Comparison of a one- and two-stage mixed model analysis of Australia’s National Variety Trial Southern Region wheat data

Gogel, Beverley J.; Smith, Alison; Cullis, Brian R

doi:10.1007/s10681-018-2116-4

Cited by 44 publications

(73 citation statements)

References 11 publications

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“…Following Gogel et al. (), model selection was achieved using a combination of formal and informal tools, with emphasis on the former. Formal assessment of model fit used the Akaike Information Criteria (AIC).…”

Section: Statistical Modelsmentioning

confidence: 99%

“…Gogel, Smith, and Cullis () and Welham, Gogel, Smith, Thompson, and Cullis () have shown that stagewise approaches for the analysis of MET data can lead to a loss of efficiency in the prediction of the nested variety effects within environments. Furthermore, many of these approaches fail to adequately model variety (and hence marker) by environment interaction by either choosing to only fit simple marker main effects across environments or using inappropriate variance models, including the compound symmetric, for the marker by environment interaction effects (see for example, Bentley et al., ; Cros et al., ; Würschum, Reif, Kraft, Janssen, & Zhao, ).…”

Section: Introductionmentioning

confidence: 99%

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Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Tolhurst

Mathews

Smith

et al. 2019

J Animal Breeding Genetics

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Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi‐environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single‐step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non‐genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.

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Section: Statistical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Tolhurst

Mathews

Smith

et al. 2019

J Animal Breeding Genetics

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show abstract

“…The models discussed thus far have numerous deficiencies. Some key issues are that they involve piecemeal approaches (typically first requiring analyses of individual trials to obtain variety by evironment means for use as data in a subsequent analysis) so are inherently inefficient (Welham et al 2010;Gogel et al 2018); most require balanced data, that is, all varieties in all environments; they assume variety effects to be fixed rather than random (see Smith et al 2005, for a discussion), which has particular limitations for our example since it is not possible to include pedigree information and finally, they rarely provide a good fit to the data.…”

Section: Introductionmentioning

confidence: 99%

“…The FA structure has been found to perform extremely well in terms of providing a good fit to the data and a parsimonious model for VEI (Kelly et al 2007). The success of the approach for plant breeding programmes has also led to its adoption within the Australian National Variety Trials (NVT) system (Smith et al 2015;Gogel et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data

2018

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An advanced and widely used method of analysis for multi-environment trial data involves a linear mixed model with factor analytic (FA) variance structures for the variety by environment effects. This model can accommodate unbalanced data, that is, not all varieties in all environments, it allows the use of pedigree information and appropriate accommodation of individual trial experimental designs, and most importantly the FA structure for the variety by environment effects is parsimonious and regularly results in a good fit to the data. The model provides accurate predictions of the variety effects for every environment in the data-set but this constitutes a large and unwieldly amount of information to process for the purpose of variety selection. We address this issue in the current paper by proposing factor analytic selection tools to summarise the predictions in a concise yet informative manner. The tools, which are natural derivatives of the FA structure, result in measures of overall performance and stability across the environments in the data-set. All measures are expressed on the same scale as the trait under consideration and can easily be combined to form an index for selection.

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“…See, for example, Cullis, Jefferson, Thompson, and Smith (); Smith, Borg, Gogel, and Cullis () Smith, Cullis, and Thompson (), Smith, Cullis, and Thompson (); Smith and Cullis (); Welham, Gogel, Smith, Thompson, and Cullis (); Smith, Butler, Cavanagh, and Cullis (), Smith, Thompson, Butler, and Cullis () Bev Gogel: an early version of the single step MET analyses used for NVT and co‐developer of the prediction algorithm in ASReml. See, for example, Butler, Gogel, Cullis, and Thompson ();Gilmour, Cullis, Welham, Gogel, and Thompson (); Gilmour, Cullis, Welham, Gogel, and Thompson (); Gogel, Smith, and Cullis (); Welham et al (); Welham, Cullis, Gogel, Gilmour, and Thompson () Dave Butler: ASReml‐R and numerous other statistical software packages, including a design system for plant breeders which can incorporate genetic relationships. See, for example, Butler, Cullis, Gilmour, and Thompson (), Butler, Cullis, Gilmour, and Gogel (); Butler, Smith, and Cullis (); Butler, Tan, and Cullis (); Smith, Butler, Cavanagh, and Cullis (); Neil Coombes: a novel model‐based experimental design package called DiGGeR.…”

Section: Brian Cullis: Cmajor‐drinkwater—international Collaboration mentioning

confidence: 99%

Students’, colleagues’ and research partners’ experience about work and accomplishments from collaborating with Robin Thompson

Hickey

Hill

Blasco

et al. 2019

J Animal Breeding Genetics

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Comparison of a one- and two-stage mixed model analysis of Australia’s National Variety Trial Southern Region wheat data

Cited by 44 publications

References 11 publications

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Plant breeding selection tools built on factor analytic mixed models for multi-environment trial data

Students’, colleagues’ and research partners’ experience about work and accomplishments from collaborating with Robin Thompson

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