Heritability of neurocognitive traits in familial schizophrenia
Bibliographic record
Abstract
Neurocognitive deficits are considered promising endophenotypes for gene discovery in schizophrenia. Understanding the heritability and genetic inter-relationships of neurocognitive traits could support their use as alternatives to diagnosis. Participants were 85 adults from 17 multiplex Canadian families with familial schizophrenia linked to 1q23 who had neurocognitive testing results available. Heritability of 13 standard measures assessing motor skills, processing speed, verbal, and visuospatial memory, attention/working memory, executive functioning, and IQ was estimated using variance component models and SOLAR software. We then investigated bivariate relationships between those variables found to be heritable. IQ showed the highest heritability (h(2) = 0.64-0.74) and seven other neurocognitive measures, reflecting immediate and delayed verbal memory, attention/working memory, delayed visual memory, processing speed and motor skills, showed significant heritability (h(2) = 0.31-0.62) under one or more of the models assessed. A schizophrenia diagnostic covariate was significant (P < 0.0001) for all heritable variables. Bivariate analyses suggested that memory-IQ and visuomotor-processing speed formed two groups of heritable traits. The results provide further evidence of the heritability of selected neurocognitive measures, and their relationship to schizophrenia and underlying genetic architecture. Composite measures of memory or processing speed may be heritable phenotypes useful for studies of neurocognition.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".