Genomic value prediction for quantitative traits under the epistatic model

Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL). The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects) and marker pairs (epistatic effects) to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait) for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive) effects were used for prediction. When the interaction (epistatic) effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding

GmGBP1, a homolog of human ski interacting protein in soybean, regulates flowering and stress tolerance in Arabidopsis

BACKGROUND: SKIP is a transcription cofactor in many eukaryotes. It can regulate plant stress tolerance in rice and Arabidopsis. But the homolog of SKIP protein in soybean has been not reported up to now. RESULTS: In this study, the expression patterns of soybean GAMYB binding protein gene (GmGBP1) encoding a homolog of SKIP protein were analyzed in soybean under abiotic stresses and different day lengths. The expression of GmGBP1 was induced by polyethyleneglycol 6000, NaCl, gibberellin, abscisic acid and heat stress. GmGBP1 had transcriptional activity in C-terminal. GmGBP1 could interact with R2R3 domain of GmGAMYB1 in SKIP domain to take part in gibberellin flowering pathway. In long-day (16 h-light) condition, transgenic Arabidopsis with the ectopic overexpression of GmGBP1 exhibited earlier flowering and less number of rosette leaves; Suppression of AtSKIP in Arabidopsis resulted in growth arrest, flowering delay and down-regulation of many flowering-related genes (CONSTANS, FLOWERING LOCUS T, LEAFY); Arabidopsis myb33 mutant plants with ectopic overexpression of GmGBP1 showed the same flowering phenotype with wild type. In short-day (8 h-light) condition, transgenic Arabidopsis plants with GmGBP1 flowered later and showed a higher level of FLOWERING LOCUS C compared with wild type. When treated with abiotic stresses, transgenic Arabidopsis with the ectopic overexpression of GmGBP1 enhanced the tolerances to heat and drought stresses but reduced the tolerance to high salinity, and affected the expressions of several stress-related genes. CONCLUSIONS: In Arabidopsis, GmGBP1 might positively regulate the flowering time by affecting CONSTANS, FLOWERING LOCUS T, LEAFY and GAMYB directly or indirectly in photoperiodic and gibberellin pathways in LDs, but GmGBP1 might represse flowering by affecting FLOWERING LOCUS C and SHORT VEGETATIVE PHASE in autonomous pathway in SDs. GmGBP1 might regulate the activity of ROS-eliminating to improve the resistance to heat and drought but reduce the high-salinity tolerance

Identification of the Genomic Region Underlying Seed Weight per Plant in Soybean (Glycine max L. Merr.) via High-Throughput Single-Nucleotide Polymorphisms and a Genome-Wide Association Study

Seed weight per plant (SWPP) of soybean (Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future

Weighted gene co-expression network analysis identifies genes related to HG Type 0 resistance and verification of hub gene GmHg1

IntroductionThe soybean cyst nematode (SCN) is a major disease in soybean production thatseriously affects soybean yield. At present, there are no studies on weighted geneco-expression network analysis (WGCNA) related to SCN resistance.MethodsHere, transcriptome data from 36 soybean roots under SCN HG Type 0 (race 3) stresswere used in WGCNA to identify significant modules.Results and DiscussionA total of 10,000 differentially expressed genes and 21 modules were identified, of which the module most related to SCN was turquoise. In addition, the hub gene GmHg1 with high connectivity was selected, and its function was verified. GmHg1 encodes serine/threonine protein kinase (PK), and the expression of GmHg1 in SCN-resistant cultivars (‘Dongnong L-204’) and SCN-susceptible cultivars (‘Heinong 37’) increased significantly after HG Type 0 stress. Soybean plants transformed with GmHg1-OX had significantly increased SCN resistance. In contrast, the GmHg1-RNAi transgenic soybean plants significantly reduced SCN resistance. In transgenic materials, the expression patterns of 11 genes with the same expression trend as the GmHg1 gene in the ‘turquoise module’ were analyzed. Analysis showed that 11genes were co-expressed with GmHg1, which may be involved in the process of soybean resistance to SCN. Our work provides a new direction for studying the Molecular mechanism of soybean resistance to SCN

Identification of QTL underlying vitamin E contents in soybean seed among multiple environments

Vitamin E (VE) in soybean seed has value for foods, medicines, cosmetics, and animal husbandry. Selection for higher VE contents in seeds along with agronomic traits was an important goal for many soybean breeders. In order to map the loci controlling the VE content, F5-derived F6 recombinant inbred lines (RILs) were advanced through single-seed-descent (SSD) to generate a population including 144 RILs. The population was derived from a cross between ‘OAC Bayfield’, a soybean cultivar with high VE content, and ‘Hefeng 25’, a soybean cultivar with low VE content. A total of 107 polymorphic simple sequence repeat markers were used to construct a genetic linkage map. Seed VE contents were analyzed by high performance liquid chromatography for multiple years and locations (Harbin in 2007 and 2008, Hulan in 2008 and Suihua in 2008). Four QTL associated with α-Toc (on four linkage groups, LGs), eight QTL associated with γ-Toc (on eight LGs), four QTL associated with δ-Toc (on four LGs) and five QTL associated with total VE (on four LGs) were identified. A major QTL was detected by marker Satt376 on linkage group C2 and associated with α-Toc (0.0012 > P > 0.0001, 5.0% < R2 < 17.0%, 25.1 < α-Toc < 30.1 μg g−1), total VE (P < 0.0001, 7.0% < R2 < 10.0%, 118.2 < total VE < 478.3 μg g−1). A second QTL detected by marker Satt286 on LG C2 was associated with γ-Toc (0.0003 > P > 0.0001, 6.0% < R2 < 13.0%, 141.5 < γ-Toc < 342.4 μg g−1) and total VE (P < 0.0001, 2.0% < R2 < 9.0%, 353.9 < total VE < 404.0 μg g−1). Another major QTL was detected by marker Satt266 on LG D1b that was associated with α-Toc (0.0002 > P > 0.0001, 4.0% < R2 < 6.0%, 27.7 < α-Toc < 43.7 μg g−1) and γ-Toc (0.0032 > P > 0.0001, 3.0% < R2 < 10.0%, 69.7 < γ-Toc < 345.7 μg g−1). Since beneficial alleles were all from ‘OAC Bayfield’, it was concluded that these three QTL would have great potential value for marker assisted selection for high VE content

GmLecRlk, a Lectin Receptor-like Protein Kinase, Contributes to Salt Stress Tolerance by Regulating Salt-Responsive Genes in Soybean

Soybean [Glycine max (L.) Merr.] is an important oil crop that provides valuable resources for human consumption, animal feed, and biofuel. Through the transcriptome analysis in our previous study, GmLecRlk (Glyma.07G005700) was identified as a salt-responsive candidate gene in soybean. In this study, qRT-PCR analysis showed that the GmLecRlk gene expression level was significantly induced by salt stress and highly expressed in soybean roots. The pCAMBIA3300-GmLecRlk construct was generated and introduced into the soybean genome by Agrobacterium rhizogenes. Compared with the wild type (WT), GmLecRlk overexpressing (GmLecRlk-ox) soybean lines had significantly enhanced fresh weight, proline (Pro) content, and catalase (CAT) activity, and reduced malondialdehyde (MDA) and H2O2 content under salt stress. These results show that GmLecRlk gene enhanced ROS scavenging ability in response to salt stress in soybean. Meanwhile, we demonstrated that GmLecRlk gene also conferred soybean salt tolerance when it was overexpressed alone in soybean hairy root. Furthermore, the combination of RNA-seq and qRT-PCR analysis was used to determine that GmLecRlk improves the salt tolerance of soybean by upregulating GmERF3, GmbHLH30, and GmDREB2 and downregulating GmGH3.6, GmPUB8, and GmLAMP1. Our research reveals a new mechanism of salt resistance in soybean, which exposes a novel avenue for the cultivation of salt-resistant varieties

GWAS and WGCNA Analysis Uncover Candidate Genes Associated with Oil Content in Soybean

Soybean vegetable oil is an important source of the human diet. However, the analysis of the genetic mechanism leading to changes in soybean oil content is still incomplete. In this study, a total of 227 soybean materials were applied and analyzed by a genome-wide association study (GWAS). There are 44 quantitative trait nucleotides (QTNs) that were identified as associated with oil content. A total of six, four, and 34 significant QTN loci were identified in Xiangyang, Hulan, and Acheng, respectively. Of those, 26 QTNs overlapped with or were near the known oil content quantitative trait locus (QTL), and 18 new QTNs related to oil content were identified. A total of 594 genes were located near the peak single nucleotide polymorphism (SNP) from three tested environments. These candidate genes exhibited significant enrichment in tropane, piperidine, and pyridine alkaloid biosynthesiss (ko00960), ABC transporters (ko02010), photosynthesis-antenna proteins (ko00196), and betalain biosynthesis (ko00965). Combined with the GWAS and weighted gene co-expression network analysis (WGCNA), four candidate genes (Glyma.18G300100, Glyma.11G221100, Glyma.13G343300, and Glyma.02G166100) that may regulate oil content were identified. In addition, Glyma.18G300100 was divided into two main haplotypes in the studied accessions. The oil content of haplotype 1 is significantly lower than that of haplotype 2. Our research findings provide a theoretical basis for improving the regulatory mechanism of soybean oil content