SA94. When Rare Meets Common: Burden and Impact of Treatable Rare Genetic Diseases in Primary Psychiatric Populations
Bibliographic record
Abstract
Background: Many rare genetic syndromes are known to phenotypically manifest with psychiatric symptoms that can be indistinguishable from primary psychiatric disorders. While the majority of ongoing psychiatric genetic research has been dedicated to the identification and characterization of genes involved in primary psychiatric disorders, little research has been done to determine the extent to which rare genetic variants contribute to the overall psychiatric disease load. Within schizophrenia and bipolar populations, we are conducting the first study of its kind to determine the prevalence of 4 treatable genetic syndromes (Niemann Pick disease type C [NPC], Wilson disease, acute intermittent porphyria [AIP], and homocystinuria [HOM]) manifesting as primary psychiatric disorders. We hypothesize that a significant subpopulation of patients with psychiatric disorders have underlying rare genetic conditions. Methods: We are screening 1323 schizophrenia and 1200 bipolar disorder samples, along with 980 sex- and age-matched healthy controls, all with available DNA and extensive phenotype data. We are using a matrix-type pooled targeted deep sequencing of the genes NPC1, NPC2, ATP7B, HMBS, and CBS to screen for the 4 genetic diseases. Pathogenic variants within the targeted genes will be identified using an in-house analytic pipeline with quality control, variant discovery designed specifically for identifying variants in the matrix pooled targeted sequencing approach, and functionality prediction programs to determine variant pathogenicity. Sanger sequencing will be used to validate identified mutations and decrease false-positive calls. Results: A total of 1024 schizophrenia samples have been sequenced (average read depth = 468× per sample, average read length = 190 bp) using our matrix pooled targeted sequencing method. Sequencing of an additional 1024 schizophrenia and bipolar samples is currently underway. In our initial screening of 1024 schizophrenia patients, we found a significant overrepresentation of carrier/affected status among the screened patients (P < .0001, χ2 = 38.147, df = 4). Specifically, in total for all 4 genetic diseases, we identified 11 previously known pathogenic variants based on the ClinVar database and 34 predicted pathogenic variants based on four variant pathogenicity prediction softwares (Sift, PolyPhen 2, Mutation Taster, Condel). Conclusion: Screening for treatable genetic diseases, such as NPC, WD, AIP, and HOM, within schizophrenia and bipolar samples could provide a possible explanation for severe treatment resistance and treating the genetic condition can effectively “cure” patients of their otherwise difficult-to-treat psychiatric symptoms. Ultimately, this proof-of-principle study will lead to the development of molecular diagnostic tools for detection of underlying genetic disorders in psychiatric patients and will allow for precision medicine.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".