AutoGP: An Intelligent Breeding Platform for Enhancing Maize Genomic Selection.

In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding. Furthermore, recently developed genomic selection (GS) models such as deep learning (DL) are complicated and inconvenient for breeders to navigate and optimize within their breeding programs. Here, we present the development of an intelligent breeding platform named AutoGP (http://autogp.hzau.edu.cn), which integrates genotype extraction, phenotypic extraction, and GS models of genotype-to-phenotype within a user-friendly web interface. AutoGP has three main advantages over previously developed platforms: 1) we designed an efficient sequencing chip to identify high-quality, high-confidence SNPs throughout gene regulatory networks; 2) we developed a complete workflow for plant phenotypic extraction (such as plant height and leaf count) from smartphone-captured video; 3) we provided a broad model pool, allowing users to select from five ML models (SVM, XGBoost, GBDT, MLP, and RF) and four commonly used DL models (DeepGS, DLGWAS, DNNGP, and SoyDNGP). For the convenience of breeders, we employ datasets from the maize (Zea mays) CUBIC population as a case study to demonstrate the usefulness of AutoGP. We show that our genotype chips can effectively extract high-quality SNPs associated with the days to tasseling and plant height. The models present reliable predictive accuracy on different populations, which can provide effective guidance for breeders. Overall, AutoGP offers a practical solution to streamline the breeding process, enabling breeders to achieve more efficient and accurate genomic selection.