# Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art

## TL;DR

This paper reviews methods for identifying genes that cause human diseases using genetic and molecular data.

## Contribution

The paper provides an updated overview of causal gene inference methods and their interplay in molecular studies.

## Key findings

- Colocalization helps identify shared genetic signals between traits.
- Mendelian randomization improves causal inference by reducing confounding.
- Network-based approaches model complex gene relationships but face limitations like pleiotropy.

## Abstract

In the last decade, genome‐wide association studies (GWAS) have identified tens of thousands of common variants associated with a wide array of complex traits and diseases. Integration of GWAS with molecular data has informed the development of statistical tools for causal gene discovery. In this paper, we give an overview of commonly used causal inference methods and discuss the strengths and limitations of colocalization, Mendelian randomization (MR) and network‐based approaches. Colocalization is often used to assess whether the genetic association signals for two traits arise from the same causal variant, thereby strengthening inferred causal associations. MR was developed to tackle issues of confounding and reverse causality, providing a rigorous approach to causal inference and demonstrating improved false discovery rates. Unlike MR, network‐based analyses employ a discovery approach and model complex relationships between multiple variables. All causal inference methods are, to varying degrees, susceptible to spurious associations due to genetic confounding, pleiotropy and linkage disequilibrium. Here, we discuss the latest developments in the field of causal gene inference and limitations of these methods. We give an overview of interplay between different approaches as well as practical applications with reference to published examples in context of heart disease.