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Adjusting for Spatial Effects in Genomic Prediction

Author

Listed:
  • Xiaojun Mao

    (Fudan University)

  • Somak Dutta

    (Iowa State University)

  • Raymond K. W. Wong

    (Texas A&M University)

  • Dan Nettleton

    (Iowa State University)

Abstract

This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field model with an additive covariance structure that incorporates genotype effects, spatial effects and subpopulation effects. An empirical study shows the existence of spatial effects and heterogeneity across different subpopulation families, while simulations illustrate the improvement in selecting genotypically superior plants by adjusting for spatial effects in genomic prediction.

Suggested Citation

  • Xiaojun Mao & Somak Dutta & Raymond K. W. Wong & Dan Nettleton, 2020. "Adjusting for Spatial Effects in Genomic Prediction," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 699-718, December.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:4:d:10.1007_s13253-020-00396-1
    DOI: 10.1007/s13253-020-00396-1
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    References listed on IDEAS

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    2. Arũnas P. Verbyla & Brian R. Cullis & Michael G. Kenward & Sue J. Welham, 1999. "The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 269-311.
    3. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
    4. Somak Dutta & Debashis Mondal, 2015. "An h-likelihood method for spatial mixed linear models based on intrinsic auto-regressions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(3), pages 699-726, June.
    5. J. Besag & D. Higdon, 1999. "Bayesian analysis of agricultural field experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 691-746.
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    Cited by:

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    2. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Salihoğlu, Tayfun & Albayrak, Ayşe Nur & Eryılmaz, Yaşasın, 2021. "A method for the determination of urban transformation areas in Kocaeli," Land Use Policy, Elsevier, vol. 109(C).

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