Comprehensive whole genome sequence analyses yields novel genetic and structural insights for Intellectual Disability
Bibliographic record
Abstract
BACKGROUND: Intellectual Disability (ID) is among the most common global disorders, yet etiology is unknown in ~30% of patients despite clinical assessment. Whole genome sequencing (WGS) is able to interrogate the entire genome, providing potential to diagnose idiopathic patients. METHODS: We conducted WGS on eight children with idiopathic ID and brain structural defects, and their normal parents; carrying out an extensive data analyses, using standard and discovery approaches. RESULTS: We verified de novo pathogenic single nucleotide variants (SNV) in ARID1B c.1595delG and PHF6 c.820C > T, potentially causative de novo two base indels in SQSTM1 c.115_116delinsTA and UPF1 c.1576_1577delinsA, and de novo SNVs in CACNB3 c.1289G > A, and SPRY4 c.508 T > A, of uncertain significance. We report results from a large secondary control study of 2081 exomes probing the pathogenicity of the above genes. We analyzed structural variation by four different algorithms including de novo genome assembly. We confirmed a likely contributory 165 kb de novo heterozygous 1q43 microdeletion missed by clinical microarray. The de novo assembly resulted in unmasking hidden genome instability that was missed by standard re-alignment based algorithms. We also interrogated regulatory sequence variation for known and hypothesized ID genes and present useful strategies for WGS data analyses for non-coding variation. CONCLUSION: This study provides an extensive analysis of WGS in the context of ID, providing genetic and structural insights into ID and yielding diagnoses.
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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".