Heterogeneous Graph Genomic Prediction for Genotype-Environment Effects
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Solution Overview
Problem
Existing genomic prediction methods based on genotype data suffer from low prediction accuracy due to overlooking the complex interaction between genotypes and environments, and lack integration of multi-omics data such as genomics, transcriptomics, and metabolomics.
Innovation Solution
A genomic prediction method utilizing a genotype-environment interaction heterogeneous graph that integrates genotype and environmental data, generating a heterogeneous graph with nodes representing crop varieties and environments, and using graph processing models to aggregate features and predict phenotypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear models are used for genomic prediction, then the model complexity is low, but the prediction accuracy is low due to overlooking genotype-environment interaction
Solution Approach 1:
The patent introduces a heterogeneous graph as an intermediary structure to capture genotype-environment interactions. The graph includes genotype nodes, environment nodes, and interaction edges that mediate between genetic and environmental factors, enabling the model to accurately predict phenotypes by considering their complex interactions rather than using simple linear models.
Solution Approach 2:
The patent transitions from traditional linear models to a heterogeneous graph representation that adds multiple dimensions to the data structure. By representing genotypes, environments, and their interactions in a graph framework with different node and edge types, the model captures complex relationships that linear models cannot accommodate, thereby improving prediction accuracy.
2Measurement precision
If only genomic data is used for prediction, then the data processing is simple, but the prediction accuracy is limited due to lack of multi-omics integration
Solution Approach 1:
The patent merges multiple types of omics data (genomics, transcriptomics, proteomics, metabolomics) into a unified heterogeneous graph framework. By combining these diverse data types and integrating them with environmental data in a single graph structure, the model achieves comprehensive multi-omics integration that significantly improves prediction accuracy while managing data complexity through a cohesive framework.
3Measurement precision
If heterogeneous graph with multi-omics data is used, then the prediction accuracy is improved, but the computational complexity and data processing difficulty increase
Solution Approach 1:
The patent segments the complex data processing task into distinct modules: data preprocessing, graph construction, feature extraction, and prediction. By dividing the heterogeneous graph construction into separate steps (processing each omics layer independently, then integrating them), the model reduces the difficulty of handling complex multi-omics data while maintaining comprehensive integration for accurate predictions.
Data Source
AI summary
Provided are a genomic prediction method and apparatus based on a genotype-environment interaction heterogeneous graph, relating to the technical field of bioinformatics. The method includes: obtaining genotype data of a to-be-predicted crop variety and generating genotype features of the to-be-predicted crop variety based on the genotype data of the to-be-predicted crop variety; obtaining environmental data of a target environment and generating environmental features of the target environment based on the environmental data of the target environment; generating a heterogeneous graph based on the genotype features of the to-be-predicted crop variety, genotype features of at least one other crop variety, the environmental features of the target environment, environmental features of at least one other environment, and phenotype data; and inputting the heterogeneous graph into a trained heterogeneous graph prediction model to obtain predicted phenotype data of the to-be-predicted crop variety in the target environment outputted by the heterogeneous graph prediction model.