A Comparison of Analysis Methods for Late‐stage Variety Evaluation Trials

Welham, S. J.; Gogel, Beverley J.; Smith, Alison; Thompson, R.; Cullis, Brian R

doi:10.1111/j.1467-842x.2010.00570.x

Cited by 88 publications

(105 citation statements)

References 19 publications

(47 reference statements)

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Section: Discussionmentioning

confidence: 99%

“…Mixed-model analysis can then be performed either on the individual plant (or plot) data or on genotypic means. In the literature on multi-environment trials (Smith et al 2001(Smith et al , 2005Oakey et al 2006;Piepho and Williams 2006;Piepho et al , 2012Boer et al 2007;Verbyla et al 2007;Stich et al 2008;Möhring and Piepho 2009;Van Eeuwijk et al 2010;Welham et al 2010;Malosetti et al 2013) these approaches are referred to as respectively one-stage and two-stage. These works consider mostly populations for which a pedigree is available, typically experimental populations.…”

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confidence: 99%

“…However, the possibility to include covariates observed at the individual plant level may be an important practical advantage. While two-stage procedures are usually considered preferable in complex multiexperiment settings (Welham et al 2010;Piepho et al 2012), a one-stage approach may give a more convenient and less error-prone analysis of a single experiment with a simple design.State-of-the-art phenotyping platforms can measure plant traits with increasing accuracy and throughput. Compared to human traits, the key advantages are that phenotyping is performed under experimental conditions and can include different individuals of the same genotype.…”

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Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within-and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.KEYWORDS marker-based estimation of heritability; GWAS; genomic prediction; Arabidopsis thaliana; one-vs. two-stage approaches N ARROW-SENSE heritability is an important parameter in quantitative genetics, determining the response to selection and representing the proportion of phenotypic variance that is due to additive genetic effects (Jacquard 1983;Ritland 1996;Visscher et al. 2006Visscher et al. , 2008Holland et al. 2010;Sillanpaa 2011). This definition of heritability goes back to Fisher (1918) and Wright (1920) almost a century ago. In plant species for which replicates of the same genotype are available (inbred lines, doubled haploids, clones), a different form of heritability, broadsense heritability, is traditionally estimated by the intraclass correlation coefficient for genotypic effects, using estimates for within-and between-genotype variance. Broad-sense heritability is also referred to as repeatability and gives the proportion of phenotypic variance explained by heritable (additive) and nonheritable (dominance, epistasis) genetic variance.With the arrival of high-density genotyping there is growing interest in marker-based estimation of narrow-sense heritability (WTCCC 2007;Yang et al. 2010Yang et al. , 2011Vatti...

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Section: Discussionmentioning

confidence: 99%

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Marker-Based Estimation of Heritability in Immortal Populations

et al. 2014

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“…There are two approaches to analysing MET data using mixed model, the so-called one-and twostage approaches (Welham et al, 2010). In a one-stage analysis, individual plot data from all trials are combined in a single analysis (Cullis et al, 1998).…”

Section: Statistical Tools For Model Selection and Test Of Consistencymentioning

confidence: 99%

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Negash

Mwambi

Zewotir

et al. 2014

Span. j. agric. res.

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The most common procedure for analyzing multi-environmental trials is based on the assumption that the residual error variance is homogenous across all locations considered. However, this may often be unrealistic, and therefore limit the accuracy of variety evaluation or the reliability of variety recommendations. The objectives of this study were to show the advantages of mixed models with spatial variance-covariance structures, and direct implications of model choice on the inference of varietal performance, ranking and testing based on two multi-environmental data sets from realistic national trials. A model comparison with a χ 2 -test for the trials in the two data sets (wheat data set BW00RVTI and barley data set BW01RVII) suggested that selected spatial variance-covariance structures fitted the data significantly better than the ANOVA model. The forms of optimally-fitted spatial variance-covariance, ranking and consistency ratio test were not the same from one trial (location) to the other. Linear mixed models with single stage analysis including spatial variance-covariance structure with a group factor of location on the random model also improved the real estimation of genotype effect and their ranking. The model also improved varietal performance estimation because of its capacity to handle additional sources of variation, location and genotype by location (environment) interaction variation and accommodating of local stationary trend.

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“…In the presence of a large number of environments and few genotypes, the increased number of estimates for required variance components can result in problems of convergence, loss of efficiency, and increased computational demand (Welham et al, 2010). In addition, poor estimation of the (co)variance components can significantly reduce the predictive ability of the UN structures, compared to diagonal models (Balestre et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Factor analysis using mixed models of multi-environment trials with different levels of unbalancing

Nuvunga¹,

Oliveira²,

Pamplona³

et al. 2015

Genet. Mol. Res.

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ABSTRACT.This study aimed to analyze the robustness of mixed models for the study of genotype-environment interactions (G x E). Simulated unbalancing of real data was used to determine if the method could predict missing genotypes and select stable genotypes. Data from multienvironment trials containing 55 maize hybrids, collected during the 2005-2006 harvest season, were used in this study. Analyses were performed in two steps: the variance components were estimated by restricted maximum likelihood, using the expectation-maximization (EM) algorithm, and factor analysis (FA) was used to calculate the factor scores and relative position of each genotype in the biplot. Random unbalancing of the data was performed by removing 10, 30, and 50% of the plots; the scores were then re-estimated using the FA model. It was observed that 10, 30, and 50% unbalancing exhibited mean correlation values of 0.7, 0.6, and 0.56, respectively. Overall, the genotypes classified as stable in the biplot had smaller prediction error sum of squares (PRESS) value and prediction 14263 Factor analysis using mixed models ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (4): 14262-14278 (2015) amplitude of ellipses. Therefore, our results revealed the applicability of the PRESS statistic to evaluate the performance of stable genotypes in the biplot. This result was confirmed by the sizes of the prediction ellipses, which were smaller for the stable genotypes. Therefore, mixed models can confidently be used to evaluate stability in plant breeding programs, even with highly unbalanced data.

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A Comparison of Analysis Methods for Late‐stage Variety Evaluation Trials

Cited by 88 publications

References 19 publications

Marker-Based Estimation of Heritability in Immortal Populations

Marker-Based Estimation of Heritability in Immortal Populations

Mixed model with spatial variance–covariance structure for accommodating of local stationary trend and its influence on multi-environmental crop variety trial assessment

Factor analysis using mixed models of multi-environment trials with different levels of unbalancing

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