A Quantitative Trait Nucleotide-Based Genomic Selection Strategy for Seed Oil and Protein Content in Soybean

Area of Science:

• Plant breeding
• Genetics
• Agricultural science

Background:

• Genomic selection (GS) is vital in plant breeding but hindered by high genotyping costs.
• Developing cost-effective GS strategies is crucial for practical application in large populations.

Purpose of the Study:

• To evaluate a Quantitative Trait Nucleotide (QTN)-assisted genomic selection (GS) strategy for reducing genotyping costs in soybean breeding.
• To assess the efficiency of QTN-assisted GS for improving soybean seed oil content (OC) and protein content (PC).

Main Methods:

• Six multi-parent F4 soybean populations (n=4404) were genotyped using a 20K SNP chip.
• Quantitative Trait Nucleotides (QTNs) associated with OC and PC were identified.
• Genomic prediction accuracies were compared using genome-wide SNPs, all detected QTNs, and trait-specific QTN panels across various training population sizes.

Main Results:

• 83 and 110 significant QTNs were identified for OC and PC, respectively.
• The panel of all detected QTNs generally showed higher prediction accuracy than genome-wide SNPs or trait-specific QTNs.
• Phenotypic verification confirmed that the PC-specific QTN panel increased PC and combined OC + PC values.

Conclusions:

• A small set of QTNs offers a cost-effective approach for genomic selection in soybean breeding.
• QTN-assisted GS can significantly reduce genotyping costs while maintaining or improving prediction accuracy.
• This strategy facilitates practical implementation of GS in large-scale soybean breeding programs.