Adjusting for Spatial Effects in Genomic Prediction

Xiaojun Mao, Somak Dutta (), Raymond K. W. Wong and Dan Nettleton
Additional contact information
Xiaojun Mao: Fudan University
Somak Dutta: Iowa State University
Raymond K. W. Wong: Texas A&M University
Dan Nettleton: Iowa State University

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 4, No 13, 699-718

Abstract: 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.

Keywords: Gaussian random field; Genomic prediction; Spatial effects; Subpopulation effects (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13253-020-00396-1

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