Population Genomic Screening for Genetic Etiologies of Neurodevelopmental/Psychiatric Disorders Demonstrates Personal Utility and Positive Participant Responses
Bibliographic record
Abstract
Genomic variants that cause neurodevelopmental/psychiatric disorders (NPD) are relatively prevalent and highly penetrant. This study aimed to understand adults’ immediate responses to receiving NPD-related results to inform inclusion in population-based genomic screening programs. Nine recurrent, pathogenic copy number variants (CNVs) were identified from research exome data, clinically confirmed, and disclosed to adult participants of the Geisinger MyCode Community Health Initiative DiscovEHR cohort by experienced genetic counselors. A subset of in-person genetic counseling sessions (n = 27) were audio-recorded, transcribed, and coded using a grounded theory approach. Participant reactions were overwhelmingly positive and indicated that an NPD genetic etiology was highly valuable and personally useful. Participants frequently reported learning disabilities or other NPD that were not documented in their electronic health records and noted difficulties obtaining support for NPD needs. Most intended to share their genetic result with family members and health care providers and were interested in how their result could improve their healthcare. This study indicates that results from population-based NPD genomic screening can provide personal value for adults with NPD, were viewed positively by participants, and could improve clinical outcomes by informing symptom monitoring for NPD and co-morbidities, promoting improved health behaviors, and enhancing psychotherapeutic approaches.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".