Campus Units
Statistics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
7-26-2019
Journal or Book Title
arxiv
Abstract
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genome-wide association studies (GWAS), spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field (GRF) 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.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Mao, Xiaojun; Dutta, Somak; Wong, Raymond K. W.; and Nettleton, Dan, "Adjusting for Spatial Effects in Genomic Prediction" (2019). Statistics Publications. 254.
https://lib.dr.iastate.edu/stat_las_pubs/254
Included in
Applied Statistics Commons, Genomics Commons, Plant Sciences Commons, Statistical Models Commons
Comments
This is a pre-print made available through arxiv: https://arxiv.org/abs/1907.11581.