Simultaneous human platelet antigen genotyping and detection of novel single nucleotide polymorphisms by targeted next‐generation sequencing
Bibliographic record
Abstract
BACKGROUND: Twenty-nine human platelet antigen systems have been described to date, but the majority of current genotyping methods are restricted to the identification of those most commonly associated with alloantibody production in a clinical context. This can result in a protracted investigation if causative human platelet antigens are rare or novel. A targeted next-generation sequencing approach was designed to detect all known human platelet antigens with the additional capability of identifying novel mutations in the encoding genes. STUDY DESIGN AND METHODS: A targeted enrichment, high-sensitivity HaloPlex assay was designed to sequence all exons and flanking regions of the six genes known to encode human platelet antigens. Indexed DNA libraries were prepared from 47 previously human platelet antigen-genotyped samples and subsequently combined into one of three pools for sequencing on an Illumina MiSeq platform. The generated FASTQ files were aligned and scrutinized for each human platelet antigen polymorphism using SureCall data analysis software. RESULTS: Forty-six samples were successfully genotyped for human platelet antigens 1 through 29bw, with an average per base coverage depth of 1144. Concordance with historical human platelet antigen genotypes was 100%. A putative novel mutation in Exon 10 of the integrin β-3 (ITGB3) gene from an unsolved case of fetal neonatal alloimmune thrombocytopenia was also detected. CONCLUSION: A next-generation sequencing-based method that can accurately define all known human platelet antigen polymorphisms was developed. With the ability to sequence up to 96 samples simultaneously, our HaloPlex design could be used for high-throughput human platelet antigen genotyping. This method is also applicable for investigating fetal neonatal alloimmune thrombocytopenia when rare or novel human platelet antigens are suspected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".