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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The performance of interspecific and intraspecific cotton hybrid populations were compared to investigate the correlation among the F2, F3 and F4 bulk generations in terms of yield, lint percentage, and fiber quality from 2009 to 2011. In addition, the effect of combining abilities on hybrid performance were investigated at further generations. For this purpose, nine interspecific and six intraspecific hybrids with eight parents were evaluated under the west part of Turkey (Aegean region) environmental conditions. With the generation progresses, the highest drops in fiber length and fiber strength were detected in interspecific due to the inbreeding depression and genetic breakdown in advanced populations. Based on 15 tested hybrids, the observed mean yield and fiber quality in the F2 do not adequately predict the performance of hybrids in the F4 generation. Thus, selection of individual plants should be delayed until the F4 generation especially for interspecific hybrids, but individual plants would be selected at early generation in intraspecific hybrid populations. In the study it was found that a higher general combining ability (GCA) does not necessarily confer a higher specific combining ability (SCA) and that the GCA and SCA were independent of one another, and that in F4 high yielded hybrid population was obtained from cross which at least one parent of hybrid has maximum positive GCA effects. These results indicated that instead of using only one criteria, the F2 performance for intraspecific hybrids and the F3 performance for interspecific hybrids, low heterosis and inbreeding depression, combining ability of parents could be used together to determine the most promising hybrid populations to be used as a source for further selection.