T11. THE STRUCTURAL VARIANTS OF COMPLEMENT COMPONENT (C4) IN THE RISK AND CLINICAL CHARACTERISTICS OF SCHIZOPHRENIA
Notice bibliographique
Résumé
Schizophrenia (SCZ) is a heritable psychiatric disorder which affects approximately 1% of the population. The disease is characterized by both positive and negative symptoms, as well as declines in cognitive functioning. SCZ often becomes clinically apparent from late adolescence to early adulthood and severely impacts the quality of life. Although treatments exist, the development of preventive or curative interventions is hindered by the lack of mechanistic understanding of the pathophysiology of SCZ. The complement component 4 (C4) gene has been identified as one of the largest effect size markers for SCZ risk (Sekar et al., 2016). Sekar et al. have shown the longer version of C4A (C4AL) is linked with higher neural C4A expression which is associated with higher SCZ risk. The C4 gene discovery opened a new direction in SCZ research. This study aims to further explore the relationship between C4 structural variations and clinical characteristics in our Toronto Schizophrenia sample. 599 adults (age 18+) with SCZ or schizoaffective disorder were recruited from our CAMH hospital. Clinical and demographic information was gathered through structured clinical interviews (SCID) and chart review. The copy numbers of the C4A, C4B, C4L, and C4S in each sample were determined using ABI TaqMan copy number variation (CNV) protocol. Also, C4 CNV data on healthy controls were obtained on a small preliminary sample (n=111) from the Sekar et al. paper. Fisher’s exact test was performed to compare C4 CNV distribution between patients and controls. Linear and binomial logistic regression were performed to assess the relationship between C4 CNV and clinical characteristics of SCZ. The additive genotypic model in the analyses and sex was adjusted in the model of the age of onset. All statistical analyses were conducted using IBM SPSS software. The CNV counts of C4 structural variants in our sample ranges from 0–6, 0–5, 0–6, and 0–4 for C4A, C4B, C4L, and C4S respectively. No significant difference was observed in C4 CNV distribution between patients and healthy controls (p = 0.866, 0.795, 0.570, 0.430 for C4A, C4B, C4L, and C4S respectively). Age of onset was the only clinical characteristic that showed nominal association with C4A CNV, and the effect became more robust with sex was as a covariate (p = 0.05, p = 0.008, adjusted for sex). There was no significant association observed between C4 CNV and other clinical characteristics tested, such as symptom severity; Global Assessment of Function; and presence of symptoms such as delusions, hallucinations, disorganized speech or behavior, catatonia, alogia, avolition, inappropriate affect, and affective flattening. In the brain, C4 plays a crucial role in synaptic pruning. The process of synaptic pruning reaches a peak in late adolescence which is the same time when SCZ becomes clinically apparent. Synaptic pruning also explains the deficit of synaptic connections, as well as the cognitive decline that is commonly seen in SCZ. Sekar et al. previously identified the combination of C4A and the retroviral insertion creating - long version (C4AL) as a risk haplotype for SCZ. In our sample, we found no difference in C4 CNV distribution between patients and healthy control. Age of onset was the only clinical characteristic showed nominal significance with CA4 CNV, but the finding is contrary to the current knowledge of C4 in SCZ. Our results suggested higher C4A copy number leads to a later age of onset. The lack of significant finds could be due to the discordance in ancestry between patients and healthy controls, and small sample size. The next step is to estimate neural C4A expression using C4 structural haplotype data and explore the roles of neural C4A expression in SCZ risk and phenotypes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».