Haplotype-Based Association Studies of <i>IGFBP1</i> and <i>IGFBP3</i> with Prostate and Breast Cancer Risk: The Multiethnic Cohort
Bibliographic record
Abstract
Collective evidence suggests that the insulin-like growth factor (IGF) system plays a role in prostate and breast cancer risk. IGF-binding proteins (IGFBP) are the principal regulatory molecules that modulate IGF-I bioavailability in the circulation and tissues. To examine whether inherited differences in the IGFBP1 and IGFBP3 genes influence prostate and breast cancer susceptibility, we conducted two large population-based association studies of African Americans, Native Hawaiians, Japanese Americans, Latinos, and Whites. To thoroughly assess the genetic variation across the two loci, we (a) sequenced the IGFBP1 and IGFBP3 exons in 95 aggressive prostate and 95 advanced breast cancer cases to ensure that we had identified all common missense variants and (b) characterized the linkage disequilibrium patterns and common haplotypes by genotyping 36 single nucleotide polymorphisms (SNP) spanning 71 kb across the loci ( approximately 20 kb upstream and approximately 40 kb downstream, respectively) in a panel of 349 control subjects of the five racial/ethnic groups. No new missense SNPs were found. We identified three regions of strong linkage disequilibrium and selected a subset of 23 tagging SNPs that could accurately predict both the common IGFBP1 and IGFBP3 haplotypes and the remaining 13 SNPs. We tested the association between IGFBP1 and IGFBP3 genotypes and haplotypes for their associations with prostate and breast cancer risk in two large case-control studies nested within the Multiethnic Cohort [prostate cases/controls = 2,320/2,290; breast cases (largely postmenopausal)/controls = 1,615/1,962]. We observed no strong associations between IGFBP1 and IGFBP3 genotypes or haplotypes with either prostate or breast cancer risk. Our results suggest that common genetic variation in the IGFBP1 and IGFBP3 genes do not substantially influence prostate and breast cancer susceptibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".