Identifying Causal Genes and Building Regulatory Networks in Crops Using the CisTrans-ECAS Method

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method's core principle is the decomposition of gene expression into two distinct components: a <i>cis</i>-expression component (<i>cis</i>-EC), regulated by local genetic variants, and a <i>trans</i>-expression component (<i>trans</i>-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding. Key features • Pinpointing causal genes with high precision: Integrates <i>cis-</i> and <i>trans</i>-expression components to distinguish true causal genes from LD artifacts, even for small-effect loci. • Reconstructing gene regulatory networks: Uses gene expression as molecular traits to identify upstream regulators, revealing complex molecular regulatory pathways. • Versatile and reproducible workflow: An R-based pipeline using PLINK and GCTA, applicable to rice and other species with population genomics and transcriptomics data. • Experimentally validated reliability: The method successfully identified key genes <i>OsMADS17</i> and <i>SDT</i> that regulate rice spikelet number, with their regulatory relationship confirmed by molecular experiments.