Abstract: Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is a major afforestation species in southern China, currently advancing through multi-objective, advanced-generation breeding. As breeding programs progress, maintaining genetic diversity and minimizing inbreeding depression are critical. Traditional pedigree management, which relies primarily on static documentation, is often inefficient, inaccurate, and non-interactive, making it difficult to support data-driven parental selection. To overcome these limitations, we developed ChineseFirPedV, a web-based system for interactive genealogy visualization and kinship management tailored to Chinese fir breeding. Built with Python, the system uses NetworkX for constructing pedigree graphs and Pyvis for dynamic visualization. It integrates both traditional pedigree records and SNP genotyping data from 174 elite Chinese fir individuals to generate both network-based genealogical diagrams and kinship cluster maps. The system offers interactive tools for exploring kinship networks, customizing node attributes, and designing mating strategies. When molecular data are available, the system computes genomic relationship matrices and visualizes them as clustering graphs to highlight relatedness patterns. The integration of pedigree and genomic information enhances accuracy in identifying optimal parental combinations and detecting anomalies or errors in recorded lineage. ChineseFirPedV effectively supports kinship verification, pedigree reconstruction, and breeding value estimation, improving decision-making in advanced-generation breeding. Its modular design allows for scalability and potential adaptation to other long-cycle, complex-genome tree species. This system provides a centralized, digital platform for managing genealogical and molecular data, facilitating more precise and efficient genetic improvement strategies for Chinese fir and other forest trees.

Key words: Kinship visualization, Chinese fir breeding, Genealogical network diagrams, Information management system, NetworkX and Pyvis