Transcriptomic characterization of two major Fusarium resistance quantitative trait loci (QTLs), Fhb1 and Qfhs.ifa-5A, identifies novel candidate genes

Fusarium head blight, caused by Fusarium graminearum, is a devastating disease of wheat. We developed near-isogenic lines (NILs) differing in the two strongest known F. graminearum resistance quantitative trait loci (QTLs), Qfhs.ndsu-3BS (also known as resistance gene Fhb1) and Qfhs.ifa-5A, which are located on the short arm of chromosome 3B and on chromosome 5A, respectively. These NILs showing different levels of resistance were used to identify transcripts that are changed significantly in a QTL-specific manner in response to the pathogen and between mock-inoculated samples. After inoculation with F. graminearum spores, 16 transcripts showed a significantly different response for Fhb1 and 352 for Qfhs.ifa-5A. Notably, we identified a lipid transfer protein which is constitutively at least 50-fold more abundant in plants carrying the resistant allele of Qfhs.ifa-5A. In addition to this candidate gene associated with Qfhs.ifa-5A, we identified a uridine diphosphate (UDP)-glycosyltransferase gene, designated TaUGT12887, exhibiting a positive difference in response to the pathogen in lines harbouring both QTLs relative to lines carrying only the Qfhs.ifa-5A resistance allele, suggesting Fhb1 dependence of this transcript. Yet, this dependence was observed only in the NIL with already higher basal resistance. The complete cDNA of TaUGT12887 was reconstituted from available wheat genomic sequences, and a synthetic recoded gene was expressed in a toxin-sensitive strain of Saccharomyces cerevisiae. This gene conferred deoxynivalenol resistance, albeit much weaker than that observed with the previously characterized barley HvUGT13248

Genomic selection in bread wheat

Die jüngsten Fortschritte in der molekularen Markeranalyse und das Aufkommen von Hochdurchsatz-Genotypisierungsplattformen ermöglichen die genomweite Analyse einer Weizenzuchtlinie zu den Kosten einer einzelnen Parzelle in einem herkömmlichen Feldversuch. Genomische Selektion nutzt diese dichten Markerinformationen und kombiniert sie mit phänotypischen Informationen, um den Zuchtwert von neuen Kandidatenlinien vorherzusagen. Die vorliegende Arbeit stellt eine der ersten Anwendungen von genomischer Selektion in einem realen Weizenzüchtungsprogramm dar. Mittels Genotypisierung-durch-Sequenzierung konnten Elite-Weizenzuchtlinien auf Basis von Einzelbasenunterschieden charakterisiert und genomische Fingerabdrücke erstellt werden. Basierend auf daraus erstellten Vorhersagemodellen war es möglich, genomische Zuchtwerte für ungetestete Weizenlinien ausschließlich anhand ihres genomischen Fingerabdrucks vorherzusagen. Verschiedene Faktoren, die die genomische Vorhersage beeinflussen können, wurden in dieser Arbeit untersucht. RR-BLUP, ein statistisches Vorhersagemodell, stellte sich in diesen Vergleichen als stabiles und leistungsfähiges Modell heraus, das vor allem durch kurze Berechnungszeiten besticht. Zudem wurde der Einfluss der Populationsgröße sowie die erforderliche Anzahl von genetischen Markern untersucht. Eine Trainingspopulationsgröße von 250 Weizenlinien war bereits ausreichend um gute Vorhersageergebnisse zu erzielen, wohingegen der Einsatz von mehr als 3000 Markern empfehlenswert erscheint. In einem abschließenden Experiment, das im Zuge eines laufenden Weizenzüchtungsprogramms durchgeführt wurde, konnte gezeigt werden, dass Zuchtwerte aufgrund eines einzelnen genetischen Fingerabdrucks ebenbürtig mit den Zuchtwerten aus konventionellen Feldversuchen sind. Die Ergebnisse lassen darauf schließen, dass Genomische Selektion in der Pflanzenzüchtung ebenso erfolgreich sein kann, wie sie in der Tierzucht bereits ist.Recent advances in molecular marker analysis and the advent of high-throughput genotyping platforms now enable genome-wide marker analysis of one breeding line to be carried out at the cost of one plot in a conventional field trial. Genomic selection, a new paradigm in plant breeding makes use of this dense marker information and integrates it with phenotypic information to accurately predict the breeding value of new candidate lines. The work at hand represents one of the first applications of genomic selection in an actual plant breeding program. The latest genotyping technologies were evaluated for their use in genomic selection. Using genotyping-by-sequencing it was possible to characterize elite wheat breeding lines based on single base differences to create genomic fingerprints. These fingerprints were integrated with conventional phenotypic information to create statistical prediction models. Based on these models it was possible to predict genomic breeding values for untested wheat lines solely based on their genomic fingerprint. Factors that may influence the genomic predictions were investigated in this work. RR-BLUP, a statistical prediction model, turned out to be a stable and well performing model with the advantage of short computation times. Moreover, the influence of the population size was investigated as well as the required number of genetic markers. A training population size of 250 wheat lines was already sufficient for achieving good prediction results whereas the use of at least 3000 markers seems advisable. Finally, in experiment within an actual plant breeding setting, it could be shown that breeding values based on a single genomic fingerprint were on par with breeding values obtained by conventional field trials. The results of this work indicate that genomic selection can be as successful in plant breeding as it is already in animal breeding.eingereicht von Christian AmetzZsfassung in dt. SpracheWien, Univ. für Bodenkultur, Diss., 2015OeBB(VLID)193136

Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain

Improving the baking quality of bread wheat by genomic selection in early generations (vol 131, pg 477, 2017)

Gungor, Huseyin/0000-0001-6708-6337; Michel, Sebastian/0000-0001-6636-2694; Epure, Doru-Gabriel/0000-0002-6209-2794; Buerstmayr, Hermann/0000-0002-0748-2351WOS: 000423400100018PubMed: 29340751Unfortunately, the co-author, Dr. Gungor was missed out in the authorship of original publication by mistake and it is updated now.University of Natural Resources and Life Sciences Vienna (BOKU); EU Eurostars project "E! 6399 Genomic selection of wheat varieties for robustness, yield and quality"; EU Eurostars project "E! 8959 Genomic selection for nitrogen use efficiency in wheat"Open access funding provided by University of Natural Resources and Life Sciences Vienna (BOKU). We like to thank Maria Burstmayr and her team for the tremendous work when extracting the DNA of several hundred wheat lines each year, Herbert Hetzendorfer for managing the collection of the phenotypic data, and Monika Opalo for screening the germplasm with regard to the Glu-1 loci. This research was funded by the EU Eurostars projects "E! 6399 Genomic selection of wheat varieties for robustness, yield and quality" and "E! 8959 Genomic selection for nitrogen use efficiency in wheat". We thank the anonymous reviewers for their valuable comments and suggestions for improving the manuscript

Improving the baking quality of bread wheat by genomic selection in early generations

Gungor, Huseyin/0000-0001-6708-6337; Epure, Doru-Gabriel/0000-0002-6209-2794; Buerstmayr, Hermann/0000-0002-0748-2351; Michel, Sebastian/0000-0001-6636-2694WOS: 000423400100017PubMed: 29063161Genomic selection shows great promise for pre-selecting lines with superior bread baking quality in early generations, 3 years ahead of labour-intensive, time-consuming, and costly quality analysis. The genetic improvement of baking quality is one of the grand challenges in wheat breeding as the assessment of the associated traits often involves time-consuming, labour-intensive, and costly testing forcing breeders to postpone sophisticated quality tests to the very last phases of variety development. The prospect of genomic selection for complex traits like grain yield has been shown in numerous studies, and might thus be also an interesting method to select for baking quality traits. Hence, we focused in this study on the accuracy of genomic selection for laborious and expensive to phenotype quality traits as well as its selection response in comparison with phenotypic selection. More than 400 genotyped wheat lines were, therefore, phenotyped for protein content, dough viscoelastic and mixing properties related to baking quality in multi-environment trials 2009-2016. The average prediction accuracy across three independent validation populations was r = 0.39 and could be increased to r = 0.47 by modelling major QTL as fixed effects as well as employing multi-trait prediction models, which resulted in an acceptable prediction accuracy for all dough rheological traits (r = 0.38-0.63). Genomic selection can furthermore be applied 2-3 years earlier than direct phenotypic selection, and the estimated selection response was nearly twice as high in comparison with indirect selection by protein content for baking quality related traits. This considerable advantage of genomic selection could accordingly support breeders in their selection decisions and aid in efficiently combining superior baking quality with grain yield in newly developed wheat varieties.University of Natural Resources and Life Sciences Vienna (BOKU); EUEuropean Union (EU) [E! 6399, E! 8959]Open access funding provided by University of Natural Resources and Life Sciences Vienna (BOKU). We like to thank Maria Burstmayr and her team for the tremendous work when extracting the DNA of several hundred wheat lines each year, Herbert Hetzendorfer for managing the collection of the phenotypic data, and Monika Opalo for screening the germplasm with regard to the Glu-1 loci. This research was funded by the EU Eurostars projects "E! 6399 Genomic selection of wheat varieties for robustness, yield and quality" and "E! 8959 Genomic selection for nitrogen use efficiency in wheat". We thank the anonymous reviewers for their valuable comments and suggestions for improving the manuscript

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Grausgruber, Heinrich/0000-0003-4790-0922; Gungor, Huseyin/0000-0001-6708-6337; Epure, Doru-Gabriel/0000-0002-6209-2794; Buerstmayr, Hermann/0000-0002-0748-2351; Michel, Sebastian/0000-0001-6636-2694WOS: 000393965900009PubMed: 27826661Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.University of Vienna; EUEuropean Union (EU)Open access funding provided by University of Vienna. We like to thank Maria Burstmayr and her team for the tremendous work when extracting the DNA of several hundred wheat lines each year as well as Herbert Hetzendorfer for managing the collection of the phenotypic data. This research was funded by the EU Eurostars projects "E! 6399 Genomic selection of wheat varieties for robustness, yield and quality" and "E! 8959 Genomic selection for nitrogen use efficiency in wheat". We thank the anonymous reviewers for their valuable comments and suggestions for improving the manuscript

Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism

Powdery mildews are phytopathogens whose growth and reproduction are entirely dependent on living plant cells. The molecular basis of this life-style, obligate biotrophy, remains unknown. We present the genome analysis of barley powdery mildew, Blumeria graminis f.sp. hordei (Blumeria), as well as a comparison with the analysis of two powdery mildews pathogenic on dicotyledonous plants. These genomes display massive retrotransposon proliferation, genome-size expansion, and gene losses. The missing genes encode enzymes of primary and secondary metabolism, carbohydrate-active enzymes, and transporters, probably reflecting their redundancy in an exclusively biotrophic life-style. Among the 248 candidate effectors of pathogenesis identified in the Blumeria genome, very few (less than 10) define a core set conserved in all three mildews, suggesting thatmost effectors represent species-specific adaptations