Flexible modelling of spatial variation in agricultural field trials with the R package INLA

Maria Lie Selle, Ingelin Steinsland, John Hickey, Gregor Gorjanc

Research output: Contribution to journalArticlepeer-review

Abstract

Key message Established spatial models improve the analysis of agricultural field trials with or without genomic data and can be fitted with the open-source R package INLA.

Abstract The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the Integrated Nested Laplace Approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive (AR1⊗AR1) models and a Gaussian random field (Mate ́rn) model that is approximated via the stochastic partial differential equation (SPDE) approach. The Mate ́rn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding program with different levels of spatial variation, with and without genome-wide markers, and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the AR1⊗AR1 and the Mate ́rn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Mate ́rn model and the R package INLA.
Original languageEnglish
JournalTAG Theoretical and Applied Genetics
Early online date18 Sep 2019
DOIs
Publication statusE-pub ahead of print - 18 Sep 2019

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