Genomic selection in sugar beet breeding populations

Würschum et al. BMC Genetics 2013, 14:85 http://www.biomedcentral.com/1471-2156/14/85 RESEARCH ARTICLE Open Access Genomic selection in sugar beet breeding populations Tobias Würschum1*, Jochen C Reif1,3, Thomas Kraft2, Geert Janssen2,4 and Yusheng Zhao1,3 Abstract Background: Genomic selection exploits dense genome-wide marker data to predict breeding values. In this study we used a large sugar beet population of 924 lines representing different germplasm types present in breeding populations: unselected segregating families and diverse lines from more advanced stages of selection. All lines have been intensively phenotyped in multi-location field trials for six agronomically important traits and genotyped with 677 SNP markers. Results: We used ridge regression best linear unbiased prediction in combination with fivefold cross-validation and obtained high prediction accuracies for all except one trait. In addition, we investigated whether a calibration developed based on a training population composed of diverse lines is suited to predict the phenotypic performance within families. Our results show that the prediction accuracy is lower than that obtained within the diverse set of lines, but comparable to that obtained by cross-validation within the respective families. Conclusions: The results presented in this study suggest that a training population derived from intensively phenotyped and genotyped diverse lines from a breeding program does hold potential to build up robust calibration models for genomic selection. Taken together, our results indicate that genomic selection is a valuable tool and can thus complement the genomics toolbox in sugar beet breeding. Background Genomic selection has been suggested as a novel approach to increase selection gain in crop and livestock breeding programs [1-3]. Whereas QTL mapping strategies are based on the assumption that individual chromosomal regions can be identified that contribute to the trait and whose effects are estimated, genomic selection uses genome-wide marker data to estimate genomic breeding values of individuals. For plant breeding, genomic selection has been evaluated using empirical data from different crops, including maize e.g., [4-11], barley e.g., [12-14], wheat e.g., [5,15-17], as well as sugar beet [18]. Ridge regression best linear unbiased prediction (RRBLUP) [1,19] has been shown to provide high prediction accuracies across a range of crops and traits [14]. RRBLUP assumes that each marker contributes to the trait and has the same variance which is in accordance with the infinitisemal model of quantitative genetics and * Correspondence: 1 State Plant Breeding Institute, University of Hohenheim, 70593 Stuttgart, Germany Full list of author information is available at the end of the article explains why RR-BLUP provides good results for complex traits [20]. Genomic selection is based on linkage disequilibrium between markers and QTL affecting the trait. In addition, Habier et al. [21] showed that the accuracy of genomic selection depends on the exploitation of genetic relationships between individuals. RR-BLUP was most efficient in exploiting these genetic relationships since all available markers are used in the model. Plants within breeding programs will always show a certain degree of relatedness and in addition, most important agronomic traits are complex traits. This suggests that RR-BLUP should be well suited for genomic selection in applied plant breeding. Another major advantage of RR-BLUP is that it is computationally less demanding than other approaches. In genomic selection marker effects are first estimated based on a set of individuals which have been phenotyped and genotyped. This is often referred to as the training population. In a second step, the breeding values of individuals that have been genotyped but not phenotyped are predicted. It has been shown that the prediction accuracy decreases when the genetic relatedness between the © 2013 Würschum et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Würschum et al. BMC Genetics 2013, 14:85 http://www.biomedcentral.com/1471-2156/14/85 individuals in the training population and those in the prediction set decreases [21] and that high accuracies require that genotypes from the populations in which prediction will be done are represented in the training population [22]. In applied plant breeding programs different germplasm types are available: large biparental families from early generations which have not been selected yet and which are tested less intensively, and diverse lines from late generations that remained after several rounds of selection [23]. The latter are tested most intensively in field trials and are often also genotyped to characterize them at the molecular level. A key question for an efficient and costeffective implementation of genomic selection in breeding programs is therefore whether a calibration model developed based on a training population consisting of a diverse set of lines can be used for prediction of the phenotypic performance within segregating families. In this study we employed a large sugar beet population consisting of a panel of diverse lines and four segregating families to evaluate the potential of genomic selection for different yield- as well as quality-related traits in sugar beet and to investigate the prediction accuracy of genomic selection within families using a training population composed of a diverse set of lines. Results The population under study is composed of a total of 924 lines which can be divided into two subpopulations: 248 lines are derived from four biparental families that are connected by one common parent and 676 lines form a diversity set with different degrees of relatedness (Figure 1). All six traits showed significant genotypic variance estimates (P < 0.01) and medium to high heritabilities in the entire population (0.38 to 0.71) and in the diversity set (0.51 to 0.70) while across the four families the heritabilities ranged from 0.24 to 0.76 (Additional file 1: Table S1). Page 2 of 8 In single families the heritabilities ranged between 0.02 to 0.60. The Box-Whisker-Plots indicate significant differences among the four families for all traits (Figure 2). Consequently, the data set presents a good basis to evaluate the prospects of genomic selection in applied sugar beet breeding. We used fivefold cross-validation to assess the accuracy of genomic predictions for the six traits in the entire population and in the diversity set (Figure 3). We found that the cross-validated prediction accuracy was high for all traits except for α-amino nitrogen content in the entire population which showed only a moderate pr (...truncated)