GAP: A Graph Neural Network Phenotype Prediction Model for Genotype-Environment Interactions

GAP (Genotype-Environment Graph Attention Prediction) is a genotype-environment interaction (G×E) phenotype prediction model based on graph neural networks (GNNs). It integrates genotype maps and environmental features to provide an efficient computational tool for predicting complex traits such as crop yield. This model addresses the limitations of traditional statistical methods in handling G×E interactions. By modeling genomic linkage disequilibrium (LD) relationships using graph structures and combining attention mechanisms to enable end-to-end learning, it features strong interpretability and good generalization ability, making it suitable for scenarios like crop breeding and environmental adaptability research.

In the fields of agricultural genomics and crop breeding, predicting complex traits (such as yield, disease resistance, and quality indicators) is a core challenge. Traditional statistical methods (like GBLUP and Bayesian regression) struggle to handle genotype-environment interactions (G×E). G×E interaction refers to the phenomenon where the same genotype exhibits different performances in different environments (e.g., yield differences of maize hybrids in arid vs. humid regions). Accurate modeling of this interaction is crucial for breeding varieties with wide adaptability. In recent years, GNNs have shown potential in genomics—by treating SNPs as nodes and LD relationships as edges to construct genotype graph structures. The GAP model is an innovative practice of this idea.

GAP is a deep learning framework that integrates genotype graph structures and environmental features. Its core design includes:

1. Genotype Graph Representation: SNPs as nodes, LD relationships as edges. Node features include SNP position, chromosome information, and genotype values; edge features include LD values.
2. Environmental Feature Integration: Environmental variables (temperature, precipitation, etc.) are used as global features to learn nonlinear interactions between genotypes and environments.
3. Graph Attention Mechanism: Uses GAT (Graph Attention Network) to capture node relationships, automatically learn differences in SNP importance, and support dynamic adjustments. Technical features: End-to-end learning, interpretability (attention weights reveal key SNPs), strong generalization ability, and efficient computation (accelerated by compiled optimization modules).

Data Format Requirements: 6 types of input files are required, including genotype graph data (Maize_A.txt for edge definitions, Maize_edge_attributes.txt for edge features, Maize_node_attributes.txt for node features, sample_id.txt for sample identifiers) and environment/phenotype data (env.txt for environmental features, pheno.txt for phenotype values). Data preparation process: SNP annotation → LD calculation → edge construction → node feature engineering → environment aggregation → data alignment. Usage Methods: Environment configuration (create environment via conda, verify installation), model training (run_train.py), hyperparameter tuning (tune_params.py). The directory structure is clear, including data, scripts, etc.

GAP is suitable for various crop genomic prediction scenarios:

• Multi-environment Trial Analysis: Integrate multi-year, multi-location data to predict new variety adaptability and evaluate genotype stability.
• Breeding Decision Support: Optimize parent selection, predict hybrid combinations, and sort material screening.
• Environmental Adaptability Research: Identify environment-sensitive genotypes and analyze the genetic basis of G×E.
• Accelerated Genomic Selection: Replace or supplement field trials to shorten breeding cycles and reduce costs.

1. Genome Representation via Graph Structure: Explicitly models LD relationships, captures local correlations, and improves biological interpretability.
2. Interpretability of Attention Mechanism: Attention weights show the contribution of key SNPs, aiding QTL mapping and functional gene identification.
3. Efficient Implementation via Compilation Optimization: Cython-compiled extension modules enhance training speed and support large-scale data.

Current Limitations: Platform restrictions (only Linux x86_64 and Python 3.11), specific input formats, species specificity (examples are for maize). Future Directions: Expand to more crops, integrate multi-omics data, cross-species transfer learning, integrate more environmental factors, and develop visualization tools.

GAP combines the representation capabilities of GNNs with G×E interaction modeling, providing a fully functional tool for crop breeding and quantitative genetics researchers. Facing the challenges of climate change and food security, GAP and its future developments are expected to help breed crop varieties with strong adaptability and stable yields.