Genome sequencing broadens the range of contributing variants with clinical implications in schizophrenia
Bibliographic record
Abstract
The range of genetic variation with potential clinical implications in schizophrenia, beyond rare copy number variants (CNVs), remains uncertain. We therefore analyzed genome sequencing data for 259 unrelated adults with schizophrenia from a well-characterized community-based cohort previously examined with chromosomal microarray for CNVs (none with 22q11.2 deletions). We analyzed these genomes for rare high-impact variants considered causal for neurodevelopmental disorders, including single-nucleotide variants (SNVs) and small insertions/deletions (indels), for potential clinical relevance based on findings for neurodevelopmental disorders. Also, we investigated a novel variant type, tandem repeat expansions (TREs), in 45 loci known to be associated with monogenic neurological diseases. We found several of these variants in this schizophrenia population suggesting that these variants have a wider clinical spectrum than previously thought. In addition to known pathogenic CNVs, we identified 11 (4.3%) individuals with clinically relevant SNVs/indels in genes converging on schizophrenia-relevant pathways. Clinical yield was significantly enriched in females and in those with broadly defined learning/intellectual disabilities. Genome analyses also identified variants with potential clinical implications, including TREs (one in DMPK; two in ATXN8OS) and ultra-rare loss-of-function SNVs in ZMYM2 (a novel candidate gene for schizophrenia). Of the 233 individuals with no pathogenic CNVs, we identified rare high-impact variants (i.e., clinically relevant or with potential clinical implications) for 14 individuals (6.0%); some had multiple rare high-impact variants. Mean schizophrenia polygenic risk score was similar between individuals with and without clinically relevant rare genetic variation; common variants were not sufficient for clinical application. These findings broaden the individual and global picture of clinically relevant genetic risk in schizophrenia, and suggest the potential translational value of genome sequencing as a single genetic technology for schizophrenia.
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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".