Prevalence of genetic variants associated with inflammatory bowel disease in a healthy First Nations cohort
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease is the result of both genes and environment. Canadian First Nations people, despite living in a region with a high prevalence of inflammatory bowel disease, are relatively protected from this disease. We aimed to compare the carriage of genetic variants associated with inflammatory bowel disease in healthy First Nations and white people. METHODS: DNA was extracted from the venous blood of healthy First Nations (n = 340) and white (n = 285) participants from Manitoba. Genotyping was performed for 69 single nucleotide polymorphisms (SNPs) with known or suspected associations with inflammatory bowel disease. We compared the genotypes between groups by logistic regression, adjusting for multiple testing. We calculated a risk score for the NOD2 gene by adding the number of risk alleles at three important NOD2 SNPs (G908R, R702W and 3020insC). RESULTS: We found genetic variation between white and First Nations participants at 45 of 69 SNPs. Notably, carriage of the ATG16L1 T300A mutation was lower in First Nations participants (p = 4.1 × 10(-30)). Cumulative carriage of important NOD2 variants was significantly lower among First Nations participants (3.9% v. 15.2%; p < 0.0001 for risk score) than among white participants. Risk variants in IL23R (p = 0.014) and IL12B (p = 1.2 × 10(-16)), among others, were more prevalent among First Nations participants than among white participants. INTERPRETATION: The low prevalence of variants associated with bacterial processing and handling in First Nations people may explain their relative protection from inflammatory bowel disease. Increased carriage of a number of risk variants, for example in the interleukin-23/Th17 pathway, is especially intriguing given their importance in other inflammatory diseases of high incidence in First Nations 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".