Dataset Information

Genomic selection in sugar beet breeding populations.

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.

SUBMITTER: Wurschum T

PROVIDER: S-EPMC3848454 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

Genomic selection in sugar beet breeding populations.

Würschum Tobias T Reif Jochen C JC Kraft Thomas T Janssen Geert G Zhao Yusheng Y

BMC genetics 20130918

<h4>Background</h4>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.<h4>Results</h4>We us ...[more]

PMID: 24047500

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Project description:Key messageWe evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across 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 [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 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 [Formula: see text] = 0.19 for this derived trait.

Project description:Productivity of sugar beet and brassica vegetable crops is constrained by the nematode Heterodera schachtii worldwide. In sugar beet cropping areas of Central Europe and North America, H. schachtii is managed by crop rotation, and cultivation of resistant brassica cover crops. The recently released nematode-tolerant sugar beet cultivars suffer less damage than susceptible cultivars at high initial population densities of H. schachtii. Many tolerant cultivars allow for less nematode reproduction than susceptible cultivars. Monoculture of susceptible hosts can facilitate the evolution of suppressive soil. Objectives of this study were to determine if susceptible hosts are required for this process, and if monoculture with sugar beet genotypes of different host status (susceptible, resistant, tolerant) impact this capacity. Additionally, we tested if amending soil with the cyst nematode pathogens Pasteuria nishizawae or Hyalorbilia sp. strain DoUCR50 favored the establishment of soil suppressiveness. In 4-year microplot studies with H. schachtii Schach0 or Schach1, one susceptible, one Schach0-resistant, and one tolerant sugar beet genotype were monocultured. In 2010, plots were amended with P. nishizawae or DoUCR50, the last being introduced into non-treated soil for Schach0, and into previously biocide-treated soil for Schach1. In 2011, respective Schach0 plots received a second amendment with DoUCR50. Nematode population densities and growth and yield parameters were determined annually. Effects of P. nishizawae and DoUCR50 on populations of H. schachtii were limited and not consistent. Starting in the second year of the monoculture, eggs of both H. schachtii pathotypes became diseased. Up to 90% of the total eggs were encumbered by the third cropping cycle, under the susceptible, resistant, and tolerant cultivar. In all years, the tolerant genotype produced the highest and most stable white sugar yields while yields of the other cultivars slowly improved during the monoculture. Results of this study suggested the presence of egg-infecting factors in this sugar beet monoculture that dramatically increased the proportions of diseased eggs. The tolerant cultivar allowed establishment of soil suppressiveness without the initial yield decline observed when susceptible sugar beet genotypes are grown in monoculture.

Dataset Information

Genomic selection in sugar beet breeding populations.

Background

Results

Conclusions

Publications

Genomic selection in sugar beet breeding populations.

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