Correcting for spatial heterogeneity in plant breeding experiments with P-splines

Rodríguez‐Álvarez, María Xosé; Boer, Martin P.; Eeuwijk, Fred van; Eilers, Paul H.C.

doi:10.1016/j.spasta.2017.10.003

Cited by 214 publications

(284 citation statements)

References 39 publications

Supporting

Mentioning

263

Contrasting

Unclassified

Order By: Relevance

“…Oakey's Method [29] propose a generalized heritability measure that was recently re-expressed by [40] as…”

Section: Methodsmentioning

confidence: 99%

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

Lourenço

Ogutu

Piepho

2019

Preprint

View full text Add to dashboard Cite

Background. Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. Results. The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. Conclusions. The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction while alleviating the need for performing outlier detection for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.

show abstract

“…Oakey's Method [29] propose a generalized heritability measure that was recently re-expressed by [40] as…”

Section: Methodsmentioning

confidence: 99%

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

Lourenço

Ogutu

Piepho

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…We used parts of the wheat field trial data presented in Lado et al (2013) and used by Rodríguez-Álvarez et al (2018) as shown in Fig. 1.…”

Section: Chilean Wheat Datamentioning

confidence: 99%

“…The experimental design was an alpha design with two replicates. The trial had 40 rows and 20 columns and according to Rodríguez-Álvarez et al (2018) the replicates were placed such that the first/second 20 rows corresponded to the first/second replicate. This is indicated by the horizontal line in Fig.…”

Section: Chilean Wheat Datamentioning

confidence: 99%

“…One of the most widely used is the separable first-order autoregressive (AR1⊗AR1) model introduced by Cullis and Gleeson (1991) and extended by Gilmour et al (1997). It has been shown to fit well in many trials (e.g., Gilmour et al, 1997;Rodríguez-Álvarez et al, 2018). There are other models that can correct for spatial variation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, there is a whole class of Gaussian intrinsic models based on the seminal work of Besag and Higdon (1999), which have not gained much traction in plant breeding applications. Much has also been done on smoothing techniques, among which the recent SpATS approach explores two-dimensional smooth surfaces through the use of tensor product P-splines (Rodríguez-Álvarez et al, 2018). Nearest neighbour models are reviewed by Piepho et al (2008) and the use of spatial kernels is also common (Elias et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling spatial variation in agricultural field trials with INLA

Steinsland

2019

Preprint

View full text Add to dashboard Cite

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. AbstractThe 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. Here 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 wellknown independent row and column effects, separable first-order autoregressive (AR1⊗AR1) models and a geostatistical model using the stochastic partial differential equation (SPDE) approach. The SPDE approach models a Gaussian random field, which 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 1 markers, and with combining data over two locations, modelling spatial and genetic effects jointly. We evaluate predictive performance by correlation between true and estimated breeding values, the continuous rank probability score and how often the best individuals rank at the top. The results show best predictive performance with the AR1⊗AR1 and the SPDE. We also present an example of fitting the models to real wheat breeding data and simulated tree breeding data with the Nelder wheel design. Keywords field trial spatial variation Bayesian INLA SPDE

show abstract