FARP‐1 deletion is associated with lack of response to autism treatment by early start denver model in a multiplex family
Bibliographic record
Abstract
BACKGROUND: Children with autism spectrum disorder (ASD) display impressive clinical heterogeneity, also involving treatment response. Genetic variants can contribute to explain this large interindividual phenotypic variability. METHODS: Array-CGH (a-CGH) and whole genome sequencing (WGS) were performed on a multiplex family with two small children diagnosed with ASD at 17 and 18 months of age. Both brothers received the same naturalistic intervention for one year according to the Early Start Denver Model (ESDM), applied by the same therapists, yielding dramatically different treatment outcomes. RESULTS: The older sibling came out of the autism spectrum, while the younger sibling displayed very little, in any, improvement. This boy was subsequently treated applying a structured Early Intensive Behavioral Intervention paired with Augmentative Alternative Communication, which yielded a partial response within another year. The ESDM nonresponsive child carries a novel maternally inherited 65 Kb deletion at chr. 13q32.2 spanning FARP1. Farp1 is a synaptic scaffolding protein, which plays a significant role in neural plasticity. CONCLUSION: These results represent a paradigmatic example of the heuristic potential of genetic markers in predicting treatment response and possibly in supporting the targeted prescription of specific early intervention 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".