Dataset Information

Identification of Loci and Candidate Genes Responsible for Pod Dehiscence in Soybean via Genome-Wide Association Analysis Across Multiple Environments.

ABSTRACT: Pod dehiscence (shattering) is the main cause of serious yield loss during the soybean mechanical harvesting process. A better understanding of the genetic architecture and molecular mechanisms of pod dehiscence is of great significance for soybean breeding. In this study, genome-wide association analysis (GWAS) with NJAU 355K SoySNP array was performed to detect single nucleotide polymorphisms (SNPs) associated with pod dehiscence in an association panel containing 211 accessions across five environments. A total of 163 SNPs were identified as significantly associated with pod dehiscence. Among these markers, 136 SNPs identified on chromosome 16 were located in the known QTL qPDH1. One, one, three, eleven, three, one, three, three and one SNPs were distributed on chromosome 1, 4, 6, 8, 9, 11, 17, 18, and 20, respectively. Favorable SNPs and six haplotypes were identified based on ten functional SNPs; among those Hap2 and Hap3 were considered as optimal haplotypes. In addition, based on GWAS results, the candidate gene Glyma09g06290 was identified. Quantitative real-time PCR (qRT-PCR) results and polymorphism analysis suggested that Glyma09g06290 might be involved in pod dehiscence. Furthermore, a derived cleaved amplified polymorphic sequences (dCAPS) marker for Glyma09g06290 was developed. Overall, the loci and genes identified in this study will be helpful in breeding soybean accessions resistant to pod dehiscence.

SUBMITTER: Hu D

PROVIDER: S-EPMC6598122 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Loci and Candidate Genes Responsible for Pod Dehiscence in Soybean via Genome-Wide Association Analysis Across Multiple Environments.

Hu Dezhou D Kan Guizhen G Hu Wei W Li Yali Y Hao Derong D Li Xiao X Yang Hui H Yang Zhongyi Z He Xiaohong X Huang Fang F Yu Deyue D

Frontiers in plant science 20190621

Pod dehiscence (shattering) is the main cause of serious yield loss during the soybean mechanical harvesting process. A better understanding of the genetic architecture and molecular mechanisms of pod dehiscence is of great significance for soybean breeding. In this study, genome-wide association analysis (GWAS) with NJAU 355K SoySNP array was performed to detect single nucleotide polymorphisms (SNPs) associated with pod dehiscence in an association panel containing 211 accessions across five en ...[more]

PMID: 31293609

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Project description:BackgroundPhosphorus (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.ResultsGenome-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.ConclusionsThis 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.

Project description:BackgroundAs a photoperiod-sensitive and self-pollinated species, the growth periods traits play important roles in the adaptability and yield of soybean. To examine the genetic architecture of soybean growth periods, we performed a genome-wide association study (GWAS) using a panel of 278 soybean accessions and 34,710 single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) higher than 0.04 detected by the specific-locus amplified fragment sequencing (SLAF-seq) with a 6.14-fold average sequencing depth. GWAS was conducted by a compressed mixed linear model (CMLM) involving in both relative kinship and population structure.ResultsGWAS revealed that 37 significant SNP peaks associated with soybean flowering time or other growth periods related traits including full bloom, beginning pod, full pod, beginning seed, and full seed in two or more environments at -log10(P) > 3.75 or -log10(P) > 4.44 were distributed on 14 chromosomes, including chromosome 1, 2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19. Fourteen SNPs were novel loci and 23 SNPs were located within known QTLs or 75 kb near the known SNPs. Five candidate genes (Glyma.05G101800, Glyma.11G140100, Glyma.11G142900, Glyma.19G099700, Glyma.19G100900) in a 90 kb genomic region of each side of four significant SNPs (Gm5_27111367, Gm11_10629613, Gm11_10950924, Gm19_34768458) based on the average LD decay were homologs of Arabidopsis flowering time genes of AT5G48385.1, AT3G46510.1, AT5G59780.3, AT1G28050.1, and AT3G26790.1. These genes encoding FRI (FRIGIDA), PUB13 (plant U-box 13), MYB59, CONSTANS, and FUS3 proteins respectively might play important roles in controlling soybean growth periods.ConclusionsThis study identified putative SNP markers associated with soybean growth period traits, which could be used for the marker-assisted selection of soybean growth period traits. Furthermore, the possible candidate genes involved in the control of soybean flowering time were predicted.

Project description:BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments.ResultsHere, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield.ConclusionsThe present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

Project description:Fiber length, fiber strength, and fiber micronaire are the main fiber quality parameters in cotton. Thus, mining the elite and stable loci/alleles related to fiber quality traits and elucidating the relationship between the two may accelerate genetic improvement of fiber quality in cotton. Here, genome-wide association analysis (GWAS) was performed for fiber quality parameters based on phenotypic data, and 56,010 high-quality single nucleotide polymorphisms (SNPs) using 242 upland cotton accessions under 12 field environments were obtained. Phenotypic analysis exhibited that fiber length (FL) had a positive correlation with fiber strength (FS) and had a negative correlation with fiber micronaire (Mic). Genetic analysis also indicated that FL, FS, and Mic had high heritability of more than 80%. A total of 67 stable quantitative trait loci (QTLs) were identified through GWAS analysis, including 31 for FL, 21 for FS, and 22 for Mic. Of them, three pairs homologous QTLs were detected between A and D subgenomes, and seven co-located QTLs with two fiber quality parameters were found. Compared with the reported QTLs, 34 co-located with previous studies, and 33 were newly revealed. Integrated with transcriptome analysis, we selected 256, 244, and 149 candidate genes for FL, FS, and Mic, respectively. Gene Ontology (GO) analysis showed that most of the genes located in QTLs interval of the three fiber quality traits were involved in sugar biosynthesis, sugar metabolism, microtubule, and cytoskeleton organization, which played crucial roles in fiber development. Through correlation analysis between haplotypes and phenotypes, three genes (GH_A05G1494, GH_D11G3097, and GH_A05G1082) predominately expressed in fiber development stages were indicated to be potentially responsible for FL, FS, and Mic, respectively. The GH_A05G1494 encoded a protein containing SGS-domain, which is related to tubulin-binding and ubiquitin-protein ligase binding. The GH_D11G3097 encoded 20S proteasome beta subunit G1, and was involved in the ubiquitin-dependent protein catabolic process. The GH_A05G1082 encoded RAN binding protein 1 with a molecular function of GTPase activator activity. These results provide new insights and candidate loci/genes for the improvement of fiber quality in cotton.

Project description:Fusarium virguliforme is a soil borne root pathogen that causes sudden death syndrome (SDS) in soybean [Glycine max (L.) Merrill]. Once the fungus invades the root xylem tissues, the pathogen secretes toxins that cause chlorosis and necrosis in foliar tissues leading to defoliation, flower and pod drop and eventually death of plants. Resistance to F. virguliforme in soybean is partial and governed by over 80 quantitative trait loci (QTL). We have conducted genome-wide association study (GWAS) for a group of 254 plant introductions lines using a panel of approximately 30,000 SNPs and identified 19 single nucleotide polymorphic loci (SNPL) that are associated with 14 genomic regions encoding foliar SDS and eight SNPL associated with seven genomic regions for root rot resistance. Of the identified 27 SNPL, six SNPL for foliar SDS resistance and two SNPL for root rot resistance co-mapped to previously identified QTL for SDS resistance. This study identified 13 SNPL associated with eight novel genomic regions containing foliar SDS resistance genes and six SNPL with five novel regions for root-rot resistance. This study identified five genes carrying nonsynonymous mutations: (i) three of which mapped to previously identified QTL for foliar SDS resistance and (ii) two mapped to two novel regions containing root rot resistance genes. Of the three genes mapped to QTL for foliar SDS resistance genes, two encode LRR-receptors and third one encodes a novel protein with unknown function. Of the two genes governing root rot resistance, Glyma.01g222900.1 encodes a soybean-specific LEA protein and Glyma.10g058700.1 encodes a heparan-alpha-glucosaminide N-acetyltransferase. In the LEA protein, a conserved serine residue was substituted with asparagine; and in the heparan-alpha-glucosaminide N-acetyltransferase, a conserved histidine residue was substituted with an arginine residue. Such changes are expected to alter functions of these two proteins regulated through phosphorylation. The five genes with nonsynonymous mutations could be considered candidate SDS resistance genes and should be suitable molecular markers for breeding SDS resistance in soybean. The study also reports desirable plant introduction lines and novel genomic regions for enhancing SDS resistance in soybean.

Dataset Information

Identification of Loci and Candidate Genes Responsible for Pod Dehiscence in Soybean via Genome-Wide Association Analysis Across Multiple Environments.

Publications

Identification of Loci and Candidate Genes Responsible for Pod Dehiscence in Soybean via Genome-Wide Association Analysis Across Multiple Environments.

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