The mosaic oat genome gives insights into a uniquely healthy cereal crop

Cultivated oat (Avena sativa L.) is an allohexaploid (AACCDD, 2n = 6x = 42) thought to have been domesticated more than 3,000 years ago while growing as a weed in wheat, emmer and barley fields in Anatolia1,2. Oat has a low carbon footprint, substantial health benefits and the potential to replace animal-based food products. However, the lack of a fully annotated reference genome has hampered efforts to deconvolute its complex evolutionary history and functional gene dynamics. Here we present a high-quality reference genome of A. sativa and close relatives of its diploid (Avena longiglumis, AA, 2n = 14) and tetraploid (Avena insularis, CCDD, 2n = 4x = 28) progenitors. We reveal the mosaic structure of the oat genome, trace large-scale genomic reorganizations in the polyploidization history of oat and illustrate a breeding barrier associated with the genome architecture of oat. We showcase detailed analyses of gene families implicated in human health and nutrition, which adds to the evidence supporting oat safety in gluten-free diets, and we perform mapping-by-sequencing of an agronomic trait related to water-use efficiency. This resource for the Avena genus will help to leverage knowledge from other cereal genomes, improve understanding of basic oat biology and accelerate genomics-assisted breeding and reanalysis of quantitative trait studies

New DArT markers for oat provide enhanced map coverage and global germplasm characterization

BACKGROUND: Genomic discovery in oat and its application to oat improvement have been hindered by a lack of genetic markers common to different genetic maps, and by the difficulty of conducting whole-genome analysis using high-throughput markers. This study was intended to develop, characterize, and apply a large set of oat genetic markers based on Diversity Array Technology (DArT). RESULTS: Approximately 19,000 genomic clones were isolated from complexity-reduced genomic representations of pooled DNA samples from 60 oat varieties of global origin. These were screened on three discovery arrays, with more than 2000 polymorphic markers being identified for use in this study, and approximately 2700 potentially polymorphic markers being identified for use in future studies. DNA sequence was obtained for 2573 clones and assembled into a non-redundant set of 1770 contigs and singletons. Of these, 705 showed highly significant (Expectation < 10E-10) BLAST similarity to gene sequences in public databases. Based on marker scores in 80 recombinant inbred lines, 1010 new DArT markers were used to saturate and improve the 'Kanota' × 'Ogle' genetic map. DArT markers provided map coverage approximately equivalent to existing markers. After binning markers from similar clones, as well as those with 99% scoring similarity, a set of 1295 non-redundant markers was used to analyze genetic diversity in 182 accessions of cultivated oat of worldwide origin. Results of this analysis confirmed that major clusters of oat diversity are related to spring vs. winter type, and to the presence of major breeding programs within geographical regions. Secondary clusters revealed groups that were often related to known pedigree structure. CONCLUSION: These markers will provide a solid basis for future efforts in genomic discovery, comparative mapping, and the generation of an oat consensus map. They will also provide new opportunities for directed breeding of superior oat varieties, and guidance in the maintenance of oat genetic diversity

Inefficient purifying selection: the mammalian Y chromosome in the rodent genus Mus

Two related genes with potentially similar functions, one on the Y chromosome and one on the X chromosome, were examined to determine if they evolved differently because of their chromosomal positions. Six hundred fifty-seven base pairs of coding sequence of Jarid1d ( Smcy ) on the Y chromosome and Jarid1c ( Smcx ) on the X chromosome were sequenced in 13 rodent taxa. An analysis of replacement and silent substitutions, using a counting method designed for samples with small evolutionary distances, showed a significant difference between the two genes. The different patterns of replacement and silent substitutions within Jarid1d and Jarid1c may be a result of evolutionary mechanisms that are particularly strong on the Y chromosome because of its unique properties. These findings are similar to results of previous studies of Y chromosomal genes in these and other mammalian taxa, suggesting that genes on the mammalian Y evolve in a chromosome-specific manner.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46987/1/335_2005_Article_50.pd

The importance of sexual and asexual reproduction in the recent evolution of Allium vineale

In the weedy plant species Allium vineale (wild garlic), individuals may simultaneously produce sexually and asexually derived offspring, by seed and bulbils, respectively. In this study, genetic and genotypic diversity was determined in samples from 14 E</p

Genetic and environmental factors affecting reproductive variation in Allium vineale

Traits related to allocation of resources to sexual and asexual reproduction, together with seed production, were-scored on Allium vineale plants sampled from five sites in southern Sweden during a period of 4 years. In addition, random amplified polymorp</p

Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat

Genomic prediction is a powerful tool to enhance genetic gain in plant breeding. However, the method is accompanied by various complications leading to low prediction accuracy. One of the major challenges arises from the complex dimensionality of marker data. To overcome this issue, we applied two pre-selection methods for SNP markers viz. LD-based haplotype-tagging and GWAS-based trait-linked marker identification. Six different models were tested with preselected SNPs to predict the genomic estimated breeding values (GEBVs) of four traits measured in 419 winter wheat genotypes. Ten different sets of haplotype-tagged SNPs were selected by adjusting the level of LD thresholds. In addition, various sets of trait-linked SNPs were identified with different scenarios from the training-test combined and only from the training populations. The BRR and RR-BLUP models developed from haplotype-tagged SNPs had a higher prediction accuracy for FHB and SPW by 0.07 and 0.092, respectively, compared to the corresponding models developed without marker pre-selection. The highest prediction accuracy for SPW and FHB was achieved with tagged SNPs pruned at weak LD thresholds (r2 < 0.5), while stringent LD was required for spike length (SPL) and flag leaf area (FLA). Trait-linked SNPs identified only from training populations failed to improve the prediction accuracy of the four studied traits. Pre-selection of SNPs via LD-based haplotype-tagging could play a vital role in optimizing genomic selection and reducing genotyping costs. Furthermore, the method could pave the way for developing low-cost genotyping methods through customized genotyping platforms targeting key SNP markers tagged to essential haplotype blocks

Genomic selection in plant breeding: key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP—theoretically reaching one when using the Pearson's correlation as a metric—is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.552-57