Development and evaluation of a transfusion medicine genome wide genotyping array
Bibliographic record
Abstract
BACKGROUND: Many aspects of transfusion medicine are affected by genetics. Current single-nucleotide polymorphism (SNP) arrays are limited in the number of targets that can be interrogated and cannot detect all variation of interest. We designed a transfusion medicine array (TM-Array) for study of both common and rare transfusion-relevant variations in genetically diverse donor and recipient populations. STUDY DESIGN AND METHODS: The array was designed by conducting extensive bioinformatics mining and consulting experts to identify genes and genetic variation related to a wide range of transfusion medicine clinical relevant and research-related topics. Copy number polymorphisms were added in the alpha globin, beta globin, and Rh gene clusters. RESULTS: The final array contains approximately 879,000 SNP and copy number polymorphism markers. Over 99% of SNPs were called reliably. Technical replication showed the array to be robust and reproducible, with an error rate less than 0.03%. The array also had a very low Mendelian error rate (average parent-child trio accuracy of 0.9997). Blood group results were in concordance with serology testing results, and the array accurately identifies rare variants (minor allele frequency of 0.5%). The array achieved high genome-wide imputation coverage for African-American (97.5%), Hispanic (96.1%), East Asian (94.6%), and white (96.1%) genomes at a minor allele frequency of 5%. CONCLUSIONS: A custom array for transfusion medicine research has been designed and evaluated. It gives wide coverage and accurate identification of rare SNPs in diverse populations. The TM-Array will be useful for future genetic studies in the diverse fields of transfusion medicine research.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".