Parametric Estimation of the Local False Discovery Rate for Identifying Genetic Associations
Bibliographic record
Abstract
Abstract—Many genome-wide association studies have been conducted to identify single nucleotide polymorphisms (SNPs) that are associated with particular diseases or other traits. The local false discovery rate (LFDR) estimated using semiparametric models has enjoyed success in simultaneous inference. However, semiparametric LFDR estimators can be biased because they tend to overestimate the proportion of the nonassociated SNPs. We address the problem by adapting a simple parametric mixture model (PMM) and by comparing this model to the semiparametric mixture model (SMM) behind an LFDR estimator that is known to be conservatively biased. Then, we also compare the PMM with a parametric nonmixture model (PNM). In our simulation studies, we thoroughly analyze the performances of the three models under different values of p1, a prior probability that is approximately equal to the proportion of SNPs that are associated with the disease. When p₁ > 10%, the PMM generally performs better than the SMM. When p₁ < 0.1%, the SMM outperforms PMM. When p₁ lies between 0.1 and 10 percent, both methods have about the same performance. In that setting, the PMM may be preferred since it has the advantage of supplying an estimate of the detectability level of the nonassociated SNPs.
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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 | 0.099 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".