# Multi-omic Causal Discovery using Genotypes and Gene Expression

arXiv: https://arxiv.org/abs/2505.15866 · HTML: https://mlsanity.com/paper/2505.15866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 2 of 2)

- [Ancestral Causal Inference](https://mlsanity.com/paper/1606.07035/) (2016)
- [Causal discovery under a confounder blanket](https://mlsanity.com/paper/2205.05715/) (2022)

## Built on by (most cited first, 1 of 1)

- [Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data](https://mlsanity.com/paper/2607.04527/) (2026)

## Abstract (arXiv)

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.