About 8,000 years ago in the Fertile Crescent, a spontaneous hybridization of the wild diploid grass Aegilops tauschii (2n 5 14; DD) with the cultivated tetraploid wheat Triticum turgidum (2n 5 4x 5 28; AABB) resulted in hexaploid wheat (T. aestivum; 2n 5 6x 5 42; AABBDD) 1,2 . Wheat has since become a primary staple crop worldwide as a result of its enhanced adaptability to a wide range of climates and improved grain quality for the production of baker's flour 2 . Here we describe sequencing the Ae. tauschii genome and obtaining a roughly 90-fold depth of short reads from libraries with various insert sizes, to gain a better understanding of this genetically complex plant. The assembled scaffolds represented 83.4% of the genome, of which 65.9% comprised transposable elements. We generated comprehensive RNA-Seq data and used it to identify 43,150 protein-coding genes, of which 30,697 (71.1%) were uniquely anchored to chromosomes with an integrated high-density genetic map. Whole-genome analysis revealed gene family expansion in Ae. tauschii of agronomically relevant gene families that were associated with disease resistance, abiotic stress tolerance and grain quality. This draft genome sequence provides insight into the environmental adaptation of bread wheat and can aid in defining the large and complicated genomes of wheat species.We selected Ae. tauschii accession AL8/78 for genome sequencing because it has been extensively characterized genetically (Supplementary Information). Using a whole genome shotgun strategy, we generated 398 Gb of high-quality reads from 45 libraries with insert sizes ranging from 200 bp to 20 kb (Supplementary Information). A hierarchical, iterative assembly of short reads employing the parallelized sequence assembler SOAPdenovo 3 achieved contigs with an N50 length (minimum length of contigs representing 50% of the assembly) of 4,512 bp (Table 1). Paired-end information combined with an additional 18.4 Gb of Roche/454 long-read sequences was used sequentially to generate 4.23-Gb scaffolds (83.4% were non-gapped contiguous sequences) with an N50 length of 57.6 kb (Supplementary Information). The assembly represented 97% of the 4.36-Gb genome as estimated by K-mer analysis (Supplementary Information). We also obtained 13,185 Ae. tauschii expressed sequence tag (EST) sequences using Sanger sequencing, of which 11,998 (91%) could be mapped to the scaffolds with more than 90% coverage (Supplementary Information).To aid in gene identification, we performed RNA-Seq (53.2 Gb for a 117-Mb transcriptome assembly) on 23 libraries representing eight tissues including pistil, root, seed, spike, stamen, stem, leaf and sheath (Supplementary Information). Using both evidence-based and de novo gene predictions, we identified 34,498 high-confidence protein-coding loci. FGENESH 4 and GeneID models were supported by a 60% overlap with either our ESTs and RNA-Seq reads, or with homologous proteins. More than 76% of the gene models had a significant match (E value # 10 25; alignment length $ 60%) in the ...

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BackgroundSesame is an important high-quality oil seed crop. The sesame genome was de novo sequenced and assembled in 2014 (version 1.0); however, the number of anchored pseudomolecules was higher than the chromosome number (2n = 2x = 26) due to the lack of a high-density genetic map with 13 linkage groups.ResultsWe resequenced a permanent population consisting of 430 recombinant inbred lines and constructed a genetic map to improve the sesame genome assembly. We successfully anchored 327 scaffolds onto 13 pseudomolecules. The new genome assembly (version 2.0) included 97.5 % of the scaffolds greater than 150 kb in size present in assembly version 1.0 and increased the total pseudomolecule length from 233.7 to 258.4 Mb with 94.3 % of the genome assembled and 97.2 % of the predicted gene models anchored. Based on the new genome assembly, a bin map including 1,522 bins spanning 1090.99 cM was generated and used to identified 41 quantitative trait loci (QTLs) for sesame plant height and 9 for seed coat color. The plant height-related QTLs explained 3–24 % the phenotypic variation (mean value, 8 %), and 29 of them were detected in at least two field trials. Two major loci (qPH-8.2 and qPH-3.3) that contributed 23 and 18 % of the plant height were located in 350 and 928-kb spaces on Chr8 and Chr3, respectively. qPH-3.3, is predicted to be responsible for the semi-dwarf sesame plant phenotype and contains 102 candidate genes. This is the first report of a sesame semi-dwarf locus and provides an interesting opportunity for a plant architecture study of the sesame. For the sesame seed coat color, the QTLs of the color spaces L*, a*, and b* were detected with contribution rates of 3–46 %. qSCb-4.1 contributed approximately 39 % of the b* value and was located on Chr4 in a 199.9-kb space. A list of 32 candidate genes for the locus, including a predicted black seed coat-related gene, was determined by screening the newly anchored genome.ConclusionsThis study offers a high-density genetic map and an improved assembly of the sesame genome. The number of linkage groups and pseudomolecules in this assembly equals the number of sesame chromosomes for the first time. The map and updated genome assembly are expected to serve as a platform for future comparative genomics and genetic studies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2316-4) contains supplementary material, which is available to authorized users.

The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding. The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas ). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.

Wild soybean is a typical short-day plant that begins flowering when the days are shorter than its critical photoperiod. Soybean was domesticated in the temperate region of East Asia at the relatively high latitude, and the breeding and release of soybean varieties have historically centered on mid-and high-latitude temperate regions. Low-latitude areas with tropical and subtropical climates were previously considered unsuitable for soybean production because most temperate soybean varieties exhibited precocious flowering and early maturity and suffered from low yields. The discovery and introduction of the long juvenile trait into soybean varieties in the 1970s (Hartwig and Kiihl, 1979) fundamentally changed global soybean production in a way that has had an enormous influence on commodity markets. This trait delays flowering and thereby ensures sufficient vegetative growth prior to the developmental transition to reproductive growth. The long juvenile trait thus solved the early maturation and low yield problems that had hitherto prevented economically viable soybean production in lowlatitude regions (Destro et al., 2001). The United States and Brazil pioneered the introduction of the long juvenile trait in low-latitude soybean breeding programs. Brazil has expanded its soybean production enormously, from 1 million hectares in 1970 (Brown, 2004) to over 33 million hectares in 2016 (http:// gain.fas.usda.gov/Recent%20GAIN%20Publications/Oilseeds %20and%20Products%20Update_Brasilia_Brazil_12-1-2016. pdf).

Expressed sequence tags (ESTs) provide a valuable resource for the development of simple sequence repeat (SSR) or microsatellite markers. This study identified SSRs within ESTs from Nelumbo nucifera (lotus or sacred lotus), developed markers from them, and assessed the potential of those markers for diversity analysis. Within 2207 ESTs from N. nucifera downloaded from GenBank, 1483 unigenes (303 contigs and 1180 singletons) were identified. After eliminating for redundancy, 125 SSR-containing ESTs were derived, and 71 unique SSRs were detected with an average density of one SSR per 13.04 kb. Dinucleotide repeats were the dominant motif in N. nucifera, whereas the sequences AG/TC/GA/CT, AAG/TTC/GAT/AGA, and AAAGCC were the most frequent of di-, tri-, and hexanucleotide motifs, respectively. The AG/TC (40.85%) and AAG (5.63%) motifs were predominant for the di- and trinucleotide repeats, respectively. Sixty-two SSR-containing ESTs were suitable for primer design. From these sequences, 23 EST-SSR markers were developed and were applied to 39 cultivated varieties of N. nucifera, 10 accessions of wild N. nucifera, and 1 accession of Nelumbo lutea (American lotus). Genetic diversity and genetic relationships were examined by constructing unweighted pair-group method with arithmetic average dendrograms and principal coordinates analysis plots based on SSR polymorphisms. Results indicated genetic differentiation between cultivated and wild lotus and between seed lotus cultivars and rhizome lotus cultivars. These EST-SSR markers will be useful for further studies of the evolution and diversity of Nelumbo.

BackgroundOne of the overarching goals of soybean breeding is to develop lines that combine increased yield with improved quality characteristics. High-density-marker QTL mapping can serve as an effective strategy to identify novel genomic information to facilitate crop improvement. In this study, we genotyped a recombinant inbred line (RIL) population (Zhonghuang 24 × Huaxia 3) using a restriction-site associated DNA sequencing (RAD-seq) approach. A high-density soybean genetic map was constructed and used to identify several QTLs that were shown to influence six yield-related and two quality traits.ResultsA total of 47,472 single-nucleotide polymorphisms (SNPs) were detected for the RILs that were integrated into 2639 recombination bin units, with an average distance of 1.00 cM between adjacent markers. Forty seven QTLs for yield-related traits and 13 QTLs for grain quality traits were found to be distributed on 16 chromosomes in the 2 year studies. Among them, 18 QTLs were stable, and were identified in both analyses. Twenty six QTLs were identified for the first time, with a single QTL (qNN19a) in a 56 kb region explaining 32.56% of phenotypic variation, and an additional 10 of these were novel, stable QTLs. Moreover, 8 QTL hotpots on four different chromosomes were identified for the correlated traits.ConclusionsWith RAD-sequencing, some novel QTLs and important QTL clusters for both yield-related and quality traits were identified based on a new, high-density bin linkage map. Three predicted genes were selected as candidates that likely have a direct or indirect influence on both yield and quality in soybean. Our findings will be helpful for understanding common genetic control mechanisms of co-localized traits and to select cultivars for further analysis to predictably modulate soybean yield and quality simultaneously.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3854-8) contains supplementary material, which is available to authorized users.

Key messageUsing a combination of phenotypic screening, genetic and statistical analyses, and high-throughput genome-wide sequencing, we have finely mapped a dominant Phytophthora resistance gene in soybean cultivar Wayao.Abstract Phytophthora root rot (PRR) caused by Phytophthora sojae is one of the most important soil-borne diseases in many soybean-production regions in the world. Identification of resistant gene(s) and incorporating them into elite varieties are an effective way for breeding to prevent soybean from being harmed by this disease. Two soybean populations of 191 F2 individuals and 196 F7:8 recombinant inbred lines (RILs) were developed to map Rps gene by crossing a susceptible cultivar Huachun 2 with the resistant cultivar Wayao. Genetic analysis of the F2 population indicated that PRR resistance in Wayao was controlled by a single dominant gene, temporarily named RpsWY, which was mapped on chromosome 3. A high-density genetic linkage bin map was constructed using 3469 recombination bins of the RILs to explore the candidate genes by the high-throughput genome-wide sequencing. The results of genotypic analysis showed that the RpsWY gene was located in bin 401 between 4466230 and 4502773 bp on chromosome 3 through line 71 and 100 of the RILs. Four predicted genes (Glyma03g04350, Glyma03g04360, Glyma03g04370, and Glyma03g04380) were found at the narrowed region of 36.5 kb in bin 401. These results suggest that the high-throughput genome-wide resequencing is an effective method to fine map PRR candidate genes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2869-5) contains supplementary material, which is available to authorized users.