Impact of Genotype Misclassification on Genetic Association Estimates and the Bayesian Adjustment
Bibliographic record
Abstract
Genotypes of single nucleotide polymorphisms are subject to misclassification. If ignored, such misclassification can seriously distort the estimated genotype effects on the disease or outcome of interest. Validation data (gold standard or replicated surrogates) are required to assess the degree of misclassification and make adjustments. In practice, gold standard measurements may be unavailable or impractical. Collecting replicated surrogates is a reasonable option for validation data. In most practical applications, collecting replicated surrogates on all study subjects is not feasible; however, obtaining replicated surrogates on a subsample of the study population may be quite feasible. Generating duplicate data for a subsample of the study population is now common practice among genotyping laboratories. The authors propose a Bayesian method that can adjust for genotype misclassification using partial validation data. Simulation results show that the proposed method substantially reduces misclassification bias from the estimated genotype-disease association and provides appropriate uncertainty assessment, as well as improves other desirable properties of the estimated effects. The authors also provide an example showing the application of the proposed method to study data relating non-Hodgkin lymphoma to a single nucleotide polymorphism in the aryl hydrocarbon receptor gene.
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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 | 0.062 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".