# Adjusting for Spatial Effects in Genomic Prediction

## TL;DR

This paper explores how accounting for spatial effects in genomic prediction improves the accuracy of selecting superior plant genotypes, highlighting the importance of spatial adjustments in phenotypic data analysis.

## Contribution

It introduces a Gaussian random field model that incorporates spatial, genotype, and subpopulation effects for more accurate genomic prediction.

## Key findings

- Spatial effects significantly influence phenotypic measurements.
- Adjusting for spatial effects improves plant selection accuracy.
- Heterogeneity exists across subpopulations in spatial effects.

## 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.