Genetic and Clinical Analysis of <i> <scp>ABCA</scp> 4 </i> ‐Associated Disease in African American Patients
Bibliographic record
Abstract
Autosomal recessive Stargardt disease (STGD1) is caused by hundreds of mutations in the ABCA4 gene, which are often specific to racial and ethnic groups. Here, we investigated the ABCA4 variation and their phenotypic expression in a cohort of 44 patients of African American descent, a previously under-characterized racial group. Patients were screened for mutations in ABCA4 by next-generation sequencing and array-comparative genomic hybridization (aCGH), followed by analyses for pathogenicity by in silico programs. Thorough ophthalmic examination was performed on all patients. At least two (expected) disease-causing alleles in the ABCA4 gene were identified in 27 (61.4%) patients, one allele in 11 (25%) patients, and no ABCA4 mutations were found in six (13.6%) patients. Altogether, 39 different disease-causing ABCA4 variants, including seven new, were identified on 65 (74%) chromosomes, most of which were unique for this racial group. The most frequent ABCA4 mutation in this cohort was c.6320G>A (p.(R2107H)), representing 19.3% of all disease-associated alleles. No large copy number variants were identified in any patient. Most patients reported later onset of symptoms. In summary, the ABCA4 mutation spectrum in patients of West African descent differs significantly from that in patients of European descent, resulting in a later onset and "milder" disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".