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Genomic selection in a commercial winter wheat population

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Abstract

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Genomic selection models can be trained using historical data and filtering genotypes based on phenotyping intensity and reliability criterion are able to increase the prediction ability.

Abstract

We implemented genomic selection based on a large commercial population incorporating 2325 European winter wheat lines. Our objectives were (1) to study whether modeling epistasis besides additive genetic effects results in enhancement on prediction ability of genomic selection, (2) to assess prediction ability when training population comprised historical or less-intensively phenotyped lines, and (3) to explore the prediction ability in subpopulations selected based on the reliability criterion. We found a 5 % increase in prediction ability when shifting from additive to additive plus epistatic effects models. In addition, only a marginal loss from 0.65 to 0.50 in accuracy was observed using the data collected from 1 year to predict genotypes of the following year, revealing that stable genomic selection models can be accurately calibrated to predict subsequent breeding stages. Moreover, prediction ability was maximized when the genotypes evaluated in a single location were excluded from the training set but subsequently decreased again when the phenotyping intensity was increased above two locations, suggesting that the update of the training population should be performed considering all the selected genotypes but excluding those evaluated in a single location. The genomic prediction ability was substantially higher in subpopulations selected based on the reliability criterion, indicating that phenotypic selection for highly reliable individuals could be directly replaced by applying genomic selection to them. We empirically conclude that there is a high potential to assist commercial wheat breeding programs employing genomic selection approaches.

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Authors and Affiliations

  1. Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, Gatersleben, 06466, Stadt Seeland, Germany

    Sang He, Albert Wilhelm Schulthess, Vilson Mirdita, Yusheng Zhao, Jochen C. Reif & Yong Jiang

  2. KWS Lochow GmbH, Bergen, Germany

    Viktor Korzun, Reiner Bothe & Erhard Ebmeyer

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  1. Sang He
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  2. Albert Wilhelm Schulthess
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  3. Vilson Mirdita
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  4. Yusheng Zhao
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  5. Viktor Korzun
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  6. Reiner Bothe
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  8. Jochen C. Reif
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  9. Yong Jiang
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Correspondence to Jochen C. Reif.

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Communicated by H. Iwata.

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122_2015_2655_MOESM1_ESM.jpg

Supplementary Fig. S1. Principal coordinate analysis based on pairwise Rogers’ distances among the 2,325 winter wheat inbred lines. Percentages in brackets indicate the variance contribution of corresponding principal coordinates. (JPEG 6220 kb)

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Supplementary Fig. S2. Distribution of the Rogers’ distances among all pairs of the 2,325 wheat inbred lines. (JPEG 1141 kb)

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Supplementary Fig. S3. Trace plots of residual variance of genomic models based on Bayesian approach (EGBLUP, RKHS and BayesCπ). (JPEG 5045 kb)

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Supplementary Fig. S4. Relationship between the selection accuracy (square root of the broad-sense heritability, h) for grain yield and the number of environments evaluated (combining 2012 and 2013 years data).(JPEG 874 kb)

122_2015_2655_MOESM5_ESM.jpg

Supplementary Fig. S5. Distribution of Best Linear Unbiased Estimates (BLUEs Mg ha−1) of the 2,325 wheat lines evaluated for grain yield as well as linear regression of grain yield on the average number of test environments.(JPEG 1142 kb)

Supplementary Table S1. Evaluation of grain yield in different environments (location times year) (PDF 10 kb)

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He, S., Schulthess, A.W., Mirdita, V. et al. Genomic selection in a commercial winter wheat population. Theor Appl Genet 129, 641–651 (2016). https://doi.org/10.1007/s00122-015-2655-1

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