Aotearoa New Zealand Māori and Pacific Population-amplified Gout Risk Variants: <i>CLNK</i> Is a Separate Risk Gene at the <i>SLC2A9</i> Locus
Bibliographic record
Abstract
Objective The Māori and Pacific (Polynesian) population of Aotearoa New Zealand has a high prevalence of gout. Our aim was to identify potentially functional missense genetic variants in candidate inflammatory genes amplified in frequency that may underlie the increased prevalence of gout in Polynesian populations. Methods A list of 712 inflammatory disease-related genes was generated. An in silico targeted exome set was extracted from whole genome sequencing data in people with gout of various ancestral groups (Polynesian, European, East Asian; n = 55, 780, 135, respectively) to identify Polynesian-amplified common missense variants (minor allele frequency > 0.05). Candidate functional variants were tested for association with gout by multivariable-adjusted regression analysis in 2528 individuals of Polynesian ancestry. Results We identified 26 variants common in the Polynesian population and uncommon in the European and East Asian populations. Three of the 26 population-amplified variants were nominally associated with the risk of gout (rs1635712 [KIAA0319], ORmeta = 1.28, Pmeta = 0.03; rs16869924 [CLNK], ORmeta = 1.37, Pmeta = 0.002; rs2070025 [fibrinogen A alpha chain (FGA)], ORmeta = 1.34, Pmeta = 0.02). The CLNK variant, within the established SLC2A9 gout locus, was genetically independent of the association signal at SLC2A9. Conclusion We provide nominal evidence for the existence of population-amplified genetic variants conferring risk of gout in Polynesian populations. Polymorphisms in CLNK have previously been associated with gout in other populations, supporting our evidence for the association of this gene with gout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".