The contribution of rare variation to prostate cancer heritability
Bibliographic record
Abstract
Although genome-wide association studies (GWAS) have found more than a hundred common susceptibility alleles for prostate cancer, the GWAS reported variants jointly explain only 33% of risk to siblings, leaving the majority of the familial risk unexplained. We use targeted sequencing of 63 known GWAS risk regions in 9,237 men from four ancestries (African, Latino, Japanese, and European) to explore the role of low-frequency variation in risk for prostate cancer. We find that the sequenced variants explain significantly more of the variance in the trait than the known GWAS variants, thus showing that part of the missing familial risk lies in poorly tagged causal variants at known risk regions. We report evidence for genetic heterogeneity in SNP effect sizes across different ancestries. We also partition heritability by minor allele frequency (MAF) spectrum using variance components methods, and find that a large fraction of heritability (0.12, s.e. 0.05; 95% CI [0.03, 0.21]) is explained by rare variants (MAF<0.01) in men of African ancestry. We use the heritability attributable to rare variants to estimate the coupling between selection and allelic effects at 0.48 (95% CI of [0.19, 0.78]) under the Eyre-Walker model. These results imply that natural selection has driven down the frequency of many prostate cancer risk alleles over evolutionary history. Overall our results show that a substantial fraction of the risk for prostate cancer in men of African ancestry lies in rare variants at known risk loci and suggests that rare variants make a significant contribution to heritability of common traits.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".