<i>APC</i> I1307K and clinical management: insights from UK Biobank association analysis of colorectal and other cancer risks in Ashkenazi and non-Ashkenazi whites
Bibliographic record
Abstract
ABSTRACT APC c.3920T>A; p.Ile1307Lys (I1307K), prevalent in individuals of Ashkenazi Jewish (AJ) origin, has been associated with a modestly increased colorectal cancer (CRC) risk. Clinical recommendations for I1307K heterozygotes vary across countries and expert groups, reflecting differences in population frequencies, modest risk estimates, and limited data in non-AJ individuals. We analyzed UK Biobank data comprising 466,315 individuals (8,727 with CRC), using genomic analysis to classify AJ and non-AJ ancestries. I1307K was identified in 7.1% of AJ and 0.08% of non-AJ white participants. No significant association with CRC was observed in AJ (OR: 0.71; 95%CI: 0.17–2.95) or non-AJ white individuals (OR: 1.05; 95%CI: 0.50– 2.22). The previously established OR of 1.7–1.8 for AJ individuals lies within our 95% CI, indicating underpowered results due to limited CRC cases. No significant associations were detected for other cancers. Unbiased, adequately powered CRC case-control studies in non-AJ populations would require cohorts far larger than current resources for feasible analysis. Clinical actionability of I1307K should prioritize risk stratification based on overall CRC risk and ancestry-dependent variant detection rates. However, management strategies need not differ by ancestry once a carrier is identified, as the biological impact of I1307K should be consistent across populations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".