Search

Key message: Eighty-six R1 QTLs accounting for 89.92% phenotypic variance in a soybean RIL population were identified using RTM-GWAS with SNPLDB marker which performed superior over CIM and MLM-GWAS with BIN/SNPLDB marker.Abstract: A population (NJRIKY) composed of 427 recombinant inbred lines (RILs) derived from Kefeng-1 × NN1138-2 (MGII × MGV, MG maturity group) was applied for detecting flowering date (R1) quantitative trait locus (QTL) system in soybean. From a low-depth re-sequencing (~ 0.75 ×), 576,874 SNPs were detected and organized into 4737 BINs (recombination breakpoint determinations) and 3683 SNP linkage disequilibrium blocks (SNPLDBs), respectively. Using the association mapping procedures “Restricted Two-stage Multi-locus Genome-wide Association Study” (RTM-GWAS), “Mixed Linear Model Genome-wide Association Study” (MLM-GWAS) and the linkage mapping procedure “Composite Interval Mapping” (CIM), 67, 36 and 10 BIN-QTLs and 86, 14 and 23 SNPLDB-QTLs were detected with their phenotypic variance explained (PVE) 88.70-89.92% (within heritability 98.2%), 146.41-353.62% (overflowing) and 88.29-172.34% (overflowing), respectively. The RTM-GWAS with SNPLDBs which showed to be more efficient and reasonable than the others was used to identify the R1 QTL system in NJRIKY. The detected 86 SNPLDB-QTLs with their PVE from 0.02 to 30.66% in a total of 89.92% covered 51 out of 104 R1 QTLs in 18 crosses in SoyBase and 26 out of 139 QTLs in a nested association mapping population, while the rest 29 QTLs were novel ones. From the QTL system, 52 candidate genes were annotated, including the verified gene E1, E2, E9 and J, and grouped into 3 categories of biological processes, among which 24 genes were enriched into three protein-protein interaction networks, suggesting gene networks working together. Since NJRIKY involves only MGII and MGV, the QTL/gene system among MG000-MGX should be explored further. [ABSTRACT FROM AUTHOR]

Avicennia marina is a widely distributed mangrove species that thrives in high-salinity habitats. It plays a significant role in supporting coastal ecosystem and holds unique potential for studying molecular mechanisms underlying ecological adaptation. Despite and sometimes because of its numerous merits, this species is facing increasing pressure of exploitation and deforestation. Both study on adaptation mechanisms and conservation efforts necessitate more genomic resources for A. marina. In this study, we used Illumina sequencing of an A. marina foliar cDNA library to generate a transcriptome dataset for gene and marker discovery. We obtained 40 million high-quality reads and assembled them into 91,125 unigenes with a mean length of 463 bp. These unigenes covered most of the publicly available A. marina Sanger ESTs and greatly extended the repertoire of transcripts for this species. A total of 54,497 and 32,637 unigenes were annotated based on homology to sequences in the NCBI non-redundant and the Swiss-prot protein databases, respectively. Both Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed some transcriptomic signatures of stress adaptation for this halophytic species. We also detected an extraordinary amount of transcripts derived from fungal endophytes and demonstrated the utility of transcriptome sequencing in surveying endophyte diversity without isolating them out of plant tissues. Additionally, we identified 3,423 candidate simple sequence repeats (SSRs) from 3,141 unigenes with a density of one SSR locus every 8.25 kb sequence. Our transcriptomic data will provide valuable resources for ecological, genetic and evolutionary studies in A. marina. [ABSTRACT FROM AUTHOR]

The relative importance of various types of quantitative trait locus (QTL) conferring oil content and its fatty acid components in soybean seeds was assessed through testing a recombinant inbred line (RIL) population (derived from KF1 × NN1138-2) in randomized blocks experiments in 2004-2006. The contents of oil and oleic, linoleic, linolenic, palmitic and stearic acids were determined with automatic Soxhlet extraction system and gas chromatography, respectively. Based on the established genetic linkage map with 834 markers, QTLNetwork2.0 was used to detect QTL under the genetic model composed of additive, additive × additive (epistasis), additive × year and epistasis × year effects. The contributions to the phenotypic variances of additive QTL and epistatic QTL pairs were 15.7% (3 QTL) and 10.8% (2 pairs) for oil content, 10.4% (3 QTL) and 10.3% (3 pairs) for oleic acid, 11.6% (3 QTL) and 8.5% (2 pairs) for linoleic acid, 28.5% (7 QTL) and 7.6% (3 pairs) for linolenic acid, 27.0% (6 QTL) and 16.6% (7 pairs) for palmitic acid and 29.7% (5 QTL) and 4.3% (1 pair) for stearic acid, respectively. Those of additive QTL by year interaction were small and no epistatic QTL pair by year interaction was found. Among the 27 additive QTL and 36 epistatic QTL (18 pairs), three are duplicated between the two QTL types. A large difference was found between the genotypic variance among RILs and the total variance of mapped QTL, which accounted for 52.9-74.8% of the genotypic variation, much larger than those of additive QTL and epistatic QTL pairs. This part of variance was recognized as that due to a collection of unmapped minor QTL, like polygenes in biometrical genetics, and was designated as collective unmapped minor QTL. The results challenge the breeders for how to pyramid different types of QTL. In addition, the present study supports the mapping strategy of a full model scanning followed by verification with other procedures corresponding to the first results. [ABSTRACT FROM AUTHOR]

With 3 figures and 6 tables Abstract The relative importance of quantitative trait locus (QTL) types controlling aluminium (Al) tolerance in a recombinant inbred line population of soybean was assessed under greenhouse sand culture in 2 years. Three growth-related indicators for Al-tolerance, namely relative total plant dry weight (RTDW), relative shoot dry weight (RSDW) and relative root dry weight (RRDW) were analysed for genetic architecture. RTDW showed relatively higher correlations and shared marker regions with RSDW and RRDW. Four additive QTL, four epistatic QTL pairs and collective unmapped QTL were identified for RTDW, with respective contributions of 22.30%, 14.86% and 40.64%, in a total genetic contribution of 77.80% to phenotypic variation, while QTL × Environment contribution was relatively negligible. Similar results were found for RSDW and RRDW. Altogether, seven additive QTL, eleven epistatic QTL pairs and collective unmapped QTL were involved with Al-tolerance. Among them, one major QTL linked to marker region GMKF046-Sat_128 on B1 was shared by the three traits. The results imply that the three QTL types are all relevant and should be comprehensively utilized for the improvement of Al-tolerance in soybean. [ABSTRACT FROM AUTHOR]

To investigate the genetic mechanism of Al-tolerance in soybean, a recombinant inbred line population (RIL) with 184 F2:7:11 lines derived from the cross of Kefeng No.1 × Nannong 1138-2 (Al-tolerant × Al-sensitive) were tested in pot experiment with sand culture medium in net room in Nanjing. Four traits, i.e. plant height, number of leaves, shoot dry weight and root dry weight at seedling stage, were evaluated and used to calculate the average membership index ( FAi) as the indicator of Al-tolerance. The composite interval mapping (CIM) under WinQTL Cartographer v. 2.5 detected five QTLs (i.e. qFAi-1, qFAi-2, qFAi-3, qFAi-4 and qFAi-5), explaining 5.20%–9.07% of the total phenotypic variation individually. While with the multiple interval mapping (MIM) of the same software, five QTLs ( qFAi-1, qFAi-5, qFAi-6, qFAi-7, and qFAi-8) explaining 5.7%–24.60% of the total phenotypic variation individually were mapped. Here qFAi-1 and qFAi-5 were detected by both CIM and MIM with the locations in a same flanking marker region, GMKF046-GMKF080 on B1 and satt278-sat_95 on L, respectively. While qFAi-2 under CIM and qFAi-6 under MIM both on D1b2 were located in neighboring regions with their confidence intervals overlapped and might be the same locus. Segregation analysis under major gene plus polygene inheritance model showed that Al-tolerance was controlled by two major genes ( h2mg = 33.05%) plus polygenes ( h2pg = 52.73%). Both QTL mapping and segregation analysis confirmed two QTLs responsible for Al-tolerance with relatively low heritability, and there might be a third QTL, confounded with the polygenes in segregation analysis. [ABSTRACT FROM AUTHOR]

Ethylene plays important roles in plant growth, development, and stress responses, and ethylene receptors have been identified and studied extensively in various plant species. Here we report the cloning of four ethylene receptor genes from soybean, i.e. GmETR1, GmERS1, GmETR2 and GmEIN4. Construction of the phylogenic tree showed that GmETR1 and GmERS1 belong to subfamily I whereas GmETR2 and GmEIN4 belong to subfamily II. The four ethylene receptor genes showed different tissue-specific expression patterns in roots, stems, leaves, cotyledons, flowers, pods and seeds of soybean. These genes were differentially regulated by various abiotic stresses and plant hormones. The possible roles of the four genes in soybean plant were also discussed. [ABSTRACT FROM AUTHOR]