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Theoretical and Applied Genetics

, Volume 127, Issue 3, pp 749–762 | Cite as

The impact of population structure on genomic prediction in stratified populations

  • Zhigang Guo
  • Dominic M. Tucker
  • Christopher J. Basten
  • Harish Gandhi
  • Elhan Ersoz
  • Baohong Guo
  • Zhanyou Xu
  • Daolong Wang
  • Gilles Gay
Original Paper

Abstract

Key message

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize.

Abstract

Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5 % of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65 % improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.

Keywords

Quantitative Trait Locus Population Structure Single Nucleotide Polymorphism Genetic Gain Genomic Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors of the current manuscript would like to thank researchers and institutions who contributed to the development of the rice and maize diversity panels. In addition, the authors would like to express gratitude to the editor and three anonymous reviewers for their detailed input in assessment and improvement of the manuscript.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

122_2013_2255_MOESM1_ESM.pdf (372 kb)
Supplementary material 1 (PDF 371 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhigang Guo
    • 1
  • Dominic M. Tucker
    • 2
  • Christopher J. Basten
    • 1
  • Harish Gandhi
    • 3
  • Elhan Ersoz
    • 4
  • Baohong Guo
    • 4
  • Zhanyou Xu
    • 4
  • Daolong Wang
    • 1
  • Gilles Gay
    • 1
  1. 1.Syngenta Biotechnology, Inc.DurhamUSA
  2. 2.Syngenta, Inc.ClintonUSA
  3. 3.Syngenta India Ltd.R.R. DistrictIndia
  4. 4.Syngenta, Inc.SlaterUSA

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