Estimating the Frequency of False‐Negative Pharmacogenetic Test Results by Self‐Reported Ancestry
Bibliographic record
Abstract
Concerns about the applicability of pharmacogenetic (PGx) testing across diverse ancestry groups have risen from the underrepresentation of non-European populations in PGx research. Current PGx panels may fail to detect relevant variants in non-European populations, increasing the likelihood of false-negative results. To investigate this, we assessed reference allele (*1) and genotype (*1/*1) frequencies by self-reported ancestry in a cohort of 1086 youth aged 6-24 years who underwent PGx testing. Testing included 10 pharmacogenes (CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, NUDT15, SLCO1B1, TPMT, and VKORC1) using a panel covering all Association for Molecular Pathology Tier 1 alleles and 53% of Tier 2 alleles. Compared with Europeans (n = 727), non-Europeans (n = 359) had higher *1 allele frequencies for CYP2C9, CYP2D6, and CYP3A5 (all P < 0.01), while Europeans had higher frequencies for CYP2C19 and VKORC1 (all P < 0.01). Similarly, *1/*1 genotype frequencies were higher in non-Europeans for CYP2C9 and CYP3A4 (all P < 0.01), but higher in Europeans for VKORC1 (P < 0.01). False-negative estimates exceeded 1% for CYP2B6, CYP2D6, CYP2C9, CYP2C19, and SLCO1B1 in at least one ancestry group. These findings support the notion that *1 allele and *1/*1 genotype frequencies are more frequent in non-Europeans for specific genes, but Europeans are also at comparable risk for false-negative results. Expanded allele coverage on PGx panels could mitigate false-negative risks, improving equity in PGx testing across diverse populations.
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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 | 0.043 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".