Polygenic risk scores distinguish patients from non‐affected adult relatives and from normal controls in schizophrenia and bipolar disorder multi‐affected kindreds
Bibliographic record
Abstract
Recent studies have used results on SNP association with schizophrenia (SZ) and bipolar disorder (BD) to create polygenic risk scores (PRS) discriminating non‐familial unrelated patients from controls. Little is known about the role of PRS in densely affected multigenerational families. We tested PRS differences between affected SZ and BD family members from their non‐affected adult relatives (NAARs) in Eastern Quebec Kindreds and from controls. We examined 1227 subjects: from 17 SZ and BD kindreds, we studied 153 patients (57 SZ, 13 schizoaffective, and 83 BD) and 180 NAARs, and 894 unrelated controls from the Eastern Quebec population. PRS were derived from published case‐control association studies of SZ and BD. We also constructed a combined SZ and BD PRS by using SNPs from both SZ and BD PRS. SZ patients had higher SZ PRS than controls (p = 0.0039, R2 = 0.027) and BD patients had higher BD PRS than controls (p = 0.013, R2 = 0.027). Differences between affected subjects and NAARs and controls were significant with both SZ and BD PRS. Moreover, a combined SZ‐BD PRS was also significantly associated with SZ and BD when compared to NAARs (p = 0.0019, R2 = 0.010) and controls (p = 0.0025, R2 = 0.028), revealing a SZ‐BD commonality effect in PRS at the diagnosis level. The SZ and the BD PRS, however, showed a degree of specificity regarding thought disorder symptoms. Overall, our report would confirm the usefulness of PRS in capturing the contribution of common genetic variants to the risk of SZ and BD in densely affected families.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".