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Existing GWAS gene prioritization tools are powerful but often use complex black box models trained on datasets containing biases. Here, we used a data-driven approach to construct a truth set of causal genes in 200 GWAS loci. We found that a simple logistic regression model performed as well as a more complex XGBoost model, and that many commonly-used gene prioritization features could be removed without meaningfully affecting performance (\n                    <jats:italic>e.g.<\/jats:italic>\n                    , expression quantitative trait locus colocalization and Mendelian randomization). We present CALDERA, a gene prioritization tool that uses a logistic regression model and uses just four input features. In independent benchmarking datasets of resolved GWAS loci, CALDERA achieved state-of-the-art performance in comparison with other methods (FLAMES, L2G, and cS2G). CALDERA outputs causal gene probabilities for all genes in a given GWAS locus and we show that these probabilities are well-calibrated. Applying CALDERA to 93 UK Biobank traits, we predicted 11,956 putative causal genes, potentially resolving up to 52% of loci. Overall, CALDERA provides a powerful solution for prioritizing potentially causal genes in GWAS loci that minimizes the data processing required to construct input features and generates an easily-interpretable output score.\n                  <\/jats:p>","DOI":"10.1371\/journal.pgen.1012079","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:32:06Z","timestamp":1773768726000},"page":"e1012079","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Simplifying causal gene identification in GWAS 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