Assessment of fidelity and utility of the whole‐genome amplification for the clinical tests offered in a histocompatibility and immunogenetics laboratory
Bibliographic record
Abstract
Increasing emphasis on the use of molecular tests in a histocompatibility and immunogenetics laboratory (HIL) poses a potential problem of lack of sufficient DNA to perform multiple genetic analyses. In this study, we report the feasibility, fidelity and utility of multiple displacement amplification (MDA) method to perform whole-genome amplification (WGA) to generate DNA specimens that can be analyzed by multiple molecular techniques and can be used for different clinical tests offered by an HIL. The MDA-generated DNA when compared with the native DNA showed 100% congruency in genotyping of 37 genes/loci using multiple downstream molecular techniques: sequence-based typing and sequence-specific primer-based typing for 5 human leukocyte antigen (HLA) class I and II genes (HLA-A, B, C, DRB1 and DQB1), luminex-based sequence-specific oligonucleotide (SSO) genotyping for a panel of 16 killer immunoglobulin-like receptor (KIR) genes and automated fragment size analysis for a panel of 15 short tandem repeat (STR) loci and amelogenin gene. For post-allogeneic hematopoietic cell transplantation (HCT) chimerism analysis, MDA-generated DNA appeared useful for enriching pre-transplant DNA but not for enriching post-transplant chimeric DNA. Overall, our results show that MDA-based WGA could generate DNA of high yield and fidelity that can be used for various clinical tests and research purposes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".