AUTHOR=Xiong Haizheng , Chen Yilin , Pan Yong-Bao , Wang Jinshe , Lu Weiguo , Shi Ainong 

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

JOURNAL=Frontiers in Plant Science

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1179357

DOI=10.3389/fpls.2023.1179357

ISSN=1664-462X

ABSTRACT=<p>Soybean brown rust (SBR), caused by <italic>Phakopsora pachyrhizi</italic>, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including 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 used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. 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 <italic>Glyma.02G084100</italic>, <italic>Glyma.03G175300</italic>, <italic>Glyma.04g189500</italic>, <italic>Glyma.09G023800</italic>, <italic>Glyma.12G160400</italic>, <italic>Glyma.13G064500</italic>, <italic>Glyma.14g073300</italic>, and <italic>Glyma.19G190200</italic>. The annotations of these genes included but not limited to: <italic>LRR</italic> class gene, <italic>cytochrome 450</italic>, cell wall structure, <italic>RCC1, NAC, ABC</italic> transporter, <italic>F-box</italic> domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers</p>