Table

In the information age, global scientific research results have been shared like never before, and the huge amount of data generated by researchers around the world is centrally stored in several public databases, such as UK Biobank, FinnGen, GWAS Catalog, IEU OpenGWAS, etc. However, the current utilization efficiency of these databases is obviously insufficient.In this paper, the Genome-wide Precision Causal Network Translational Medical Paradigm (GPCN-TMP) is proposed for the first time. The paradigm makes full use of global publicly available genomic and multi-omics databases, with the core approach involving Mendelian Randomization (MR) and Generalized Summary-data-based Mendelian Randomization (GSMR) to discover a large number of potential causal relationships through automated large-scale data-driven analysis and rigorous statistical validation.In this paper, we have conducted a comprehensive validation and demonstration by applying the GPCN-TMP theory using the cystatin family as an example. Without any predetermined research directions, we applied self-developed large-scale automated MR and GSMR batch analysis codes to extract relevant data from global public databases, perform preliminary causal network analysis and obtain a large number of potential relationship results. Subsequent cross-validation with existing literature fully demonstrated the scientific validity and feasibility of the GPCN-TMP paradigm.