Using a Genome-Wide Scan and Meta-analysis to Identify a Novel IBD Locus and Confirm Previously Identified IBD Loci
Bibliographic record
Abstract
Seven loci that potentially confer susceptibility to inflammatory bowel disease (IBD) or one of its subtypes have been identified to date; however, most are unconfirmed, and the complete set of loci contributing to disease susceptibility has not yet been determined. The authors aim to identify loci contributing to disease susceptibility in an IBD population from Canada and to compare their results in a systematic manner with those of previously published IBD data sets. The authors performed genome-wide linkage analysis on 63 IBD families from Nova Scotia, Canada. They then undertook a meta-analysis to combine the results of their study with those of the four previously published IBD genome-wide scans with complete data reported. Their genome-wide scan identified three regions of suggestive linkage to IBD: 11p, and The locus on chromosome 11p has not been previously reported. Meta-analysis of multiple scans revealed linked regions corresponding to the, and loci. Meta-analysis of linkage data is a powerful approach for identifying and confirming common susceptibility loci and specifically shows that, and are the major, common IBD susceptibility loci in the populations studied thus far.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".