Genome sequencing as a platform for pharmacogenetic genotyping: a pediatric cohort study
Bibliographic record
Abstract
Abstract Whole-genome sequencing and whole-exome sequencing have proven valuable for diagnosing inherited diseases, particularly in children. However, usage of sequencing data as a pharmacogenetic screening tool to ensure medication safety and effectiveness remains to be explored. Sixty-seven variants in 19 genes with known effects on drug response were compared between genome sequencing and targeted genotyping data for coverage and concordance in 98 pediatric patients. We used targeted genotyping data as a benchmark to assess accuracy of variant calling, and to identify copy number variations of the CYP2D6 gene. We then predicted clinical impact of these variants on drug therapy. We find genotype concordance across those panels to be > 97%. Concordance of CYP2D6 predicted phenotype between estimates of whole-genome sequencing and targeted genotyping panel were 90%; a result from a lower coverage depth or variant calling difficulties in our whole-genome sequencing data when copy number variation and/or the CYP2D6*4 haplotype were present. Importantly, 95 children had at least one clinically actionable pharmacogenetic variant. Diagnostic genomic sequencing data can be used for pre-emptive pharmacogenetic screening. However, concordance between genome-wide sequencing and target genotyping needs to be characterized for each of the pharmacologically important genes.
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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 | 0.001 | 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 | 0.001 | 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".