Epistasis Between Type 2 Diabetes Susceptibility Loci on Chromosomes 1q21‐25 and 10q23‐26 in Northern Europeans
Bibliographic record
Abstract
Characterisation of the interactions between susceptibility loci (epistasis) is central to a full understanding of the genetic aetiology and the molecular pathology of complex diseases. We have examined, in British and French pedigrees, evidence for epistasis between the type 2 diabetes susceptibility loci on chromosomes 1q21-25 and 10q23-26 using two complementary linkage-based approaches. Joint two-locus linkage analysis of 1q and 10q in British pedigrees provided significant evidence for interaction (P < or = 0.003) when comparing a general epistasis model with multiplicative or additive-effects-only models. Conditional linkage analysis (which models epistasis as a deviation from multiplicativity only) confirmed these findings, with significant LOD score increases at the 1q (P = 0.0002) and 10q (P = 0.0023) loci. These analyses provided sizeable reductions in the 1-LOD support intervals for both loci. Analyses of the British and French pedigrees together yielded comparable, but not enhanced, findings, with significant (P < or = 0.003) evidence for epistasis in joint two-locus linkage analysis, and during conditional linkage analysis significant increases in linkage evidence at the 1q (P = 0.0002) and 10q (P = 0.0036) loci. Our findings of epistasis nevertheless substantiate the evidence for genuine genetic effects at both loci, facilitate endeavours to fine-map these loci in population samples, and support further examination of this interaction at the nucleotide level by providing a robust prior hypothesis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".