A Swedish genome-wide haplotype association analysis identifies novel candidate loci associated with endometrial cancer risk
Bibliographic record
Abstract
Genome-wide association studies [GWAS] have identified a limited number of endometrial cancer risk loci by analyzing single nucleotide polymorphisms [SNPs]. We hypothesized that analyzing haplotypes rather than SNPs could provide novel and more detailed information on genetic cancer susceptibility loci. To examine the association of a SNP or haplotype with endometrial cancer risk we performed a two-stage haplotype GWAS. The discovery GWAS included a sub-cohort of 1,116 Swedish endometrial cancer cases and 5,021 controls from previously published GWAS data. A sliding window analysis was employed with window sizes of 1-25 SNPs using a logistic regression model. The Swedish haplotype analysis identified 15 novel candidate risk loci (2q31.1, 4p16.1, 4p15.31, 6q13, 7p21.1, 9p13.3, 10q26.3, 11q21, 12q13.11, 13q12.11, 15q13.3, 16q24.3, 19q13.32, 20p12.3 and 22q13.2) with OR ranging from 1.6 to 3.3 and p-values from 4.25 × 10-8 to 9.86 × 10-15. A second replication haplotype analysis of the Swedish novel loci was performed using two cohorts from Belgium and Germany. In spite of small sample sizes in the replication cohorts, there was still support for most loci with positive ORs. In addition, the findings in the two European cohorts motivates further studies to search for founder haplotypes. These novel findings suggested that endometrial cancer loci, identified through haplotype analysis, conferred a higher risk compared to previous single-variant GWAS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".