Multi-omic Causal Discovery using Genotypes and Gene Expression

Stephen M. Asiedu, David Watson

Track: Tiny paper track (up to 4 pages)

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 initializes 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.

Submission Number: 41