P359: Early-onset psychosis: Exploring the psychological phenotype of DLG4-related synaptopathy
Bibliographic record
Abstract
PCR-confirmed SARS-CoV-2 patients were enrolled in the GENCOV study.We defined inpatients as being admitted for management of severe COVID-19, and compared SNPs to those not requiring hospitalization.The enrichment of effect alleles in hospitalized vs non-hospitalized individuals was compared by Pearson's X 2 or Fisher's exact test, as appropriate.We used multiple logistic regression analysis [Odds Ratio (OR) with 95% confidence interval (CI)] to assess the confounding effects of age, sex, and ancestry, as well as the direction of the association.Bonferroni correction (Pc) was done to account for multiple testing for twenty-six SNPs with statistical significance set at P≤0.00192.Results: Genome sequencing was performed on 1,108 samples [median age: 50 years (IQR: 36-63), 523 (47.2%) are male, and 261 (23.6%) were hospitalized].Initially, we assessed the association of socio-demographic factors commonly associated with COVID-19 clinical outcome -hospitalization.The proportion of individuals who were hospitalized and aged ≥ 60 years, male, and non-White ancestry was 54.7%, 31.2%, and 34.2%, respectively.Thus, age ≥ 60 years (OR=12.04,95% CI: 8.70-16.66),male sex (OR=2.25,95% CI: 1.69-2.99),and non-White ancestry (OR=13.75,95% CI: 7.89-23.96)were all associated significantly with increased risk of hospitalization.Despite the small sample size, two out of the twenty-six SNPs analyzed showed significant association with hospitalization.After adjusting for age, sex, and ancestry LZTFL1 [rs11385942, OR=2.78 (1.94-3.98),Pc=3.51x10 -9 ], and TLR7 [rs3853839, OR=2.05 (1.24-3.41),Pc=3.20x10 -4 ] were both associated with an increased risk for hospitalization.Conclusion: Our study illustrates that genetic variation plays a significant role in the host response to SARS-CoV-2 infection, and influences COVID-19 clinical outcomes.The LMNA variant has been linked with neutropenia among COVID-19 patients, LZTFL1 promotes airway ciliogenesis, and TLR7 is implicated in the interferon pathway.Fine-mapping to identify the causal variation in linkage-disequilibrium to the associated SNPs may help to further unravel the mechanistic factors associated with COVID-19 clinical outcomes and lead to therapeutic interventions.To achieve significant statistical power, GENCOV data was contributed to the Canadian HostSeq and to the HGI for further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".