Clinical application of 2.7M Cytogenetics array for CNV detection in subjects with idiopathic autism and/or intellectual disability
Bibliographic record
Abstract
Qiao Y, Tyson C, Hrynchak M, Lopez‐Rangel E, Hildebrand J, Martell S, Fawcett C, Kasmara L, Calli K, Harvard C, Liu X, Holden JJA, Lewis SME, Rajcan‐Separovic E. Clinical application of 2.7M Cytogenetics array for CNV detection in subjects with idiopathic autism and/or intellectual disability. Higher resolution whole‐genome arrays facilitate the identification of smaller copy number variations (CNVs) and their integral genes contributing to autism and/or intellectual disability (ASD/ID). Our study describes the use of one of the highest resolution arrays, the Affymetrix® Cytogenetics 2.7M array, coupled with quantitative multiplex polymerase chain reaction (PCR) of short fluorescent fragments (QMPSF) for detection and validation of small CNVs. We studied 82 subjects with ASD and ID in total (30 in the validation and 52 in the application cohort) and detected putatively pathogenic CNVs in 6/52 cases from the application cohort. This included a 130‐kb maternal duplication spanning exons 64–79 of the DMD gene which was found in a 3‐year‐old boy manifesting autism and mild neuromotor delays. Other pathogenic CNVs involved 4p14, 12q24.31, 14q32.31, 15q13.2‐13.3, and 17p13.3. We established the optimal experimental conditions which, when applied to select small CNVs for QMPSF confirmation, reduced the false positive rate from 60% to 25%. Our work suggests that selection of small CNVs based on the function of integral genes, followed by review of array experimental parameters resulting in highest confirmation rate using multiplex PCR, may enhance the usefulness of higher resolution platforms for ASD and ID gene discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".