Using HapMap Tools in Pharmacogenomic Discovery: The Thiopurine Methyltransferase Polymorphism
Bibliographic record
Abstract
One purpose of the International HapMap Project is to provide a genome-wide resource to discover pharmacogenetic determinants of drug response. The thiopurine methyltransferase (TPMT) 719A>G single-nucleotide polymorphism (SNP) causes decreased TPMT activity, increased intracellular thiopurines, and drug toxicities. Using HapMap cell lines and 3.3 million SNPs, we tested whether the TPMT 719A>G SNP could be identified as predicting TPMT phenotype. Assuming TPMT was a candidate gene, five SNPs and four haplotypes predicted TPMT phenotype, two of which were in complete linkage disequilibrium with the functional 719A>G SNP. We also used a genome-wide approach to rank all 17,542 genes as predictors of TPMT activity. A TPMT haplotype, HAP1, significantly predicted TPMT phenotype; however, haplotypes of 96 genes ranked higher than TPMT. Our findings show that HapMap resources are useful for pharmacogenetic discovery when the candidate gene is known, but challenges remain for definitive gene identification when a genome-wide agnostic approach is employed.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".