大豆褐锈(SBR),由Phakopsorapachyrhizi引起,是一种威胁全球大豆生产的毁灭性真菌病。这项研究在3,082份大豆种质中使用七个模型进行了全基因组关联研究(GWAS),以通过30,314个高质量单核苷酸多态性(SNP)鉴定与SBR抗性相关的标记。然后是五个基因组选择(GS)模型,包括岭回归最佳线性无偏预测因子(rrBLUP),基因组最佳线性无偏预测因子(gBLUP),贝叶斯最小绝对收缩和选择算子(贝叶斯LASSO),随机森林(RF),和支持向量机(SVM),使用全基因组SNP集和基于GWAS的标记集预测SBR抗性的育种值。四个SNP,即GM18_57,223,391(LOD=2.69),Gm16_29,491,946(LOD=3.86),Gm06_45,035,185(LOD=4.74),和GM18_51,994,200(LOD=3.60),位于报告的P.pachyrhiziR基因附近,分别为Rpp1、Rpp2、Rpp3和Rpp4。其他重要的SNP,包括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),和GM19_44,734,953(LOD=6.02),与丰富的抗病基因有关,例如Glyma.02G084100、Glyma.03G175300、Glyma.04g189500、Glyma.09G023800、Glyma.12G160400、Glyma.13G064500、Glyma.14g073300和Glyma.19G190200。这些基因的注释包括但不限于:LRR类基因,细胞色素450,细胞壁结构,RCC1,NAC,ABC运输车,F-box域,等。基于GWAS的标记在基因组预测中显示比全基因组SNP更准确,贝叶斯LASSO模型是SBR抗性预测的理想模型,准确率为44.5%~60.4%。这项研究有助于育种者预测抗病性等复杂性状的选择准确性,并可以通过确定的标记缩短大豆育种周期。
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.