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Record W2069667220 · doi:10.1038/sj.clpt.6100135

Using HapMap Tools in Pharmacogenomic Discovery: The Thiopurine Methyltransferase Polymorphism

2007· article· en· W2069667220 on OpenAlexfundno aff
Terreia S. Jones, Wentao Yang, William E. Evans, Mary V. Relling

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2007
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute of General Medical SciencesAmerican Lebanese Syrian Associated CharitiesMcGill University
KeywordsInternational HapMap ProjectThiopurine methyltransferasePharmacogenomicsSingle-nucleotide polymorphismHaplotypePharmacogeneticsLinkage disequilibriumSNPBiologyGenetics1000 Genomes ProjectCandidate geneGenotypingSNP genotypingImputation (statistics)Computational biologyGeneAlleleGenotypeMedicineInternal medicineComputer scienceAzathioprine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.236
GPT teacher head0.497
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2007
Admission routes1
Has abstractyes

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