Wenkai Du1, Lihua Ning2, Yongshun Liu1, Shixi Zhang1, Yuming Yang1, Qing Wang1, Shengqian Chao1, Hui Yang1,3, Fang Huang1, Hao Cheng4, Deyue Yu5. 1. National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China. 2. Institute of Crop Germplasm and Biotechnology, Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China. 3. School of Life Sciences, Guangzhou University, Guangzhou, 510006, China. 4. National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China. jenny21star@njau.edu.cn. 5. National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, 210095, China. dyyu@njau.edu.cn.
Abstract
BACKGROUND: Phosphorus (P) is an essential element in maintaining high biomass and yield in crops. Soybean [Glycine max (L.) Merr.] requires a large amount of P during growth and development. Improvement of P efficiency and identification of P efficiency genes are important strategies for increasing soybean yield. RESULTS: Genome-wide association analysis (GWAS) with NJAU 355 K SoySNP array was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with three shoot P efficiency-related traits of a natural population of 211 cultivated soybeans and relative values of these traits under normal P (+P) condition and P deficiency (-P) condition. A total of 155 SNPs were identified significantly associated with P efficiency-related traits. SNPs that were significantly associated with shoot dry weight formed a SNP cluster on chromosome 11, while SNPs that were significantly associated with shoot P concentration formed a SNP cluster on chromosome 10. Thirteen haplotypes were identified based on 12 SNPs, and Hap9 was considered as the optimal haplotype. Four SNPs (AX-93636685, AX-93636692, AX-93932863, and AX-93932874) located on chromosome 10 were identified to be significantly associated with shoot P concentration under +P condition in two hydroponic experiments. Among these four SNPs, two of them (AX-93636685 and AX-93932874) were also significantly associated with the relative values of shoot P concentration under two P conditions. One SNP AX-93932874 was detected within 5'-untranslated region of Glyma.10 g018800, which contained SPX and RING domains and was named as GmSPX-RING1. Furthermore, the function research of GmSPX-RING1 was carried out in soybean hairy root transformation. Compared with their respective controls, P concentration in GmSPX-RING1 overexpressing transgenic hairy roots was significantly reduced by 32.75% under +P condition; In contrast, P concentration in RNA interference of GmSPX-RING1 transgenic hairy roots was increased by 38.90 and 14.51% under +P and -P conditions, respectively. CONCLUSIONS: This study shows that the candidate gene GmSPX-RING1 affects soybean phosphorus efficiency by negatively regulating soybean phosphorus concentration in soybean hairy roots. The SNPs and candidate genes identified should be potential for improvement of P efficiency in future soybean breeding programs.
<<span class="Chemical">span class="abstract_title">BACKGROUND:span> <spaspan>n class="Chemical">Phosphorus (P) is an essential element in maintaining high biomass and yield in crops. Soybean [Glycine max (L.) Merr.] requires a large amount of P during growth and development. Improvement of P efficiency and identification of P efficiency genes are important strategies for increasing soybean yield. RESULTS: Genome-wide association analysis (GWAS) with NJAU 355 K SoySNP array was performed to identify single nucleotide polymorphisms (SNPs) significantly associated with three shoot P efficiency-related traits of a natural population of 211 cultivated soybeans and relative values of these traits under normal P (+P) condition and P deficiency (-P) condition. A total of 155 SNPs were identified significantly associated with P efficiency-related traits. SNPs that were significantly associated with shoot dry weight formed a SNP cluster on chromosome 11, while SNPs that were significantly associated with shoot P concentration formed a SNP cluster on chromosome 10. Thirteen haplotypes were identified based on 12 SNPs, and Hap9 was considered as the optimal haplotype. Four SNPs (AX-93636685, AX-93636692, AX-93932863, and AX-93932874) located on chromosome 10 were identified to be significantly associated with shoot P concentration under +P condition in two hydroponic experiments. Among these four SNPs, two of them (AX-93636685 and AX-93932874) were also significantly associated with the relative values of shoot P concentration under two P conditions. One SNP AX-93932874 was detected within 5'-untranslated region of Glyma.10 g018800, which contained SPX and RING domains and was named as GmSPX-RING1. Furthermore, the function research of GmSPX-RING1 was carried out in soybean hairy root transformation. Compared with their respective controls, P concentration in GmSPX-RING1 overexpressing transgenic hairy roots was significantly reduced by 32.75% under +P condition; In contrast, P concentration in RNA interference of GmSPX-RING1 transgenic hairy roots was increased by 38.90 and 14.51% under +P and -P conditions, respectively. CONCLUSIONS: This study shows that the candidate gene GmSPX-RING1 affects soybeanphosphorus efficiency by negatively regulating soybeanphosphorus concentration in soybean hairy roots. The SNPs and candidate genes identified should be potential for improvement of P efficiency in future soybean breeding programs.
Entities:
Keywords:
GWAS; GmSPX-RING1; P concentration; P efficiency; Soybean
Authors: Peter J Bradbury; Zhiwu Zhang; Dallas E Kroon; Terry M Casstevens; Yogesh Ramdoss; Edward S Buckler Journal: Bioinformatics Date: 2007-06-22 Impact factor: 6.937