Common Genetic Variation at <i>PTEN</i> and Risk of Sporadic Breast and Prostate Cancer
Bibliographic record
Abstract
PTEN frequently shows loss of heterozygosity in breast and prostate cancers, and mutations in this gene are responsible for Cowden disease, a rare Mendelian syndrome that includes breast cancer as part of its phenotype. Thus, PTEN serves as a candidate susceptibility gene for both breast and prostate cancer risk. Whether common inherited variation (either coding or noncoding) at the PTEN locus contribute to nonfamilial, sporadic breast and prostate cancer risk is not known. In this study, we employed a linkage disequilibrium-based approach to test for association between common genetic variation at the PTEN locus and breast and prostate cancer risk in African-American, Native Hawaiian, Japanese, Latina, and White men and women in the Multiethnic Cohort Study. We genotyped 17 common single nucleotide polymorphisms (SNP; >/=5% frequency in at least one ethnic group) spanning the PTEN gene to define the common alleles in these populations. These SNPs were in strong linkage disequilibrium, indicating that our survey captured most of the common sequence variation across this locus. Eight tagging SNPs were selected to predict the common PTEN haplotypes (>/=0.05 frequency) in these populations (two additional tagging SNPs were required for African Americans). These SNPs were evaluated in a breast cancer case-control study (cases, n = 1,615; controls, n = 1,962) and prostate cancer case-control study (cases, n = 2,320; controls, n = 2,290) nested within the Multiethnic Cohort Study. Multiple testing was explicitly accounted for by applying a permutation-based framework. We found no strong association with any common haplotype in relation to breast or prostate cancer risk. In summary, our results show that common variants in PTEN do not substantially influence risk of these two common cancers.
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".