Robust fine-mapping in the presence of linkage disequilibrium mismatch
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Fine-mapping methods based on summary statistics from genome-wide association studies (GWAS) and linkage disequilibrium (LD) information are widely used to identify potential causal variants. However, LD mismatch between the external LD reference panel and the GWAS population is common and can lead to compromised accuracy of fine-mapping. We developed RSparsePro, a probabilistic graphical model with an efficient variational inference algorithm, to perform robust fine-mapping in the presence of LD mismatch. In simulation studies with a varying degree of LD mismatch, RSparsePro identified credible sets with a consistently higher power and coverage than SuSiE. In fine-mapping cis-protein quantitative trait loci, RSparsePro identified credible sets with a consistently higher enrichment of variants with functional impacts and cross-study replication rates. In fine-mapping risk loci for low-density lipoprotein cholesterol in ancestry-specific GWAS, RSparsePro identified biologically relevant variants in drug target genes and implicated potential regulatory mechanisms. RSparsePro is openly available at https://github.com/zhwm/RSparsePro_LD .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it