It is only recently, with the advent of long-read sequencing technologies, that we are beginning to uncover previously uncharted regions of complex and inherently recursive plant genomes. To comprehensively study and exploit the genome of the neglected oilseed Brassica nigra, we generated two high-quality nanopore de novo genome assemblies. The N50 contig lengths for the two assemblies were 17.1 Mb (12 contigs), one of the best among 324 sequenced plant genomes, and 0.29 Mb (424 contigs), respectively, reflecting recent improvements in the technology. Comparison with a de novo short-read assembly corroborated genome integrity and quantified sequence-related error rates (0.2%). The contiguity and coverage allowed unprecedented access to low-complexity regions of the genome. Pericentromeric regions and coincidence of hypomethylation enabled localization of active centromeres and identified centromere-associated ALE family retro-elements that appear to have proliferated through relatively recent nested transposition events (<1 Ma). Genomic distances calculated based on synteny relationships were used to define a post-triplication Brassica-specific ancestral genome, and to calculate the extensive rearrangements that define the evolutionary distance separating B. nigra from its diploid relatives.

The fungal pathogen Sclerotinia sclerotiorum causes stem rot of oilseed rape (Brassica napus) worldwide. In preparation for genome-wide association mapping (GWAM) of sclerotinia resistance in B. napus, 152 accessions from diverse geographical regions were screened with a single Canadian isolate, #321. Plants were inoculated by attaching mycelium plugs to the main stem at full flower. Lesion lengths measured 7, 14 and 21 days after inoculation were used to calculate the area under the disease progress curve (AUDPC). Depth of penetration was noted and used to calculate percent soft and collapsed lesions (% s + c). The two disease traits were highly correlated (r = 0.93). Partially resistant accessions (AUDPC <7 and % s + c <2) were identified primarily from South Korea and Japan with a few from Pakistan, China and Europe. Genotyping of accessions with 84 simple sequence repeat markers provided 690 polymorphic loci for GWAM. The general linear model in TASSEL best fitted the data when adjusted for population structure (STRUCTURE), GLM + Q. After correction for positive false discovery rate, 34 loci were significantly associated with both disease traits of which 21 alleles contributed to resistance, while the remaining enhanced susceptibility. The phenotypic variation explained by the loci ranged from 6 to 25 %. Five loci mapped to published quantitative trait loci conferring sclerotinia resistance in Chinese lines.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-016-0496-5) contains supplementary material, which is available to authorized users.

BackgroundPhenotypic variation is determined by a combination of genotype, environment and their interactions. The realization that allelic diversity can be both genetic and epigenetic allows the environmental component to be further separated. Partitioning phenotypic variation observed among inbred lines with an altered epigenome can allow the epigenetic component controlling quantitative traits to be estimated. To assess the contribution of epialleles on phenotypic variation and determine the fidelity with which epialleles are inherited, we have developed a novel hypomethylated population of strawberry (2n = 2x = 14) using 5-azacytidine from which individuals with altered phenotypes can be identified, selected and characterized.ResultsThe hypomethylated population was generated using an inbred strawberry population in the F. vesca ssp. vesca accession Hawaii 4. Analysis of whole genome sequence data from control and hypomethylated lines indicate that 5-azacytidine exposure does not increase SNP above background levels. The populations contained only Hawaii 4 alleles, removing introgression of alternate F. vesca alleles as a potential source of variation. Although genome sequencing and genetic marker data are unable to rule out 5-azacytidine induced chromosomal rearrangements as a potential source of the trait variation observed, none were detected in our survey. Quantitative trait variation focusing on flowering time and rosette diameter was scored in control and treated populations where expanded levels of variation were observed among the hypomethylated lines. Methylation sensitive molecular markers indicated that 5-azacytidine induced alterations in DNA methylation patterns and inheritance of methylation patterns were confirmed by bisulfite sequencing of targeted regions. It is possible that methylation polymorphisms might underlie or have induced genetic changes underlying the observable differences in quantitative phenotypes.ConclusionsThis population developed in a uniform genetic background provides a resource for the discovery of new variation controlling quantitative traits. Genome sequence analysis indicates that 5-azacytidine did not induce point mutations and the induced variation is largely restricted to DNA methylation. Using this resource, we have identified new variation and demonstrated the inheritance of both variant trait and methylation patterns. Although direct associations remain to be determined, these data suggest epigenetic variation might be subject to selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0936-8) contains supplementary material, which is available to authorized users.

1537 ReseaRch B raSSica napuS is an amphidiploid species formed from multiple independent fusion events between ancestors of Brassica rapa L. (AA; 2n = 20) and Brassica oleracea L. (CC; 2n = 18) (U, 1935). The diploid Brassica species most likely originated from a hexaploid ancestor (Parkin et al., 2005). Exchange of DNA segments between the A and C genome is rare and no major chromosomal rearrangements have occurred since the fusion of the two progenitor genomes (Howell et al., 2008;Parkin and Lydiate, 1997). A recent study based on comparative analysis of chloroplast haplotypes indicate that B. rapa is likely the maternal ancestor of B. napus rather than B. oleracea as previously thought (Allender and King, 2010). The B. napus gene pool can be subdivided into biannual vegetable, rutabaga, or swede subspecies [rapifera Metzg. including the variety napobrassica) and annual oilseed or fodder subspecies (napus or subspecies oleifera (DC) Metzg.], which include economically important crops primarily used for vegetable oil, leafy and root vegetables, fodder and to a lesser extent for industrial oil and biodiesel (McNaughton, 1995). It is believed that B. napus is a relatively ABSTRACT Information on genetic diversity and population structure is needed for continued improvement in Brassica napus L. A total of 169 B. napus lines, collected between 1970 and 2000 in Australia,

High contiguity long read assembly of Brassica nigra allows localization of activecentromeres and provides insights into the ancestral Brassica genome.

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.