Ankylosing spondylitis: beyond genome-wide association studies
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article discusses genomic investigations in ankylosing spondylitis (AS) beyond genome-wide association (GWA) studies, but prior to this, genetic variants achieving genome-wide significance will be summarized highlighting key pathways contributing to disease pathogenesis. RECENT FINDINGS: Evidence suggests that disease pathogenesis is attributed to a complex interplay of genetic, environmental and immunological factors. GWA studies have greatly enhanced our understanding of AS pathogenesis by illuminating distinct immunomodulatory pathways affecting innate and acquired immunity, most notably the interleukin-23/interleukin-17 pathway. However, despite the wealth of new information gleaned from such studies, a fraction of the heritability (24.4%) has been explained. This review will focus on investigations beyond GWA studies including copy number variants, gene expression profiling, including microRNA (miRNA), epigenetics, rare variants and gene-gene interactions. SUMMARY: To address the 'missing heritability' and advance beyond GWA studies, a concerted effort involving rethinking of study design and implementation of newer technologies will be required. The coming of age of next-generation sequencing and advancements in epigenetic and miRNA technologies, combined with familial-focused investigations using well-characterized cohorts, is likely to reveal some of the hidden genomic mysteries associated with AS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".