Genotyping-by-sequencing (GBS) is a high-throughput genotyping approach that is starting to be used in several crop species, including bread wheat. Anchoring GBS tags on chromosomes is an important step towards utilizing them for wheat genetic improvement. Here we use genetic linkage mapping to construct a consensus map containing 28644 GBS markers. Three RIL populations, PBW343 × Kingbird, PBW343 × Kenya Swara and PBW343 × Muu, which share a common parent, were used to minimize the impact of potential structural genomic variation on consensus-map quality. The consensus map comprised 3757 unique positions, and the average marker distance was 0.88 cM, obtained by calculating the average distance between two adjacent unique positions. Significant variation of segregation distortion was observed across the three populations. The consensus map was validated by comparing positions of known rust resistance genes, and comparing them to wheat reference genome sequences recently published by the International Wheat Genome Sequencing Consortium, Rye and Ae. tauschii genomes. Three well-characterized rust resistance genes (Sr58/Lr46/Yr29, Sr2/Yr30/Lr27, and Sr57/Lr34/Yr18) and 15 published QTLs for wheat rusts were validated with high resolution. Fifty-two per cent of GBS tags on the consensus map were successfully aligned through BLAST to the right chromosomes on the wheat reference genome sequence. The consensus map should provide a useful basis for analyzing genome-wide variation of complex traits. The identified genes can then be explored as genetic markers to be used in genomic applications in wheat breeding.

Recent advances in genomics indicate functional significance of a majority of genome sequences and their long range interactions. As a detailed examination of genome organization and function requires very high quality genome sequence, the objective of this study was to improve reference genome assembly of banana (Musa acuminata). We have developed a modular bioinformatics pipeline to improve genome sequence assemblies, which can handle various types of data. The pipeline comprises several semi-automated tools. However, unlike classical automated tools that are based on global parameters, the semi-automated tools proposed an expert mode for a user who can decide on suggested improvements through local compromises. The pipeline was used to improve the draft genome sequence of Musa acuminata. Genotyping by sequencing (GBS) of a segregating population and paired-end sequencing were used to detect and correct scaffold misassemblies. Long insert size paired-end reads identified scaffold junctions and fusions missed by automated assembly methods. GBS markers were used to anchor scaffolds to pseudo-molecules with a new bioinformatics approach that avoids the tedious step of marker ordering during genetic map construction. Furthermore, a genome map was constructed and used to assemble scaffolds into super scaffolds. Finally, a consensus gene annotation was projected on the new assembly from two pre-existing annotations. This approach reduced the total Musa scaffold number from 7513 to 1532 (i.e. by 80 %), with an N50 that increased from 1.3 Mb (65 scaffolds) to 3.0 Mb (26 scaffolds). 89.5 % of the assembly was anchored to the 11 Musa chromosomes compared to the previous 70 %. Unknown sites (N) were reduced from 17.3 to 10.0 %. The release of the Musa acuminata reference genome version 2 provides a platform for detailed analysis of banana genome variation, function and evolution. Bioinformatics tools developed in this work can be used to improve genome sequence assemblies in other species.

Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index. The prediction accuracy was moderate to high (0.44 to 0.69) for growth-related traits. The predictive ability for body weight increased by 17.0% (from 0.69 to 0.83) when missing genotype was imputed. Within population prediction using five-fold across validation approach showed that the gBLUP model performed well for growth traits (weight, length and condition factor), with the coefficient of determination (R2) from linear regression analysis ranging from 0.49 to 0.71. Collectively our results demonstrated, for the first time in yellowtail kingfish, the potential application of genomic selection for growth-related traits in the future breeding program for this species, S. lalandi.

Bambara groundnut [Vigna subterranea (L) Verdc.] is an indigenous legume crop grown mainly in subsistence and small-scale agriculture in sub-Saharan Africa for its nutritious seeds and its tolerance to drought and poor soils. Given that the lack of ex ante sequence is often a bottleneck in marker-assisted crop breeding for minor and underutilised crops, we demonstrate the use of limited genetic information and resources developed within species, but linked to the well characterised common bean (Phaseolus vulgaris) genome sequence and the partially annotated closely related species; adzuki bean (Vigna angularis) and mung bean (Vigna radiata). From these comparisons we identify conserved synteny blocks corresponding to the Linkage Groups (LGs) in bambara groundnut genetic maps and evaluate the potential to identify genes in conserved syntenic locations in a sequenced genome that underlie a QTL position in the underutilised crop genome. Two individual intraspecific linkage maps consisting of DArTseq markers were constructed in two bambara groundnut (2n = 2x = 22) segregating populations: 1) The genetic map of Population IA was derived from F2 lines (n = 263; IITA686 x Ankpa4) and covered 1,395.2 cM across 11 linkage groups; 2) The genetic map of Population TD was derived from F3 lines (n = 71; Tiga Nicuru x DipC) and covered 1,376.7 cM across 11 linkage groups. A total of 96 DArTseq markers from an initial pool of 142 pre-selected common markers were used. These were not only polymorphic in both populations but also each marker could be located using the unique sequence tag (at selected stringency) onto the common bean, adzuki bean and mung bean genomes, thus allowing the sequenced genomes to be used as an initial ‘pseudo’ physical map for bambara groundnut. A good correspondence was observed at the macro synteny level, particularly to the common bean genome. A test using the QTL location of an agronomic trait in one of the bambara groundnut maps allowed the corresponding flanking positions to be identified in common bean, mung bean and adzuki bean, demonstrating the possibility of identifying potential candidate genes underlying traits of interest through the conserved syntenic physical location of QTL in the well annotated genomes of closely related species. The approach of adding pre-selected common markers in both populations before genetic map construction has provided a translational framework for potential identification of candidate genes underlying a QTL of trait of interest in bambara groundnut by linking the positions of known genetic effects within the underutilised species to the physical maps of other well-annotated legume species, without the need for an existing whole genome sequence of the study species. Identifying the conserved synteny between underutilised species without complete genome sequences and the genomes of major crops and model species with genetic and trait data is an important step in the translation of resources and information from major crop and model species into the minor crop species. Such minor crops will be required to play an important role in future agriculture under the effects of climate change.

Net form net blotch (NFNB), caused by the fungal pathogen Pyrenophora teres f. teres, is an important foliar disease present in all barley-producing regions of the world. This fungus is a hemibiotrophic and heterothallic ascomycete, where sexual recombination can lead to changes in disease expression in the host. Knowledge of the genetic architecture and genes involved in virulence is vital to increase the durability of NFNB resistance in barley cultivars. We used a genome-wide association mapping approach to characterize P. teres f. teres genomic regions associated with virulence in Australian barley cultivars. One hundred eighty-eight P. teres f. teres isolates collected across five Australian states were genotyped using Diversity Arrays Technology sequence markers and phenotyped across 20 different barley genotypes. Association mapping identified 14 different genomic regions associated with virulence, with the majority located on P. teres f. teres chromosomes 3 and 5 and one each present on chromosomes 1, 6, and 9. Four of the regions identified were confirmed by quantitative trait loci (QTL) mapping. The QTL regions are discussed in the context of their genomic architecture together with examination of their gene contents, which identified 20 predicted effectors. The number of QTL shown in this study at the population level clearly illustrates a complex genetic basis of P. teres f. teres virulence compared with pure necrotrophs, such as the wheat pathogens Parastagonospora nodorum and Parastagonospora tritici-repentis.

The objectives of the research were to determine the position of quantitative trait loci (QTL) for α-amylase activity on the genetic map of a rye recombinant inbred line population—S120 × S76—and to compare them to known QTL for preharvest sprouting and heading earliness. Fourteen QTL for α-amylase activity on all seven chromosomes were identified. The detected QTL were responsible for 6.09–23.32% of α-amylase activity variation. The lowest LOD value (2.22) was achieved by locus QAa4R-M3 and the highest (7.79) by locus QAa7R-M1. Some QTL intervals for features of interest overlapped partially or completely. There were six overlapping QTL for α-amylase activity and preharvest sprouting (on 1R, 3R, 4R, 6R, 7R) and the same number for preharvest sprouting and heading earliness (on 1R, 2R, 6R, 7R). Furthermore, there was one interval partially common to all three traits, mapped on the long arm of chromosome 1R. Testing of lines originating from hybrid breeding programs, such as S120 and S76, may provide important information about the most significant genes and markers for selection in commercial breeding. Among the statistically significant markers selected in the Kruskal–Wallis test (P < 0.005), there were 55 common ones for preharvest sprouting and heading earliness (1R, 2R, 6R), 30 markers coinciding between α-amylase activity and preharvest sprouting (5R, 7R) and one marker for α-amylase activity and heading earliness (6R).