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      Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass

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          Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain.

          Abstract

          Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.

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          The online version of this article (10.1007/s00122-018-3121-7) contains supplementary material, which is available to authorized users.

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          A synteny-based draft genome sequence of the forage grass Lolium perenne.

          Here we report the draft genome sequence of perennial ryegrass (Lolium perenne), an economically important forage and turf grass species that is widely cultivated in temperate regions worldwide. It is classified along with wheat, barley, oats and Brachypodium distachyon in the Pooideae sub-family of the grass family (Poaceae). Transcriptome data was used to identify 28,455 gene models, and we utilized macro-co-linearity between perennial ryegrass and barley, and synteny within the grass family, to establish a synteny-based linear gene order. The gametophytic self-incompatibility mechanism enables the pistil of a plant to reject self-pollen and therefore promote out-crossing. We have used the sequence assembly to characterize transcriptional changes in the stigma during pollination with both compatible and incompatible pollen. Characterization of the pollen transcriptome identified homologs to pollen allergens from a range of species, many of which were expressed to very high levels in mature pollen grains, and are potentially involved in the self-incompatibility mechanism. The genome sequence provides a valuable resource for future breeding efforts based on genomic prediction, and will accelerate the development of new varieties for more productive grasslands.
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            Accuracy of genomic selection for alfalfa biomass yield in different reference populations

            Background Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. Results Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25 % for Me and 30 % for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection. Conclusions Genomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2212-y) contains supplementary material, which is available to authorized users.
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              The Analysis of Crop Variety Evaluation Data in Australia

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                Author and article information

                Contributors
                luke.pembleton@ecodev.vic.gov.au
                Journal
                Theor Appl Genet
                Theor. Appl. Genet
                TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0040-5752
                1432-2242
                2 June 2018
                2 June 2018
                2018
                : 131
                : 9
                : 1891-1902
                Affiliations
                [1 ]ISNI 0000 0004 0407 2669, GRID grid.452283.a, Agriculture Victoria Research, , AgriBio, Centre for AgriBioscience, ; 5 Ring Road, Bundoora, VIC 3083 Australia
                [2 ]New Zealand Agriseeds, 2547 Old West Coast Road, Christchurch, 7671 New Zealand
                [3 ]ISNI 0000 0001 2342 0938, GRID grid.1018.8, School of Applied Systems Biology, , La Trobe University, ; Bundoora, VIC 3086 Australia
                Author notes

                Communicated by Hiroyoshi Iwata.

                Author information
                http://orcid.org/0000-0002-3555-4586
                Article
                3121
                10.1007/s00122-018-3121-7
                6096624
                29860624
                7389a893-ccaf-4054-b4cb-e10a982f2700
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 15 November 2017
                : 24 May 2018
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                © Springer-Verlag GmbH Germany, part of Springer Nature 2018

                Genetics
                Genetics

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