利用基因型先验信息，高效推断基因调控中的因果关系。
Multi-omic Causal Discovery using Genotypes and Gene Expression
- 以基因型为因果锚点，从空矩阵开始逐步构建基因表达的因果网络。
- 在真实和合成数据上均实现高精度因果推断，显著减少错误连接。
- 适合功能基因组学、药物研发等需要解析基因调控机制的研究者。
多组学数据中的因果发现对于理解基因调控机制至关重要，但受限于高维性、直接与间接关系区分困难以及隐藏混杂因素。我们提出GENESIS（GEne Network inference from Expression SIgnals and SNPs），一种基于约束的算法，利用基因型的天然因果顺序来推断转录组数据中的祖先关系。与传统方法从全连接图出发不同，GENESIS初始为空的祖先矩阵，通过一系列可证明正确的边际和条件独立性检验，迭代填充直接、间接或非因果关系。通过将基因型作为固定的因果锚点，GENESIS为经典因果发现算法提供了一个有原则的“起点”，将搜索空间限制在生物学合理的边中。我们在合成及真实基因组数据集上测试了GENESIS。该框架为揭示复杂性状中的因果通路提供了强大工具，有望应用于功能基因组学、药物发现和精准医学。
原文摘要 · Abstract (English)
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.
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