Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits.

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre) , an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.