SoyDNGP: A Web-Accessible Deep Learning Framework for Genomic Prediction in Soybean Breeding

Abstract

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a Convolutional Neural Networks (CNN)-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its lower parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP consistently outperformed its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including Cotton, Maize, Rice, and Tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGPs potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we have designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two primary features: Trait Lookup, offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and Trait Prediction, allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction and genomic analysis, SoyDNGP opens up new possibilities in the quest for efficient and optimized soybean breeding.