Wheat kernel shape and size has been under selection since early domestication. Kernel morphology is a major consideration in wheat breeding, as it impacts grain yield and quality. A population of 160 recombinant inbred lines (RIL), developed using an elite (ND 705) and a nonadapted genotype (PI 414566), was extensively phenotyped in replicated field trials and genotyped using Infinium iSelect 90K assay to gain insight into the genetic architecture of kernel shape and size. A high density genetic map consisting of 10,172 single nucleotide polymorphism (SNP) markers, with an average marker density of 0.39 cM/marker, identified a total of 29 genomic regions associated with six grain shape and size traits; ~80% of these regions were associated with multiple traits. The analyses showed that kernel length (KL) and width (KW) are genetically independent, while a large number (~59%) of the quantitative trait loci (QTL) for kernel shape traits were in common with genomic regions associated with kernel size traits. The most significant QTL was identified on chromosome 4B, and could be an ortholog of major rice grain size and shape gene GS3 or qGL3. Major and stable loci also were identified on the homeologous regions of Group 5 chromosomes, and in the regions of TaGW2 (6A) and TaGASR7 (7A) genes. Both parental genotypes contributed equivalent positive QTL alleles, suggesting that the nonadapted germplasm has a great potential for enhancing the gene pool for grain shape and size. This study provides new knowledge on the genetic dissection of kernel morphology, with a much higher resolution, which may aid further improvement in wheat yield and quality using genomic tools.

The species cytoplasm specific (scs) genes affect nuclear-cytoplasmic interactions in interspecific hybrids. A radiation hybrid (RH) mapping population of 188 individuals was employed to refine the location of the scs (ae) locus on Triticum aestivum chromosome 1D. "Wheat Zapper," a comparative genomics tool, was used to predict synteny between wheat chromosome 1D, Oryza sativa, Brachypodium distachyon, and Sorghum bicolor. A total of 57 markers were developed based on synteny or literature and genotyped to produce a RH map spanning 205.2 cR. A test-cross methodology was devised for phenotyping of RH progenies, and through forward genetic, the scs (ae) locus was pinpointed to a 1.1 Mb-segment containing eight genes. Further, the high resolution provided by RH mapping, combined with chromosome-wise synteny analysis, located the ancestral point of fusion between the telomeric and centromeric repeats of two paleochromosomes that originated chromosome 1D. Also, it indicated that the centromere of this chromosome is likely the result of a neocentromerization event, rather than the conservation of an ancestral centromere as previously believed. Interestingly, location of scs locus in the vicinity of paleofusion is not associated with the expected disruption of synteny, but rather with a good degree of conservation across grass species. Indeed, these observations advocate the evolutionary importance of this locus as suggested by "Maan's scs hypothesis."

BackgroundThe large and complex genome of bread wheat (Triticum aestivum L., ~17 Gb) requires high resolution genome maps with saturated marker scaffolds to anchor and orient BAC contigs/ sequence scaffolds for whole genome assembly. Radiation hybrid (RH) mapping has proven to be an excellent tool for the development of such maps for it offers much higher and more uniform marker resolution across the length of the chromosome compared to genetic mapping and does not require marker polymorphism per se, as it is based on presence (retention) vs. absence (deletion) marker assay.MethodsIn this study, a 178 line RH panel was genotyped with SSRs and DArT markers to develop the first high resolution RH maps of the entire D-genome of Ae. tauschii accession AL8/78. To confirm map order accuracy, the AL8/78-RH maps were compared with:1) a DArT consensus genetic map constructed using more than 100 bi-parental populations, 2) a RH map of the D-genome of reference hexaploid wheat ’Chinese Spring’, and 3) two SNP-based genetic maps, one with anchored D-genome BAC contigs and another with anchored D-genome sequence scaffolds. Using marker sequences, the RH maps were also anchored with a BAC contig based physical map and draft sequence of the D-genome of Ae. tauschii.ResultsA total of 609 markers were mapped to 503 unique positions on the seven D-genome chromosomes, with a total map length of 14,706.7 cR. The average distance between any two marker loci was 29.2 cR which corresponds to 2.1 cM or 9.8 Mb. The average mapping resolution across the D-genome was estimated to be 0.34 Mb (Mb/cR) or 0.07 cM (cM/cR). The RH maps showed almost perfect agreement with several published maps with regard to chromosome assignments of markers. The mean rank correlations between the position of markers on AL8/78 maps and the four published maps, ranged from 0.75 to 0.92, suggesting a good agreement in marker order. With 609 mapped markers, a total of 2481 deletions for the whole D-genome were detected with an average deletion size of 42.0 Mb. A total of 520 markers were anchored to 216 Ae. tauschii sequence scaffolds, 116 of which were not anchored earlier to the D-genome.ConclusionThis study reports the development of first high resolution RH maps for the D-genome of Ae. tauschii accession AL8/78, which were then used for the anchoring of unassigned sequence scaffolds. This study demonstrates how RH mapping, which offered high and uniform resolution across the length of the chromosome, can facilitate the complete sequence assembly of the large and complex plant genomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2030-2) contains supplementary material, which is available to authorized users.

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>