Genome-wide association analyses of North American Rheumatoid Arthritis Consortium and Framingham Heart Study data utilizing genome-wide linkage results
Bibliographic record
Abstract
The power of genome-wide association studies can be improved by incorporating information from previous study findings, for example, results of genome-wide linkage analyses. Weighted false-discovery rate (FDR) control can incorporate genome-wide linkage scan results into the analysis of genome-wide association data by assigning single-nucleotide polymorphism (SNP) specific weights. Stratified FDR control can also be applied by stratifying the SNPs into high and low linkage strata. We applied these two FDR control methods to the data of North American Rheumatoid Arthritis Consortium (NARAC) study and the Framingham Heart Study (FHS), combining both association and linkage analysis results. For the NARAC study, we used linkage results from a previous genome scan of rheumatoid arthritis (RA) phenotype. For the FHS study, we obtained genome-wide linkage scores from the same 550 k SNP data used for the association analyses of three lipids phenotypes (HDL, LDL, TG). We confirmed some genes previously reported for association with RA and lipid phenotypes. Stratified and weighted FDR methods appear to give improved ranks to some of the replicated SNPs for the RA data, suggesting linkage scan results could provide useful information to improve genome-wide association studies.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".