Multi-omic Causal Discovery using Genotypes and Gene Expression
This work addresses a critical problem in genomics for researchers by providing a more efficient method to uncover causal pathways, though it is incremental as it builds on existing causal discovery approaches.
The paper tackles the challenge of causal discovery in multi-omic datasets by introducing GENESIS, a constraint-based algorithm that uses genotypes as causal anchors to infer gene regulatory relationships, achieving improved accuracy in synthetic and real-world genomic tests.
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.