ABSTRACT
Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.
Subject(s)
Glycine max , Polymorphism, Single Nucleotide , Genome, Plant/genetics , Genome-Wide Association Study , Genomics , Genotype , Plant Breeding , Polymorphism, Single Nucleotide/genetics , Glycine max/geneticsABSTRACT
100-Seed weight (100-SW) of soybean is an important but complicated quantitative trait to yield. This study was focus on the quantitative trait loci (QTLs) of soybean 100-SW from 2006 to 2010, using recombination inbred lines population that was derived from a cross between Charleston and Dongnong 594. A total of 23 QTLs for 100-SW were detected in the linkage group C2, D1a, F, G and O. Nine QTLs were identified by composite interval mapping including one QTL with the minimum confidence interval (CI) of 1.3 cM, while 14 QTLs by multiple interval mapping. Furthermore, 94 reported QTLs of 100-SW were integrated with our QTL mapping results using BioMercator. As a result, 15 consensus QTLs and their corresponding markers were identified. The minimum CI was reduced to 1.52 cM by the combination of meta-analysis. These findings may merit fine-mapping of these QTL in soybean.
Subject(s)
Chromosome Mapping , Glycine max/genetics , Seeds/genetics , Analysis of Variance , Genes, Plant , Genetic Markers , Lod Score , Phenotype , Quantitative Trait Loci , Seeds/anatomy & histology , Glycine max/anatomy & histologyABSTRACT
Soybeans are an important cash crop and are widely used as a source of vegetable protein and edible oil. MicroRNAs (miRNA) are endogenous small RNA that play an important regulatory role in the evolutionarily conserved system of gene expression. In this study, we selected four lines with extreme phenotypes, as well as high or low protein and oil content, from the chromosome segment substitution line (CSSL) constructed from suinong (SN14) and ZYD00006, and planted and sampled at three stages of grain development for small RNA sequencing and expression analysis. The sequencing results revealed the expression pattern of miRNA in the materials, and predicted miRNA-targeted regulatory genes, including 1967 pairs of corresponding relationships between known-miRNA and their target genes, as well as 597 pairs of corresponding relationships between novel-miRNA and their target genes. After screening and annotating genes that were targeted for regulation, five specific genes were identified to be differentially expressed during seed development and subsequently analyzed for their regulatory relationship with miRNAs. The expression pattern of the targeted gene was verified by Real-time Quantitative PCR (RT-qPCR). Our research provides more information about the miRNA regulatory network in soybeans and further identifies useful genes that regulate storage during soy grain development, providing a theoretical basis for the regulation of soybean quality traits.