Multi-omic Causal Discovery using Genotypes and Gene Expression

TL;DR

This paper introduces GENESIS, a new causal discovery algorithm that uses genotypes as anchors to infer gene regulatory networks from multi-omic data, improving accuracy and biological plausibility.

Contribution

GENESIS is a constraint-based causal discovery method that leverages genotypes to efficiently infer ancestral relationships in transcriptomic data, addressing high dimensionality and hidden confounders.

Findings

Successfully tested on synthetic datasets

Validated on real-world genomic data

Shows improved causal inference accuracy

Abstract

Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, differentiation of direct from indirect relationships, and hidden confounders. We introduce GENESIS (GEne Network inference from Expression SIgnals and SNPs), a constraint-based algorithm that leverages the natural causal precedence of genotypes to infer ancestral relationships in transcriptomic data. Unlike traditional causal discovery methods that start with a fully connected graph, GENESIS initialises an empty ancestrality matrix and iteratively populates it with direct, indirect or non-causal relationships using a series of provably sound marginal and conditional independence tests. By integrating genotypes as fixed causal anchors, GENESIS provides a principled ``head start'' to classical causal discovery algorithms, restricting the search space to biologically plausible edges. We test GENESIS on synthetic and real-world genomic datasets. This framework offers a powerful avenue for uncovering causal pathways in complex traits, with promising applications to functional genomics, drug discovery, and precision medicine.