum disorderExomeMutationMedicinePsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Obsessive-compulsive disorder (OCD) is a debilitating developmental neuropsychiatric disorder with a genetic risk component, yet identification of high-confidence risk genes has been challenging. We performed whole-exome sequencing in 222 OCD parent-child trios (184 trios after quality control), finding strong evidence that de novo likely gene disrupting and predicted damaging missense variants contribute to OCD risk. Together, these de novo damaging variants are enriched in OCD probands (RR 1.52, p=0.0005). We identified two high-confidence risk genes, each containing two de novo damaging variants in unrelated probands: CHD8 (Chromodomain Helicase DNA Binding Protein 8) and SCUBE1 (Signal Peptide, CUB Domain And EGF Like Domain Containing 1) . Based on our data, we estimate that 34% of de novo damaging variants seen in OCD contribute to risk, and that de novo damaging variants in approximately 335 genes contribute to risk in 22% of OCD cases. Furthermore, genes harboring de novo damaging variants in OCD are enriched for those reported in neurodevelopmental disorders, particularly autism spectrum disorders. An exploratory network analysis reveals significant functional connectivity and enrichment in canonical pathways related to immune response. SIGNIFICANCE STATEMENT Decades of genetic studies in obsessive-compulsive disorder (OCD) have yet to provide reproducible, statistically significant findings. Following an approach that has led to tremendous success in gene discovery for several neuropsychiatric disorders, here we report findings from DNA whole-exome sequencing of patients with OCD and their parents. We find strong evidence for the contribution of spontaneous, or de novo, predicted-damaging genetic variants to OCD risk, identify two high-confidence risk genes, and detect significant overlap with genes previously identified in autism. These results change the status quo of OCD genetics by identifying novel OCD risk genes, clarifying the genetic landscape of OCD with respect to de novo variation, and suggesting underlying biological pathways that will improve our understanding of OCD biology.

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)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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