Characterisation of pharmacogenomic variation in the Shetland and Orkney Isles in Scotland
Bibliographic record
Abstract
Genetic variation is partly responsible for variability in drug response across populations. However, the full catalogue of pharmacogenetic variants and their distribution are yet to be established, thus posing challenges in implementing individualised medicine in understudied populations. This study aimed to characterise variation in key drug response genes across founder populations from the Northern Isles of Scotland. We analysed whole genome sequence datasets from 498 Shetlanders and 1372 Orcadians, the majority of whom are research participants in the Viking Genes programme, and compared the genetic variation in 41 selected pharmacogenes with observed distributions in other European datasets. From this gene-set, we present frequencies of known and potentially novel star alleles (haplotypes and structural variants) for 18 core pharmacogenes analysed using StellarPGx, and variant distributions in 23 other selected pharmacogenes with existing clinical annotations in ClinPGx ( https://www.clinpgx.org ). Despite important differences in the frequencies of rare and/or novel potentially high-impact variants, the distributions of the well-studied common actionable pharmacogene star alleles do not vary dramatically across Shetland, Orkney, and the European populations represented in the 1000 Genomes Project or allele frequency meta-analyses in ClinPGx. Importantly, for gene-drug pairs with Clinical Pharmacogenetics Implementation Consortium Guidelines, we estimated (based on diplotypes alone) that the proportion of participants in the combined dataset that may benefit from a change in dose/drug ranged from 0 to 50.5%, depending on the gene-drug pair. Overall, understanding the landscape of pharmacogenomic variation in Shetland and Orkney is an important step towards implementation of precision medicine across rural Scotland.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".