Cargando…

Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials

Plant breeding field trials are typically arranged as a row by column rectangular lattice. They have been widely analysed using linear mixed models in which low order autoregressive integrated moving average (ARIMA) time series models, and the subclass of separable lattice processes, are used to acc...

Descripción completa

Detalles Bibliográficos
Autores principales: Gogel, Beverley, Welham, Sue, Cullis, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987337/
https://www.ncbi.nlm.nih.gov/pubmed/36891132
http://dx.doi.org/10.3389/fpls.2022.1021143
_version_ 1784901362835783680
author Gogel, Beverley
Welham, Sue
Cullis, Brian
author_facet Gogel, Beverley
Welham, Sue
Cullis, Brian
author_sort Gogel, Beverley
collection PubMed
description Plant breeding field trials are typically arranged as a row by column rectangular lattice. They have been widely analysed using linear mixed models in which low order autoregressive integrated moving average (ARIMA) time series models, and the subclass of separable lattice processes, are used to account for two-dimensional spatial dependence between the plot errors. A separable first order autoregressive model has been shown to be particularly useful in the analysis of plant breeding trials. Recently, tensor product penalised splines (TPS) have been proposed to model two-dimensional smooth variation in field trial data. This represents a non-stochastic smoothing approach which is in contrast to the autoregressive (AR) approach which models a stochastic covariance structure between the lattice of errors. This paper compares the AR and TPS methods empirically for a large set of early generation plant breeding trials. Here, the fitted models include information on genetic relatedness among the entries being evaluated. This provides a more relevant framework for comparison than the assumption of independent genetic effects. Judged by Akaike Information Criteria (AIC), the AR models were a better fit than the TPS model for more than 80% of trials. In the cases where the TPS model provided a better fit it did so by only a small amount whereas the AR models made a substantial improvement across a range of trials. When the AR and TPS models differ, there can be marked differences in the ranking of genotypes between the two methods of analysis based on their predicted genetic effects. Using the best fitting model for a trial as the benchmark, the rate of mis-classification of entries for selection was greater for the TPS model than the AR models. This has important practical implications for breeder selection decisions.
format Online
Article
Text
id pubmed-9987337
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99873372023-03-07 Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials Gogel, Beverley Welham, Sue Cullis, Brian Front Plant Sci Plant Science Plant breeding field trials are typically arranged as a row by column rectangular lattice. They have been widely analysed using linear mixed models in which low order autoregressive integrated moving average (ARIMA) time series models, and the subclass of separable lattice processes, are used to account for two-dimensional spatial dependence between the plot errors. A separable first order autoregressive model has been shown to be particularly useful in the analysis of plant breeding trials. Recently, tensor product penalised splines (TPS) have been proposed to model two-dimensional smooth variation in field trial data. This represents a non-stochastic smoothing approach which is in contrast to the autoregressive (AR) approach which models a stochastic covariance structure between the lattice of errors. This paper compares the AR and TPS methods empirically for a large set of early generation plant breeding trials. Here, the fitted models include information on genetic relatedness among the entries being evaluated. This provides a more relevant framework for comparison than the assumption of independent genetic effects. Judged by Akaike Information Criteria (AIC), the AR models were a better fit than the TPS model for more than 80% of trials. In the cases where the TPS model provided a better fit it did so by only a small amount whereas the AR models made a substantial improvement across a range of trials. When the AR and TPS models differ, there can be marked differences in the ranking of genotypes between the two methods of analysis based on their predicted genetic effects. Using the best fitting model for a trial as the benchmark, the rate of mis-classification of entries for selection was greater for the TPS model than the AR models. This has important practical implications for breeder selection decisions. Frontiers Media S.A. 2023-01-18 /pmc/articles/PMC9987337/ /pubmed/36891132 http://dx.doi.org/10.3389/fpls.2022.1021143 Text en Copyright © 2023 Gogel, Welham and Cullis https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Gogel, Beverley
Welham, Sue
Cullis, Brian
Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title_full Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title_fullStr Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title_full_unstemmed Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title_short Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
title_sort empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9987337/
https://www.ncbi.nlm.nih.gov/pubmed/36891132
http://dx.doi.org/10.3389/fpls.2022.1021143
work_keys_str_mv AT gogelbeverley empiricalcomparisonoftimeseriesmodelsandtensorproductpenalisedsplinesformodellingspatialdependenceinplantbreedingfieldtrials
AT welhamsue empiricalcomparisonoftimeseriesmodelsandtensorproductpenalisedsplinesformodellingspatialdependenceinplantbreedingfieldtrials
AT cullisbrian empiricalcomparisonoftimeseriesmodelsandtensorproductpenalisedsplinesformodellingspatialdependenceinplantbreedingfieldtrials