Authors' response to a refinement to ‘how many genes underlie the occurrence of common complex diseases in the population?’ by Ramal Moonesinghe
Bibliographic record
Abstract
We appreciate Dr Ramal Moonesinghe's derivation of a closed form to estimate the number of genes needed to achieve a particular population attributable fraction (PAF) assuming that the genotype frequency (G) and associated risk for disease (Rg) are constant. Dr Moonesinghe informed us of his result while our paper was in press. We tried to include this calculation in the paper with him as a co-author, but we were told that it was too late to do so. Under the assumption of constant G and Rg, an unlikely scenario in the real world, our results are consistent with that of the closed form for both additive and multiplicative models. However, the closed formula provides an exact point estimate of the number of genes needed for a PAF, while our approach provides a whole number estimate. For example, for G = 0.1, Rg = 1.2 to achieve a PAF of 30% under additive model, the closed form gives number of genes needed = 21.43, and our algorithm gives 22. Our algorithm will always give the whole number that is just above the desired PAF because we increase the number of genes one at a time during calculation. For the above example, our algorithm calculates the PAF produced by 21 genes, finds that it corresponds to a PAF just <30%, then increases to 22 genes. For 22 genes, the algorithm calculates the corresponding PAF, finds that it is just over 30%, and stops, reporting 22 as the number found.
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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 | 0.016 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.030 | 0.061 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".