Two pigmented wheat genotypes (blue and purple) and two black barley genotypes were fractionated in bran and flour fractions, examined, and compared for their free radical scavenging properties against 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical cation (Trolox equivalent antioxidant capacity, TEAC), ferric reducing antioxidant power (FRAP), total phenolic content (TPC), phenolic acid composition, carotenoid composition, and total anthocyanin content. The results showed that fractionation has a significant influence on the antioxidant properties, TPC, anthocyanin and carotenoid contents, and phenolic acid composition. Bran fractions had the greatest antioxidant activities (1.9-2.3 mmol TEAC/100 g) in all four grain genotypes and were 3-5-fold higher than the respective flour fractions (0.4-0.7 mmol TEAC/100 g). Ferulic acid was the predominant phenolic acid in wheat genotypes (bran fractions) while p-coumaric acid was the predominant phenolic acid in the bran fractions of barley genotypes. High-performance liquid chromatography analysis detected the presence of lutein and zeaxanthin in all fractions with different distribution patterns within the genotypes. The highest contents of anthocyanins were found in the middlings of black barley genotypes or in the shorts of blue and purple wheat. These data suggest the possibility to improve the antioxidant release from cereal-based food through selection of postharvest treatments.

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.

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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.