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Fusarium head blight (FHB) is a fungal disease of worldwide importance to small grain cereals that may lead to severe losses in both yield and quality. The development of resistant varieties is the most effective approach for managing the disease. Genetic variation for FHB resistance is large, including 'exotic' and 'native' wheat germplasm. Methods for selecting improved lines include: 1) phenotypic selection with direct symptom evaluation; 2) marker-assisted selection for well-characterized QTL and 3) genomic selection employing genome-wide prediction models. Breeding programs need to find the optimal deployment of the complementary approaches according to their available facilities, resources and requirements. This review aims to summarize recent advances in FHB resistance breeding, thereby discussing the importance of morphological traits like the extent of retained anthers after flowering, its suitability for indirect selection and the pronounced association of the semi-dwarfing allele Rht-D1b with increased anther retention and FHB severity. Markerassisted selection is successfully applied to select for largeeffect QTL, especially for the most prominent resistance QTL Fhb1 in bread wheat, as well as in durum wheat as recently demonstrated. The resistance locus Fhb1 has been partly elucidated, a pore-forming toxin-like gene confers resistance against fungal spread. Genomic selection for FHB resistance appears promising especially for breeding programs deploying 'native' resistance sources with many small-effect QTL.

Online communities have become popular for publishing and searching content, as well as for finding and connecting to other users. User-generated content includes, for example, personal blogs, bookmarks, and digital photos. These items can be annotated and rated by different users, and these social tags and derived user-specific scores can be leveraged for searching relevant content and discovering subjectively interesting items. Moreover, the relationships among users can also be taken into consideration for ranking search results, the intuition being that you trust the recommendations of your close friends more than those of your casual acquaintances.Queries for tag or keyword combinations that compute and rank the top-k results thus face a large variety of options that complicate the query processing and pose efficiency challenges. This paper addresses these issues by developing an incremental top-k algorithm with two-dimensional expansions: social expansion considers the strength of relations among users, and semantic expansion considers the relatedness of different tags. It presents a new algorithm, based on principles of threshold algorithms, by folding friends and related tags into the search space in an incremental on-demand manner. The excellent performance of the method is demonstrated by an experimental evaluation on three real-world datasets, crawled from deli.cio.us, Flickr, and LibraryThing.

five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of r GS = 0.51 for protein content, r GS = 0.38 for grain yield and r GS = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to r GS = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to r GS = 0.19 for this derived trait.

Key message Genomic 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. AbstractThe 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2998-x) contains supplementary material, which is available to authorized users.

Key message Early 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. AbstractThe 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-016-2818-8) contains supplementary material, which is available to authorized users.

Collection selection has been a research issue for years. Typically, in related work, precomputed statistics are employed in order to estimate the expected result quality of each collection, and subsequently the collections are ranked accordingly. Our thesis is that this simple approach is insufficient for several applications in which the collections typically overlap. This is the case, for example, for the collections built by autonomous peers crawling the web. We argue for the extension of existing quality measures using estimators of mutual overlap among collections and present experiments in which this combination outperforms CORI, a popular approach based on quality estimation. We outline our prototype implementation of a P2P web search engine, coined MINERVA 1 , that allows handling large amounts of data in a distributed and self-organizing manner. We conduct experiments which show that taking overlap into account during collection selection can drastically decrease the number of collections that have to be contacted in order to reach a satisfactory level of recall, which is a great step toward the feasibility of distributed web search.

Key message Genomic selection had a higher selection response for FHB resistance than phenotypic selection, while association mapping identified major QTL on chromosome 3B unaffected by plant height and flowering date. Abstract Fusarium head blight (FHB) is one of the most destructive diseases of durum wheat. Hence, minimizing losses in yield, quality and avoiding contamination with mycotoxins are of pivotal importance, as durum wheat is mostly used for human consumption. While growing resistant varieties is the most promising approach for controlling this fungal disease, FHB resistance breeding in durum wheat is hampered by the limited variation in the elite gene pool and difficulties in efficiently combining the numerous small-effect resistance quantitative trait loci (QTL) in the same line. We evaluated an international collection of 228 genotyped durum wheat cultivars for FHB resistance over 3 years to investigate the genetic architecture and potential of genomic-assisted breeding for FHB resistance in durum wheat. Plant height was strongly positively correlated with FHB resistance and led to co-localization of plant height and resistance QTL. Nevertheless, a major QTL on chromosome 3B independent of plant height was identified in the same chromosomal interval as reported for the prominent hexaploid resistance QTL Fhb1, though haplotype analysis highlighted the distinctiveness of both QTL. Comparison between phenotypic and genomic selection for FHB resistance revealed a superior prediction ability of the former. However, simulated selection experiments resulted in higher selection responses when using genomic breeding values for early generation selection. An earlier identification of the most promising lines and crossing parents was feasible with a genomic selection index, which suggested a much faster short-term population improvement than previously possible in durum wheat, complementing long-term strategies with exotic resistance donors.