Adjusting for Spatial Effects in Genomic Prediction
Applications
2020-06-09 v2
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.
Cite
@article{arxiv.1907.11581,
title = {Adjusting for Spatial Effects in Genomic Prediction},
author = {Xiaojun Mao and Somak Dutta and Raymond K. W. Wong and Dan Nettleton},
journal= {arXiv preprint arXiv:1907.11581},
year = {2020}
}
Comments
22 pages, 6 figures, 10 tables