GTPhen

GTPhen is a conceptual open-source framework for integrating genotype data with phenotypic information to model and predict trait expression across species. The system emphasizes modularity, graph-based representations, and explainability in genotype-phenotype relationships.

GTPhen treats genetic variation, regulatory elements, and phenotypes as nodes in a graph, with edges encoding associations drawn from databases, literature, and experimental results. It supports cross-species data integration and comparative analyses to identify conserved genotype-phenotype patterns.

The architecture leverages a graph database and interoperable data standards. It ingests VCF and annotation files, phenotype ontologies, and pathway resources, then processes them through feature extractors and a link-forecasting module that ranks potential associations.

The typical analytical workflow includes data quality control, normalization, model inference, and visualization. Outputs include trait-gene likelihoods, pathway-level insights, and transferability scores that help prioritize experiments and hypotheses.

Applications span human genetics, agriculture, and pharmacogenomics. Researchers use GTPhen to prioritize therapeutic targets, guide precision breeding, and interpret polygenic trait architectures by integrating multi-omic and phenotypic data.

Limitations include dependence on data completeness and biases in public resources. While it improves interpretability relative to opaque models, results require validation and careful consideration of confounders.