A joint transcriptome-wide association study across multiple tissues identifies new candidate susceptibility genes for breast cancer
Bibliographic record
Abstract
Abstract Genome-wide association studies (GWAS) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWAS) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify novel candidate genes, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 47 tissues from the Genotype-Tissue Expression data using a multivariate adaptive shrinkage method along with association summary statistics from the Breast Cancer Association Consortium and UK Biobank data. We identified 380 genes at 129 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold (p < 2.36 × 10 −6 ). Of them, 29 genes were located in 11 novel regions that were at least 1Mb away from published GWAS hits. The rest of TWAS-significant genes were located in 118 known genomic loci from previous GWAS of breast cancer. After conditioning on previous GWAS index variants, we found that 22 genes located in known GWAS loci remained statistically significant. Our study maps potential target genes in more than half of known GWAS loci and discovers multiple new loci, providing new insights into breast cancer genetics.
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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) | 0.001 | 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 | 0.001 |
| 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".