A genome-wide association study and genomic prediction for Phakopsora pachyrhizi resistance in soybean

Authors: XIONG, HAIZHENG - University Of Arkansas; CHEN, YILING - University Of Arkansas; Pan, Yong-Bao; SHI, AINONG - University Of Arkansas; WANG, JINSHE - Henan Academy Of Agricultural Science

Submitted to: Frontiers in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/25/2023
Publication Date: 5/29/2023
Citation: Xiong, H., Chen, Y., Pan, Y.-B., Shi, A., Wang, J. 2023. A genome-wide association study and genomic prediction for Phakopsora pachyrhizi resistance in soybean. Frontiers in Plant Science. 14(1179357):1-12. https://doi.org/10.3389/fpls.2023.1179357.
DOI: https://doi.org/10.3389/fpls.2023.1179357

Interpretive Summary: Soybean brown rust (SBR) is a serious fungal disease that threatens global soybean production. Because SBR resistance is controlled by multiple genes and the fungus often changes its ability to infect soybean, it is necessary for breeders to identify new resistant genes. This study aimed to discover DNA markers related to SBR resistance and to develop a new molecular breeding method called genomic selection (GS). A total of 3,082 soybean lines were involved in the study. The SBR disease data were downloaded from the USDA GRIN website (https://npgsweb.ars-grin.gov/gringlobal/method?id=492634). Molecular marker data of 30,314 SNP (single nucleotide polymorphism) covering the entire soybean genome were downloaded from the Soybase (https://www.soybase.org/snps/download.php). The association between the disease and molecular marker data were analyzed using seven genome-wide association study (GWAS) models. The results showed that four highly significant SNP markers (Gm18_57,223,391, Gm16_29,491,946, Gm06_45,035,185, and Gm18_51,994,200) were located near the SBR resistance genes Rpp1, Rpp2, Rpp3, and Rpp4, respectively. In addition, 10 more SNP markers, including Gm02_7,235,181, Gm02_7234594, Gm03_38,913,029, Gm04_46,003,059, Gm09_1,951,644, Gm10_39,142,024, Gm12_28,136,735, Gm13_16,350,701, Gm14_6,185,611, and Gm19_44,734,953, were also associated with SBR resistance genes that play roles in plant growth and development. Furthermore, five GS models, i.e., Ridge Regression Best Linear Unbiased Predictor, Genomic Best Linear Unbiased Predictor, Bayesian Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machines (SVM), were used to predict the breeding value of 28, 100, 500, 1000, 2000, 5000, and 30,314 SNP markers in the prediction of the SBR resistance. The results showed that the Bayesian Least Absolute Shrinkage and Selection Operator model was the ideal model with 44.5% ~ 60.4% accuracies. These results will help soybean breeders improve the selection efficiency of SBR disease resistance at the early stages of soybean breeding process and shorten the breeding cycle.

Technical Abstract: Soybean brown rust (SBR), caused by the biotrophic fungus Phakopsora pachyrhizi, is one of the most devastating fungal diseases that threatens global soybean (Glycine max) production. Because the pathogen changes its pathogenicity frequently, it is necessary to identify new genes for SBR resistance. The objectives of this study were to conduct genome-wide association study (GWAS) to find single nucleotide polymorphism (SNP) markers associated with SBR resistance and to perform genomic prediction (GP) for SBR resistance in soybean. Seven models of GWAS were performed on a panel of 3,082 soybean accessions and 30,314 SBR resistance-associated SNPs were identified. Four SNPs, namely Gm18_57,223,391 (LOD=2.69), Gm16_29,491,946 (LOD=3.86), Gm06_45,035,185 (LOD=4.74), and Gm18_51,994,200 (LOD=3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD=7.91), Gm02_7234594 (LOD=7.61), Gm03_38,913,029 (LOD=6.85), Gm04_46,003,059 (LOD=6.03), Gm09_1,951,644 (LOD=10.07), Gm10_39,142,024 (LOD=7.12), Gm12_28,136,735 (LOD=7.03), Gm13_16,350,701(LOD=5.63), Gm14_6,185,611 (LOD=5.51), and Gm19_44,734,953 (LOD=6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200, etc. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. In addition, five GS models, i.e., Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were applied to predict the breeding values of SBR resistance based on a whole genome set of 30,314 SNP markers and six other GWAS based marker sets of 28, 100, 500, 1000, 2000, and 5000 SNPs, respectively. The results showed that the Bayesian LASSO model was the ideal model for genomic prediction with 44.5% ~ 60.4% accuracies. The results of this study help breeders predict the selection accuracy of complex traits like disease resistance and can be applied at the early stages of soybean breeding process to shorten the breeding cycle.