Genome-wide analysis identifies rare copy number variations associated with inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) is an idiopathic, chronic disorder of unclear etiology with an underlying genetic predisposition. Recent genome-wide association studies have identified more than 200 IBD susceptibility loci, but the causes of IBD remain poorly defined. We hypothesized that rare (<0.1% population frequency) gene copy number variations (CNVs) could play an important mechanism for risk of IBD. We aimed to examine changes in DNA copy number in a population-based cohort of patients with IBD and search for novel genetic risk factors for IBD. METHODS: DNA samples from 243 individuals with IBD from the Manitoba IBD Cohort Study and 2988 healthy controls were analyzed using genome-wide SNP microarray technology. Three CNV calling algorithms were applied to maximize sensitivity and specificity of CNV detection. We identified IBD-associated genes affected by rare CNV from comparing the number of overlapping CNVs in IBD samples with the number of overlapping CNVs in controls for each gene. RESULTS: 4,402 CNVs detected by two or three algorithms intersected 7,061 genes, in at least one analyzed sample. Four genes (e.g. DUSP22 and IP6K3) intersected by rare deletions and fourteen genes (e.g. SLC25A10, PSPN, GTF2F1) intersected by rare duplications demonstrated significant association with IBD (FDR-adjusted p-value < 0.01). Of these, ten genes were functionally related to immune response and intracellular signalling pathways. Some of these genes were also identified in other IBD related genome-wide association studies. These suggested that the identified genes may play a role in the risk of IBD. CONCLUSION: Our results revealed new genomic loci associated with IBD, which suggested the role of rare CNVs in IBD risk.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".