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 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.

In recent years, mapping populations have provided improvements for oat genomic researches. A two-year study was conducted in East-Mediterranean conditions using Ogle1040/TAM O-301 pure-line mapping population including 136 individuals and parents. Stem diameter (SD), plant height (PH), panicle length (PL), vegetative period (VP), grain filling period (GFP), days to maturity (DM), grain number per panicle (GNP), grain weight per panicle (GWP), thousand kernel weight (TKW) and grain yield (GY) were investigated in 2014 and 2015 cropping seasons in Kahramanmaras. All the investigated traits were significantly different for years (p<0.01) and genotypes (p<0.05 and p<0.01) except SD and GNP. Genotype x year (G x Y) interaction was significant for PL, VP, GFP, DM and GY (p<0.01). In the first year, the average GY per row was 227.6 g, whilst it was 184.5 g in the second year. In terms of GY, the parents Ogle 1040 and TAM O-301 showed lower performance (154.5 and 111.5 g/row, respectively) compared to Ogle1040/TAM O-301 (OT) population average (206 g/row). OT129 genotype had the highest GY with 360 g/row. Principal component (PC) factor analysis yielded 10 PC explaining 100% of total variance in the data and the chi-square values of the PC1 to PC9 were found significant. According to PC biplot analysis, genotypes with high GY, TKW, GNP, GWP, PL and GFP were located throughout the right quadrants whereas the genotypes with high VP, DM and SD were located throughout the left quadrants. The relationships between PH × GY, GWP × GNP and GWP × TKW were positive and significant.

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Turkey is an important bread wheat producer. The objective of this study was to dissect the diversity of genetic, agronomic, and quality characteristics of bread wheat cultivars grown on 25% of the total wheat area in Turkey. A total of 24 wheat cultivars and 5 wild progenitors of wheat were examined using 24 simple sequence repeat (SSR) primers with a known physical locus on the A, B, and D genomes of hexaploid wheat. A total of 72 bands produced 939 alleles on the wheat cultivars and wild progenitors. Markers were efficient in discriminating the species and the highest genetic diversity information was obtained from the markers Xgwm312 and Xgwm372. Microsatellite markers clearly separated cv. Pandas from all other cultivars although it was closely related to most of them in terms of agronomic and quality traits. Four agronomic characteristics including yield component traits and eight bread quality analyses were used for the diversity analyses. A significant association between morphological and bread wheat quality traits was observed while the correlation was weak with the genetic data. Cultivars were also classified with respect to release year and origin. Molecular variance between old (released before the year 2000) and new cultivars accounted for 1% of the total variation and the variance was 3% between national and foreign cultivars. Results showed that the number of alleles was lower in national and new cultivars compared to foreign and old cultivars. Therefore, breeding sources do not appear to improve the genetic base of wheat cultivars in Turkey. Introducing new variation sources may be needed to broaden the narrowed gene pool of bread wheat.

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This research was carried out to evaluate the grain yield, yield traits and some quality characteristics of 18 bread wheat genotypes in seven different locations in Thrace region using principal component analysis and genotype + genotype × environment interaction (GGE) biplot analysis to determine the genotypes with high yield and desired quality characteristics during the 2016-2017 and 2017-2018 cropping years. The experiments were arranged in a randomized complete block design with four replications. Genotype, environment and genotype × environment interactions were found statistically significant at p≤0.01 level for all investigated traits. Mean values of the genotypes varied between 4841-6807 kg ha-1 for grain yield, 118.6-131.6 days for heading date, 80.4-104.7 cm for plant height, 7.7-10.4 cm for spike length, 16.4-20.3 for number of spikelets per spike, 16.4-20.3 number of grains per spike, 1.49-2.41 g grain weight per spike, 72-77.8 kg hl-1 for test weight and 36.6-45.3 g for thousand kernel weight. Principal component biplot analyzes explained the relationships between the investigated traits and genotypes at a ratio of 60.9%. It was observed that there was a positive and significant relationship between grain yield and test weight, a negative relationship with grain yield and spike length and grain weight per spike. GGE biplot analysis explained 82.65% of the relationship of genotype + genotype x environment for grain yield. According to the GGE biplot analysis two mega environments were determined and Lucilla and Glosa genotypes took place in the biggest mega environment consisted of four environments as superior genotypes.