Candidate SNP analyses integrated with mRNA expression and hormone levels reveal influence on mammographic density and breast cancer risk
Bibliographic record
Abstract
Background: Mammographic density (MD) is a well-known risk factor for breast cancer. Genetic factors may account for as much as 30-60% of variation in MD, but the specific genes responsible for MD remain largely unknown. In the current study, we use a candidate gene approach to identify genes with a putative effect on MD. Genotypic profiles of single nucleotide polymorphisms (SNPs) within these genes were obtained and tested for association with MD. In addition expression profiles and hormone data were used to further investigate these associations. Methods: We have analyzed 257 SNPs in 165 genes in two sample materials (n=454) using a discovery and verification approach in order to identify SNP markers of MD. Hormone and, when available, gene expression levels obtained from biopsies taken from breasts with varying density were also included in the analyses in order to investigate the functional role of the identified genetic factors in MD. Results: We identified 28 SNPs associated with MD in both datasets, ten of which have a p-value ≤ 0.05. Of these ten, seven are associated in cis (p≤ 0.05) with mRNA expression levels measured from breast biopsies of which four are directly involved in the signalling, metabolism and regulation of estradiol. Conclusion: SNPs residing in genes belonging to the estradiol-signaling pathway were found associated with MD in two cohorts of Norwegian postmenopausal women. Coupled with gene expression, these results aid in the understanding of the molecular signature in mammographically dense breasts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".