Direct and Competition Additive Effects in Tree Breeding: Bayesian Estimation From an Individual Tree Mixed Model

Cappa, Eduardo P.; Cantet, Rodolfo Juan Carlos

doi:10.1515/sg-2008-0008

Cited by 59 publications

(96 citation statements)

References 32 publications

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“…In general, the strength of the interactions may vary among pairs of individuals,for example, because of variation in distance among plants or variation in group size in animals. To account for such effects in the statistical model, IGEs may be multiplied by factors (Muir, 2005;Cantet and Cappa, 2008;Cappa and Cantet, 2008;Bijma, 2010a; Costa e Silva et al, 2013),…”

Section: Interpreting the Magnitude Of Igesmentioning

confidence: 99%

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

2013

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Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient W (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient W may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.

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Section: Interpreting the Magnitude Of Igesmentioning

confidence: 99%

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

2013

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“…Recently, the competition effect between neighboring trees has been described in several papers (Cappa and Cantet 2008;Cappa et al 2015Cappa et al , 2016Dutkowski et al 2002;Dutkowski et al 2006;Ye and Jayawickrama 2008). Dutkowski et al (2006) reported that 10% of diameter variables showed competition effects, represented by negative first-order autocorrelation coefficients in both column and row directions at a residual level.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

Chen

Helmersson

Westin

et al. 2017

Annals of Forest Science

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Key message Spatial analysis could improve the accuracy of genetic analyses, as well as increasing the accuracy of predicting breeding values and genetic gain for Norway spruce trials. Context Spatial analysis has been increasingly used in genetic evaluation of field trials in tree species. However, the efficiency of spatial analysis relative to the analysis using the conventional experimental designs or pre- and post-blocking method in Swedish genetic trials has not been systematically evaluated. Aims This study aims to examine the effectiveness of spatial analysis in improving the accuracy of predicting breeding values and genetic gain. Methods Spatial analysis, using separable first-order autoregressive processes of residuals in rows and columns, was used in nine types of trait classes from 145 field trials of Norway spruce (Picea abies (L.) Karst.) in Sweden. Results Ninety-six percent of variables (traits) were converged for the spatial model. Large trials with a large block variance tend to have a larger improvement from the model of experimental design to spatial model in accuracy. Growth and Pilodyn measurement traits showed greater improvements in log likelihood, accuracy, and genetic gain. Block variance was reduced by more than 80% for trait height and diameter using spatial analysis, indicating that it is more effective using both pre-blocking and post-blocking analyses in Swedish Norway spruce trials. The prediction accuracy for diameter and height for progeny breeding values showed an increase of 3.6 and 3.4%, respectively. The improvement of efficiency for growth traits is also related to the geographical location of test sites, tree age, number of survival trees, and the spacing of the trial. Conclusion The spatial analysis approach is more efficient in Swedish Norway spruce trials than the conventional methods using models based on the experimental design.

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“…For example, a growing recognition that observed phenotypes can be under shared genetic control is providing new insights into the evolution of social traits, including aggression (Moore et al, 2002;Wilson et al, 2009a), parental care (Kolliker et al, 2005) and cooperation (McGlothlin et al, 2010). In a very different context, incorporation of IGEs into artificial selection schemes offers the potential to improve target traits (Bergsma et al, 2008;Cappa, 2008) while simultaneously improving welfare in livestock species by reducing the expression of behavioural aggression (Muir and Craig, 1998;Ellen et al, 2007). Here I pose the question: are IGEs an important source of evolutionary constraint for resourcedependent traits in natural populations?…”

Section: Introductionmentioning

confidence: 99%

Competition as a source of constraint on life history evolution in natural populations

Wilson

2013

Heredity

128

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Competition among individuals is central to our understanding of ecology and population dynamics. However, it could also have major implications for the evolution of resource-dependent life history traits (for example, growth, fecundity) that are important determinants of fitness in natural populations. This is because when competition occurs, the phenotype of each individual will be causally influenced by the phenotypes, and so the genotypes, of competitors. Theory tells us that indirect genetic effects arising from competitive interactions will give rise to the phenomenon of 'evolutionary environmental deterioration', and act as a source of evolutionary constraint on resource-dependent traits under natural selection. However, just how important this constraint is remains an unanswered question. This article seeks to stimulate empirical research in this area, first highlighting some patterns emerging from life history studies that are consistent with a competition-based model of evolutionary constraint, before describing several quantitative modelling strategies that could be usefully applied. A recurrent theme is that rigorous quantification of a competition's impact on life history evolution will require an understanding of the causal pathways and behavioural processes by which genetic (co)variance structures arise. Knowledge of the G-matrix among life history traits is not, in and of itself, sufficient to identify the constraints caused by competition.

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Direct and Competition Additive Effects in Tree Breeding: Bayesian Estimation From an Individual Tree Mixed Model

Cited by 59 publications

References 32 publications

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

The quantitative genetics of indirect genetic effects: a selective review of modelling issues

Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

Competition as a source of constraint on life history evolution in natural populations

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