# Simplifying causal gene identification in GWAS loci

## TL;DR

This paper introduces CALDERA, a simpler and effective tool for identifying causal genes in genetic studies, outperforming complex models with fewer inputs.

## Contribution

CALDERA is a novel gene prioritization tool that simplifies causal gene identification in GWAS loci using a logistic regression model with minimal features.

## Key findings

- A simple logistic regression model performed as well as complex models like XGBoost in gene prioritization.
- CALDERA achieved state-of-the-art performance in benchmarking datasets compared to existing methods.
- Applying CALDERA to UK Biobank traits predicted 11,956 putative causal genes, resolving up to 52% of loci.

## 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 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 (e.g., 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.

Genome-wide association studies are a type of genetic study that have identified many genetic mutations that are involved in disease. However, in most cases the genes that are affected by these mutations are unknown. To predict these “effector genes”, we introduce a new tool called CALDERA. We show that existing tools may be unnecessarily complex: CALDERA achieves state-of-the-art prediction of known effector genes despite using a simpler machine learning model and far fewer input variables. By applying CALDERA to the results of genetic studies for 93 different traits and diseases, we were able to predict the likely effector gene for 52% of all trait- and disease-associated mutations for a total of 11,956 likely effector genes. By accurately linking mutations to genes, we gain a better understanding of disease biology and uncover potential opportunities to treat disease by manipulating the function of these genes.