A Linear-Transformer Hybrid for SNP-Based Genotype-to-Phenotype Prediction in Grapevine

TL;DR

This paper introduces LiT-G2P, a hybrid linear-transformer model that improves SNP-based genotype-to-phenotype predictions in grapevines by combining additive genetic effects with nonlinear interactions, enhancing robustness across years.

Contribution

The study presents a novel linear-transformer hybrid framework for G2P prediction that effectively integrates additive and interaction effects, improving accuracy and interpretability.

Findings

LiT-G2P outperforms baseline models in predicting grapevine traits.

It achieves RMSEs of 0.469 and 0.454 for leaf hair density across years.

Attention weights enable extraction of candidate SNPs for validation.

Abstract

Robust genotype-to-phenotype (G2P) prediction is essential for accelerating breeding decisions and genetic gain. However, it remains challenging to measure complex traits under variable field conditions and across years. In this study, we propose a linear-Transformer approach, LiT-G2P (Linear-Transformer Genotype-to-Phenotype), an automated predictive framework that integrates additive genetic variance effects with Transformer-based nonlinear interactions using genome-wide single-nucleotide polymorphisms (SNPs) data. We evaluated LiT-G2P on a panel of diverse grape accessions, genotyped with SNP markers and measured for phenotypes across two consecutive years. Target phenotypic traits include leaf hair density and trichome density of grapevines. Across both single-year and cross-year testing scenarios, LiT-G2P consistently improves prediction performance compared with baseline models. For hair density, LiT-G2P achieves the lowest error in both single-year and cross-year evaluations, with RMSEs of 0.469 and 0.454, respectively, while maintaining strong tolerance accuracies of 79.2% and 74.6%, respectively. For trichome density, LiT-G2P also presents the best overall G2P performance. In addition, we extract model-prioritized SNPs from attention weights and apply genotype-stratified analysis to provide interpretable candidate marker for downstream validation. These results demonstrate that integrating stable additive effects with learned interaction patterns can enhance cross-year robustness and support practical SNP-based predictive modeling for genomic selection.