Were genome‐wide linkage studies a waste of time? Exploiting candidate regions within genome‐wide association studies
Bibliographic record
Abstract
A central issue in genome-wide association (GWA) studies is assessing statistical significance while adjusting for multiple hypothesis testing. An equally important question is the statistical efficiency of the GWA design as compared to the traditional sequential approach in which genome-wide linkage analysis is followed by region-wise association mapping. Nevertheless, GWA is becoming more popular due in part to cost efficiency: commercially available 1M chips are nearly as inexpensive as a custom-designed 10 K chip. It is becoming apparent, however, that most of the on-going GWA studies with 2,000-5,000 samples are in fact underpowered. As a means to improve power, we emphasize the importance of utilizing prior information such as results of previous linkage studies via a stratified false discovery rate (FDR) control. The essence of the stratified FDR control is to prioritize the genome and maintain power to interrogate candidate regions within the GWA study. These candidate regions can be defined as, but are by no means limited to, linkage-peak regions. Furthermore, we theoretically unify the stratified FDR approach and the weighted P-value method, and we show that stratified FDR can be formulated as a robust version of weighted FDR. Finally, we demonstrate the utility of the methods in two GWA datasets: Type 2 diabetes (FUSION) and an on-going study of long-term diabetic complications (DCCT/EDIC). The methods are implemented as a user-friendly software package, SFDR. The same stratification framework can be readily applied to other type of studies, for example, using GWA results to improve the power of sequencing data analyses.
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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 | 0.187 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".