Random regression models for multi-environment, multi-time data from crop breeding selection trials
J. De Faveri
A B * , A. P. Verbyla A B and G. Rebetzke C
+ Author Affiliations
- Author Affiliations
A CSIRO Data61, Atherton, Qld 4883, Australia.
B UQ QAAFI, St Lucia, Qld 4072, Australia.
C CSIRO Agriculture & Food Black Mountain, Canberra, ACT 2601, Australia.
Crop & Pasture Science 74(4) 271-283 https://doi.org/10.1071/CP21732
Submitted: 4 November 2021 Accepted: 13 July 2022 Published: 31 August 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Context: In order to identify best crop genotypes for recommendation to breeders, and ultimately for use in breeding, evaluation is usually conducted in field trials across a range of environments, known as multi-environment trials. Increasingly, many breeding traits are measured over time, for example with high-throughput phenotyping at different growth stages in annual crops or repeated harvests in perennial crops.
Aims: This study aims to provide an efficient, accurate approach for modelling genotype response over time and across environments, accounting for non-genetic sources of variation such as spatial and temporal correlation.
Methods: Because the aim is genotype selection, genetic effects are fitted as random effects, and so the approach is based on random regression, in which linear or non-linear models are used to model genotype responses. A method for fitting random regression is outlined in a multi-environment situation, using underlying cubic smoothing splines to model the mean trend over time. This approach is illustrated on six wheat experiments, using data on grain-filling over thermal time.
Key results: The method correlates genetic effects over time and environments, providing predicted genotype responses while incorporating spatial and temporal correlation between observations.
Conclusions: The approach provides robust genotype predictions by accounting for temporal and spatial effects simultaneously under various situations including those in which trials have different measurement times or where genotypes within trials are not measured at the same times. The approach facilitates investigation into genotype by environment interaction (G × E) both within and across environments.
Implications: The models presented have potential to increase accuracy of predictions over measurement times and trials, provide predictions at times other than those observed, and give a greater understanding of G × E interaction, hence improving genotype selection across environments for repeated-measures traits.
Keywords: crop variety selection, cubic smoothing splines, linear mixed models, MET, MEMT, multi-environment trials, random regression models, statistical genetics.
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