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

Haizheng Xiong; Yilin Chen; Yong-Bao Pan; Jinshe Wang; Weiguo Lu; Ainong Shi

doi:10.3389/fpls.2023.1179357

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

Front Plant Sci. 2023 May 29:14:1179357. doi: 10.3389/fpls.2023.1179357. eCollection 2023.

Authors

Haizheng Xiong¹, Yilin Chen¹, Yong-Bao Pan², Jinshe Wang³, Weiguo Lu³, Ainong Shi¹

Affiliations

¹ Department of Horticulture, University of Arkansas, Fayetteville, AR, United States.
² Sugarcane Research Unit, Untied State Department of Agriculture - Agriculture Research Service (USDA-ARS), Houma, LA, United States.
³ Henan Academy of Crops Molecular Breeding, National Centre for Plant Breeding, Zhengzhou, China.

Abstract

Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, 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 Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. 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. 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.

Keywords: GWAS; Phakopsora pachyrhizi; disease resistance; genomic prediction; soybean.

Grants and funding

This study was supported by USDA NIFA HATCH project ARK0VG2018 and a USDA-ARS Non-Assistance Cooperative Agreement on Genetic Analysis and Trait-Specific Molecular Marker Development (Accession No. 440501).