The analysis of the NSW wheat variety database. I. Modelling trial error variance

Cullis, Brian R; Thomson, F.; Fisher, John Andrew; Gilmour, A.; Thompson, R.

doi:10.1007/bf00222947

Cited by 40 publications

(34 citation statements)

References 33 publications

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“…Mixed linear models were fitted with GenStat (16 th Edition, VSNi Ltd. UK), and planned comparisons at Bethungra and Leeton sites used run-range spatial models (Cullis et al 1996). Run (being the row into which hill plots were sown) and range (being the order of hill plots in the row) were treated as random factors and genotype as a fixed factor in the initial analysis of genotype means.…”

Section: Methodsmentioning

confidence: 99%

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

Wasson

Rebetzke

Kirkegaard

et al. 2014

Journal of Experimental Botany

137

116

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Section: Methodsmentioning

confidence: 99%

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

Wasson

Rebetzke

Kirkegaard

et al. 2014

Journal of Experimental Botany

137

116

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“…This paper focuses on two-stage analyses, because of the small differences with single stage analyses and the aforementioned larger handling ease. Still, good descriptions of single stage analyses are offered by Cullis et al (1996a,b), Gilmour et al (1997), and Smith et al (2005). In principle, the QTL mapping approach outlined later in this paper could also be embedded in a single stage analysis strategy.…”

Section: Generating Data To Study Genotype-by-environment Interactionmentioning

confidence: 99%

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Malosetti¹,

Ribaut²,

Eeuwijk³

2013

Front. Physiol.

409

442

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Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”

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“…The log function was chosen to link the g distributed plot error variances to the linear predictor (Cullis et al, 1996;Frensham et al, 1998). The log function was chosen to link the g distributed plot error variances to the linear predictor (Cullis et al, 1996;Frensham et al, 1998).…”

Section: Second Stagementioning

confidence: 99%

Estimating Broad‐Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials

Schmidt

Hartung

Rath³

et al. 2019

Crop Science

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525RESEARCH I n plant breeding programs and cultivar evaluation trials, cultivars (genotypes) of interest are often grown and tested at multiple locations across several years. Such a series of trials is called a multienvironment trial (MET), where a year-location combination is referred to as an environment. To quantify and eventually compare the precision of METs, plant breeders often calculate narrow-sense heritability (h 2 ) or broad-sense heritability (H 2 ) on a genotype-mean basis. The latter is defined as the proportion of phenotypic variance that is attributable to an overall variance for the genotype, thus including additive, dominance, and epistatic variance (Holland et al., 2003;Falconer and Mackay, 2005). Moreover, there are usually additional interpretations associated with H 2 : (i) it is equivalent to the coefficient of determination of a linear regression of the unobservable genotypic value on the observed phenotype, (ii) it is also the squared correlation between predicted phenotypic value and genotypic value, and (iii) it represents the proportion of the selection differential (S) that can be realized as the response to selection (R) (Falconer and Mackay, 2005). It is important to note that the necessity to estimate H 2 on a genotype-mean basis results from the fact that in plant breeding, genotypes are often tested across a wide range of environments in designed, replicated experiments. ABSTRACTBroad-sense heritability is defined as the proportion of phenotypic variance that is attributable to an overall variance for the genotype. It is often calculated as a measure (i) to quantify and eventually compare the precision of agricultural cultivar trials, and/or (ii) to estimate the response to selection in plant breeding trials. In practice, most such trials are conducted at multiple environments (i.e., year-location combinations) resulting in a multienvironment trial (MET) with unbalanced data, as, for example, not all cultivars are tested at each environment. However, the standard method for estimating heritability implicitly assumes balanced data, independent genotype effects, and homogeneous variances. Therefore, we compared the estimates for broad-sense heritability computed via the standard method to those obtained via six alternative estimation methods (example codes:https://github.com/PaulSchmidtGit/ Heritability). We did so by analyzing four cultivar METs, which all displayed a typically unbalanced data structure but differed in the genetic frameworks of their cultivars. Results indicate that the standard method may overestimate heritability for all datasets, whereas alternative methods show similar estimates per dataset and thus seem better able to handle this kind of unbalanced data. Finally, we show that to compare heritability estimates between different METs, genetic variance component estimates should be fixed to common values for both datasets.

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The analysis of the NSW wheat variety database. I. Modelling trial error variance

Cited by 40 publications

References 33 publications

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

Soil coring at multiple field environments can directly quantify variation in deep root traits to select wheat genotypes for breeding

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

Estimating Broad‐Sense Heritability with Unbalanced Data from Agricultural Cultivar Trials

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