Powered for Success: Considerations for Using the Candidate Gene Approach in Rheumatic Diseases in the Post-genomics Era
Bibliographic record
Abstract
Genetic factors play a substantive role in the susceptibility of ankylosing spondylitis (AS), as evidenced by its high heritability (> 90%) and considerable recurrence risk ratio (λs = 50–80)1. Powered by 3 AS genome-wide association studies (GWAS), 26 genetic loci have reached genome-wide significance, accounting for about 25% of the overall heritability1,2,3. The overwhelming majority of the genetic contribution is provided by the HLA-B27 variant. The most efficient method for gene identification at present appears to be association-based studies, which integrate genetic and epidemiological principles. Association-based studies have benefited immensely from the characterization of a large number of single-nucleotide polymorphism (SNP) markers, linkage disequilibrium (LD) data from the HapMap project, and more recently, the 1000 Genomes Project (www.1000genomes.org/), and the development of high-throughput genotyping technologies. The candidate gene approach focuses on associations between genetic variation within prespecified genes of interest and disease phenotypes. The selection of candidate genes is most often based on a priori knowledge of the proposed gene function on a particular trait. In this issue of The Journal , Nossent, et al present results of a cross-sectional and longitudinal study examining the relationship between 2 tumor necrosis factor-α (TNF-α) gene promoter polymorphisms with serum TNF levels and clinical outcomes, in a white Norwegian AS cohort4. Multiple lines of evidence support a key role for TNF-α in AS pathogenesis. Despite the lack of consistent association between TNF-α promoter polymorphisms and AS susceptibility5,6, it is conceivable that variants from this gene are involved in disease expression, such as extraarticular manifestations or disease severity, or with selected endophenotypes such as serum TNF-α levels or pharmacogenetic response. The study by Nossent, et al comprised a total of 335 patients with AS. They reported that the TNF-α –308 … Address correspondence to Dr. Rahman, 154 Le Marchant Road, 1 South, St. John’s, Newfoundland A1C 5B8, Canada. E-mail: prahman{at}mun.ca
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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 | 0.395 | 0.566 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".