EFGPP: Exploratory framework for genotype-phenotype prediction

2026-05-02

AI Summary

This study addresses the challenge of improving prediction accuracy for complex phenotypes such as migraine by systematically integrating multi-source heterogeneous data, including genotypes, polygenic risk scores (PRS), clinical covariates, and metabolomic features. The authors developed a reproducible analytical framework that leverages tools such as PLINK, PRSice-2, AnnoPred, and LDAK-GWAS to generate and harmonize diverse data modalities, and further evaluated the predictive utility of cross-trait–derived features (e.g., from depression) for the target phenotype. Applied to 733 individuals from the UK Biobank, the integrated model significantly enhanced migraine prediction performance, increasing the area under the receiver operating characteristic curve (AUC) from 0.644—achieved by the best single data type—to 0.688, thereby underscoring the critical value of multimodal data integration in precision phenotypic prediction.

Abstract

Predicting complex human traits from genetic data is challenging because different genetic, clinical, and molecular data sources often contain different parts of the signal. Here, we present EFGPP, a reproducible framework for generating, ranking, and combining multiple types of data for genotype-to-phenotype prediction. We applied EFGPP to migraine prediction using UK Biobank data from 733 individuals. The framework combined genotype-derived features, principal components, clinical and metabolomic covariates, and polygenic risk scores generated from migraine and depression GWAS using PLINK, PRSice-2, AnnoPred, and LDAK-GWAS. The best single data type achieved a test AUC of 0.644, while combining multiple data types improved performance to 0.688 using migraine-focused inputs and 0.663 using cross-trait depression-derived inputs. Genetic features alone did not outperform the covariates-only baseline, but genotype-derived features performed better than PRS alone, and depression-derived PRS showed useful predictive signal. Overall, EFGPP provides a practical proof-of-concept framework for prioritising and integrating heterogeneous genetic data sources for complex phenotype prediction.

Problem

• genotype-phenotype prediction
• complex traits
• heterogeneous data integration
• polygenic risk scores
• migraine prediction

Innovation

• genotype-phenotype prediction
• data integration
• polygenic risk score
• cross-trait prediction
• EFGPP framework