Pod shattering is an important reproductive process in many wild species. However, pod shattering at the maturing stage can result in severe yield loss. The objectives of this study were to discover quantitative trait loci (QTLs) for pod shattering using two recombinant inbred line (RIL) populations derived from an elite cultivar having pod shattering tolerance, namely “Daewonkong”, and to predict novel candidate QTL/genes involved in pod shattering based on their allele patterns. We found several QTLs with more than 10% phenotypic variance explained (PVE) on seven different chromosomes and found a novel candidate QTL on chromosome 16 (qPS-DS16-1) from the allele patterns in the QTL region. Out of the 41 annotated genes in the QTL region, six were found to contain SNP (single-nucleotide polymorphism)/indel variations in the coding sequence of the parents compared to the soybean reference genome. Among the six potential candidate genes, Glyma.16g076600, one of the genes with known function, showed a highly differential expression levels between the tolerant and susceptible parents in the growth stages R3 to R6. Further, Glyma.16g076600 is a homolog of AT4G19230 in Arabidopsis, whose function is related to abscisic acid catabolism. The results provide useful information to understand the genetic mechanism of pod shattering and could be used for improving the efficiency of marker-assisted selection for developing varieties of soybeans tolerant to pod shattering.

Flooding stress causes a significant yield reduction in soybean. The early growth of soybean in Korea coincides with the rainy season, potentially exposing to flooding stress. Greenhouse experiments were conducted to map the quantitative trait loci (QTL) for flooding tolerance in soybean and to identify and investigate candidate genes near the QTL hot spots. Flood stress was imposed at V1–V2 stage on a recombinant inbred line population (‘Paldalkong’ × ‘NTS1116’), and leaf chlorophyll content (CC) and shoot dry weight (DW) were measured under control and flooded conditions. The genetic map was constructed using 180K Axiom® SoyaSNP markers. The QTL were analysed under control and flooded conditions as well as for index (ratio of CC or DW under flooded to control, CCI and DWI) and flooding tolerance index (FTI, mean of CCI and DWI). A total of 20 QTL with LOD scores 3.59–19.73 causing 5.8%–33.3% phenotypic variation were identified on nine chromosomes. Chromosomes 10, 12 and 13 harboured relatively more stable QTL. Results of this study could be useful to further understand the genetic basis of soybean's flooding tolerance and applied in breeding programmes.

Flooding stress is a serious problem in soybean production, causing a remarkable yield reduction. The onset of rainy season during the early growth of soybean in Korea and some other parts of the world potentially subjects soybean plants to flooding stress. The objective of this study was to map quantitative trait loci (QTL) for flooding tolerance using a recombinant inbred line (RIL) population derived from a cross between ‘Danbaekkong’ (flood-tolerant) and ‘NTS1116′ (flood-susceptible) cultivars grown in a plastic house for two years. The plants were flood-stressed at the V1-V2 stage by ponding about 10 cm water from the soil surface. Leaf chlorophyll content and shoot dry weight were measured under control and flooded conditions to map the QTL. The genetic map was constructed using 1689 polymorphic markers obtained from the 180K Axiom® SoyaSNP markers used for genotyping the population. Ten QTL with 3.39–5.14 logarithm of odds scores and 8.1–30.7% phenotypic variations (PVE) were identified on seven chromosomes. One QTL on chromosomes 6 and 15 and two QTL on chromosome 7 were detected at least in two different environments causing up to 30.7% PVE, suggesting their potential applications in the breeding of flood-tolerant soybeans. The results could be useful in further exploring the genetic basis of flooding tolerance and developing tolerant cultivars of soybean.

Pod-shattering causes a significant yield loss in many soybean cultivars. Shattering-tolerant cultivars provide the most effective approach to minimizing this loss. We developed molecular markers for pod-shattering and validated them in soybeans with diverse genetic backgrounds. The genes Glyma.16g141200, Glyma.16g141500, and Glyma.16g076600, identified in our previous study by quantitative trait locus (QTL) mapping and whole-genome resequencing, were selected for marker development. The whole-genome resequencing of three parental lines (one shattering-tolerant and two shattering-susceptible) identified single nucleotide polymorphism (SNP) and/or insertion/deletion (InDel) regions within or near the selected genes. Two SNPs and one InDel were converted to Kompetitive Allele-Specific PCR (KASP) and InDel markers, respectively. The accuracy of the markers was examined in the two recombinant inbred line populations used for the QTL mapping, as well as the 120 varieties and elite lines, through allelic discrimination and phenotyping by the oven-drying method. Both types of markers successfully discriminated the pod shattering-tolerant and shattering-susceptible genotypes. The prediction accuracy, which was as high as 90.9% for the RILs and was 100% for the varieties and elite lines, also supported the accuracy and usefulness of these markers. Thus, the markers can be used effectively for genetic and genomic studies and the marker-assisted selection for pod-shattering tolerance in soybean.

Soybean seed protein and oil concentrations are important traits that directly affect the quality of soyfoods. Many studies and breeding programmes have been conducted to find major quantitative trait loci (QTL) that regulate protein and oil concentrations and to develop soybean cultivars with high protein and/or oil content. The purpose of this study was to identify these QTL using a selected breeding population. The population was tested in field conditions over a period of 3 years. Seed protein and oil concentrations were measured each year. Single‐nucleotide polymorphisms (SNPs) were used to construct genetic map using a 180K SoyaSNP array, which identified 1,570 SNPs. We identified 12 QTL for seed protein, 11 for seed oil concentration and four for both traits. Among these, 17 QTL were closely mapped to previously reported QTL, whereas ten sites were novel. Several QTL were detected across at least two experimental years. These loci are good candidate QTL for optimal seed protein and oil concentrations. Our results demonstrate that favourable target QTL can be successfully identified using selected breeding populations.