<scp>GenoStaR</scp> : An R Package for Genotype to Star Allele Conversion for Major Cytochrome <scp>P450</scp> Family of Genes
Bibliographic record
Abstract
Pharmacogenomics enables the personalization of drug therapy by linking genetic variations to differences in drug metabolism, efficacy, and risk of adverse reactions. Genetic polymorphisms within cytochrome P450 (CYP) genes significantly affect enzyme activity, influencing drug plasma levels, responses, and safety. Central to this process is accurate genotype-to-phenotype translation, especially for the CYP enzyme family, which metabolizes 70-80% of clinically used drugs. To address this, we have developed GenoStaR, an R package that converts genotypes into star alleles and predicts the associated metabolizer status for major cytochrome P450 genes-CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5. GenoStaR assigns star alleles using single-nucleotide polymorphisms, insertion-deletion variants, and structural variants. Given genotype data, GenoStaR uses comprehensive allele definition tables to determine diplotypes, activity scores, and predicted metabolizer status. The tool accounts for complex scenarios, including CYP2D6 copy number variations, using a tiered matching strategy and structural variant detection. We evaluated GenoStaR using two datasets. The first from the Centre for Addiction and Mental Health Individualized Medicine: Pharmacogenetics Assessment and Clinical Treatment (IMPACT) study (n = 8,287), which included genotyping data, along with star allele information from a commercial pharmacogenetic test. The second, the Toronto Schizophrenia sample (n = 188), with in-house genotype data and manually validated star alleles. GenoStaR achieved 100% concordance in diplotype calls across both datasets. GenoStaR offers a reliable, efficient, and accurate solution for converting genotypes into star alleles and predicting CYP-related metabolizer status. Its performance on a large validation dataset highlights its potential to enhance pharmacogenomic testing in clinical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.031 |
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".