<i>SERINC2</i>increases the risk of bipolar disorder in the Chinese population
Bibliographic record
Abstract
BACKGROUND: Although common variants in a large collection of patients are associated with increased risk for bipolar disorder (BD), studies have only been able to predict 25%-45% of risks, suggesting that lots of variants that contribute to the risk for BD haven't been identified. Our study aims to identify novel BD risk genes. METHODS: We performed whole-exome sequencing of 27 individuals from 6 BD multi-affected Chinese families to identify candidate variants. Targeted sequencing of one of the novel risk genes, SERINC2, in additional sporadic 717 BD patients and 312 healthy controls (HC) validated the association. Magnetic resonance imaging (MRI) were performed to evaluate the effect of the variant to brain structures from 213 subjects (4 BD subjects from a multi-affected family, 130 sporadic BD subjects and 79 HC control). RESULTS: BD pedigrees had an increased burden of uncommon variants in extracellular matrix (ECM) and calcium ion binding. By large-scale sequencing we identified a novel recessive BD risk gene, SERINC2, which plays a role in synthesis of sphingolipid and phosphatidylserine (PS). MRI image results show the homozygous nonsense variant in SERINC2 affects the volume of white matter in cerebellum. CONCLUSIONS: Our study identified SERINC2 as a risk gene of BD in the Chinese population.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".