Analysis of 47 Non-MHC Ankylosing Spondylitis Susceptibility Loci Regarding Associated Variants across Whites and Han Chinese
Bibliographic record
Abstract
Objective. To present a systematic evaluation of 47 non-MHC ankylosing spondylitis (AS) susceptibility loci that have been initially discovered through white genome-wide association studies in Han Chinese. Methods. Originally, 10,743 samples representing north and south Chinese in 4 datasets were obtained. After data quality control and imputation, metaanalysis results of 94,621 variants within 47 loci were extracted. Four ERAP1 single-nucleotide polymorphisms (SNP) and HLA-B27 tag SNP rs13202464 were used for interaction analysis. Population-attributable risk percentages of AS-associated variants were compared. Functional annotations of AS-associated variants were conducted using HaploReg, RegulomeDB, and rVarBase databases. Results. We revealed 16 AS-associated variants with nominal evidence in Han Chinese, including rs10865331 (p = 6.30 × 10−10), rs10050860 (p = 4.09 × 10−5) and rs8070463 (p = 1.03 × 10−4). Potential susceptible SNP within these 47 loci were also identified, such as rs13024541 (2p15), rs17401719 (5q15), and rs62074054 (17q21). Epistatic interactions between 3 ERAP1 SNP (rs17401719, rs30187, and rs10050860) and HLA-B27 were confirmed. Among the 16 AS-associated variants, rs30187 showed weaker risk effect, while rs10050860 and rs12504282 seemed to attribute more risk in Han Chinese than in whites. Further genomic annotation pinpointed 35 candidate functional SNP, especially in the 2p15, ERAP1, and NPEPPS-TBKBP1 regions. Conclusion. Our results provided a detailed spectrum of all the reported non-MHC AS susceptibility loci in Han Chinese, which comprehensively exhibited the ethnic heterogeneity of AS susceptibility and highlighted that 2p15, ERAP1, and NPEPPS-TBKBP1 regions may play a critical role in AS pathogenesis 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.002 | 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".