Identification of Variants in Alpha-1-Antitrypsin by High Resolution Melting
Bibliographic record
Abstract
BACKGROUND: Alpha-1-antitrypsin deficiency (AATD) is one of the most common hereditary disorders occurring in populations of European origin and is due to variants in SERPINA1, which encodes a protease inhibitor of neutrophil elastase, limiting lung damage from this enzyme. The World Health Organization has recommended that individuals with chronic obstructive pulmonary disease and asthma be tested for AATD. The development of inexpensive and simple genetic testing will help to meet this goal. METHODS: Primers and synthetic SERPINA1 gene fragments (gBlocks) were designed for 5 AATD-associated variants. PCR was run on a CFX96 Thermal Cycler with High Resolution Melting (HRM) capacity and data analyzed using the supplied HRM-analysis software. Genomic DNA from individuals (n = 86) genotyped for the S and Z variants were used for validation. HRM-analysis was performed on 3 additional samples with low alpha-1-antitrypsin levels inconsistent with the genotype determined in our clinical laboratory. RESULTS: Unique normalized melt curve and difference curve patterns were identified for the AAT variants Z, S, I, F, and MMalton using gBlocks. Similar curve shapes were seen when these primers were used to analyze the gDNA samples. HRM identified the genotypes of the gDNA correctly with 100% concordance. The curve shapes of some samples did not match the melting patterns of the targeted variant. Sequencing was used to identify the variants, including rare AATD variants c.1108_1115delinsAAAAACA (p.Glu370Lysfs*31) and c.1130dup (p.Leu377fs). CONCLUSION: We developed a rapid and inexpensive HRM-analysis method for genotyping of Z, S, MMalton, I, and F variants that was also capable of detecting other variants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".