Limited Evidence for Association of Genome-Wide Schizophrenia Risk Variants on Cortical Neuroimaging Phenotypes
Bibliographic record
Abstract
BACKGROUND: There are now over 100 established genetic risk variants for schizophrenia; however, their influence on brain structure and circuitry across the human lifespan are not known. METHODS: We examined healthy individuals 8-86 years of age, from the Centre for Addiction and Mental Health, the Zucker Hillside Hospital, and the Philadelphia Neurodevelopmental Cohort. Following thorough quality control procedures, we investigated associations of established genetic risk variants with heritable neuroimaging phenotypes relevant to schizophrenia, namely thickness of frontal and temporal cortical regions (n = 565) and frontotemporal and interhemispheric white matter tract fractional anisotropy (FA) (n = 530). RESULTS: There was little evidence for association of risk variants with imaging phenotypes. No association with cortical thickness of any region was present. Only rs12148337, near a long noncoding RNA region, was associated with white matter FA (splenium of corpus callosum) following multiple comparison correction (corrected p = .012); this single nucleotide polymorphism was also associated with genu FA and superior longitudinal fasciculus FA at p <.005 (uncorrected). There was no association of polygenic risk score with white matter FA or cortical thickness. CONCLUSIONS: In sum, our findings provide limited evidence for association of schizophrenia risk variants with cortical thickness or diffusion imaging white matter phenotypes. When taken with recent lack of association of these variants with subcortical brain volumes, our results either suggest that structural neuroimaging approaches at current resolution are not sufficiently sensitive to detect effects of these risk variants or that multiple comparison correction in correlated phenotypes is too stringent, potentially "eliminating" biologically important signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".