Winner’s curse in rare variant analysis: effect size estimation bias depends on effect direction and the association method used
Bibliographic record
Abstract
For complex human traits, a large portion of genetic heritability remains unaccounted for beyond common genetic variants; therefore, estimating the contribution of rare variants (RVs) to the etiology of complex traits is of interest. Research in this domain has primarily focused on gene-based RV testing methods, in which information from multiple variants is combined to maximize statistical power in detecting genes associated with the trait of interest. However, after discovering an association, estimating individual effects becomes challenging due to sample size limitations. Hence, the focus may shift to estimating the average genetic effect (AGE) for the group of RVs analyzed. This study demonstrates that both AGEs and individual variant effects can be influenced by competing upward and downward biases, resulting from the winner’s curse and the heterogeneity of individual variant effects, respectively. Various bias-correction techniques, including bootstrap resampling and likelihood-based methods, have been proposed to address the winner’s curse bias. We conduct a simulation study to illustrate the ramifications of these competing biases on variant effect size estimation and how they complicate the precision of pooled estimates obtained from different bias-correction techniques. We then examine the individual effect estimates of the causal variants across the simulation replicates to show how they may contribute to the observed upward and downward biases when RVs are pooled.
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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 | 0.162 | 0.462 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".