Estimating Disease Risk Associated with Mutated Genes in Family-Based Designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Many clinical decisions require accurate estimates of disease risk associated with inherited gene mutations. While several family-based designs have been proposed, their relative advantages remain unclear. METHODS: We considered four commonly-used family-based designs and evaluated their performance in terms of accuracy and efficiency under several genetic models via simulation studies. We also derived and assessed several ascertainment-corrected likelihood methods for analyzing the simulated data and real data from 12 HNPCC pedigrees from Newfoundland. RESULTS: We found that the design efficiency depends on the question of interest: the clinic-based family design with random probands yields the most efficient estimate of genetic relative risks, whereas the population-based family design with mutation carrier probands provides the most efficient penetrance estimates. For a particular question, an ascertainment correction seems possible using regular likelihood methods but the presence of genetic heterogeneity due to a strong second gene effect can lead to some bias in the risk estimation. CONCLUSIONS: This work gives a general methodological framework for analyzing family-based designs in gene characterization studies and provides more rationale for the choice of an efficient design and an appropriate likelihood method to estimate the risk associated with an inherited gene mutation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it