Genomic insights from a deeply phenotyped highly consanguineous neurodevelopmental disorders cohort
Bibliographic record
Abstract
PURPOSE: The genetic underpinning of neurodevelopmental disorders (NDDs) in diverse ethnic populations, especially those with high rates of consanguinity, remains largely unexplored. Here, we aim to elucidate genomic insight from 576 well-phenotyped and highly consanguineous (16%) NDD cohort. METHODS: We used chromosomal microarray (CMA; N:247), exome sequencing (ES; N:127), combined CMA and ES (N:202), and long-read genome sequencing to identify genetic etiology. Deep clinical multivariate data were coupled with genomic variants for stratification analysis. RESULTS: Genetic diagnosis rates were 17% with CMA, 29.92% with ES, and 37.13% with combined CMA and ES. Notably, children of consanguineous parents showed a significantly higher diagnostic yield (P < .01) compared to those from nonconsanguineous parents. Among the ES-identified pathogenic variants, 36.19% (38/105) were novel, implicating 35 unique genes. Long-read sequencing of seizure participants unresolved by combined test identified expanded FMR1 trinucleotide repeats. Additionally, we identified 2 recurrent X-linked variants in the G6PD in 3.65% (12/329) of NDD participants. These variants were absent in large-population control cohorts and cohort comprising neurodevelopmental and neuropsychiatric populations of European descendants, indicating a possible associated risk factor potentially resulting from ancient genetic drift. CONCLUSION: This study unveils unique clinical and genomic insights from a consanguinity rich Bangladeshi NDD cohort.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".