Hyper-seq Technology and Genome-Wide Selection Breeding of Soybeans

Soybeans (Glycine max (L.) Merr.) are a multifunctional crop that contributes significantly to global food security, economic development, and agricultural sustainability. Genomic selection (GS) is widely used in plant breeding, which can effectively reduce breeding costs and shorten the breeding cycle compared to traditional breeding methods. In this study, Hyper-seq technology was used to gather data on 104,728 single nucleotide polymorphism (SNP) sites from 420 natural populations of soybean that were chosen as experimental materials. Furthermore, three years’ worth of phenotypic data on the population’s main stem node count were gathered for this investigation. Comparative analysis was used to assess the validity and accuracy of a number of GS models, including Ridge Regression Best Linear Unbiased Prediction (RRBLUP), Genomic Best Linear Unbiased Prediction (GBLUP), and various Bayesian techniques (Bayesian_A, Bayesian_B, Bayesian_C, Bayesian_RR, Bayesian_LOOS, and Bayesian_RKHS). Each model’s performance was compared using fivefold cross-validation. The research findings indicate that the data obtained by Hyper-seq technology is particularly useful for breeding experiments, including genome-wide selection. The most accurate of them is Bayesian_A, whereas the one with the quickest computational efficiency is GBLUP. Using Hyper-seq technology requires integrating at least 15,000 SNPs to guarantee the model’s stability. It is also important to note that, even if 153 Hyper-seq datasets are 50% less expensive than 153 Whole Genome Sequencing datasets, the difference in prediction accuracy between the two datasets is less than 4%. This discovery further validates the reliability and efficacy of Hyper-seq technology within the domain of genome-wide selection breeding.