ArticlemedRxiv : the preprint server for health sciences2025
Simplifying causal gene identification in GWAS loci.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Updated by
Authors and funding
5 authors.
Funding
Abstract
Genome-wide association studies (GWAS) help to identify disease-linked genetic variants, but pinpointing the most likely causal genes in GWAS loci remains challenging. Existing GWAS gene prioritization tools are powerful but often use complex black box models trained on datasets containing unaddressed biases. Here, we use a data-driven approach to construct a truth set of causal genes in 406 GWAS loci. We train a gene prioritization tool, CALDERA, that uses a simple logistic regression model with L1 regularization and corrects for potential confounders. Using three independent benchmarking datasets of resolved GWAS loci, we compare the performance of CALDERA with three other methods (FLAMES, L2G, and cS2G). CALDERA outperforms all these methods in two out of three datasets and ranks second in the remaining dataset. We demonstrate that CALDERA prioritizes genes with expected properties, such as mutation intolerance (OR = 1.751 for pLI > 90%, P = 8.45×10
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.