Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

Authors

Jun Qin, Hebei Academy of Agricultural and Forestry Sciences
Fengmin Wang, Hebei Academy of Agricultural and Forestry Sciences
Qingsong Zhao, Hebei Academy of Agricultural and Forestry Sciences
Ainong Shi, University of Arkansas, FayettevilleFollow
Tiantian Zhao, Hebei Academy of Agricultural and Forestry Sciences
Qijian Song, USDA, Agricultural Research Service
Waltram Ravelombola, Texas A & M UniversityFollow
Hongzhou An, Hebei Academy of Agricultural and Forestry Sciences
Long Yan, Hebei Academy of Agricultural and Forestry Sciences
Chunyan Yang, Hebei Academy of Agricultural and Forestry SciencesFollow
Mengchen Zhang, Hebei Academy of Agricultural and Forestry SciencesFollow

Document Type

Article

Publication Date

6-2022

Keywords

Glycine max; genome-wide association study; genomic selection; genotyping by sequencing; protein content; single nucleotide polymorphism

Abstract

Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283–19315351) included 7 candidate genes and the Hap.X^AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

Citation

Qin, J., Wang, F., Zhao, Q., Shi, A., Zhao, T., Song, Q., Ravelombola, W., An, H., Yan, L., Yang, C., & Zhang, M. (2022). Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline. Frontiers in Plant Science, 13, 882732. https://doi.org/10.3389/fpls.2022.882732

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.