Estimation of macro- and micro-genetic environmental sensitivity in unbalanced datasets

Madsen, M.D.; Werf, Julius van der; Börner, Vinzent; Mulder, Henk; Clark, Sam

doi:10.1016/j.animal.2021.100411

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…For the dispersion part of the models, computation of the additive genetic variance had to consider the fact that ¾ of the additive genetic variance was included in the residual variance of the mean part of the model, thus contributing to the calculated dispersion phenotype, and was calculated for subpopulation j following Madsen et al [ 19 ] as: …”

Section: Methodsmentioning

confidence: 99%

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

Madsen,

Duijvesteijn,

van der Werf

et al. 2023

Genet Sel Evol

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Background Commercial poultry production systems follow a pyramidal structure with a nucleus of purebred animals under controlled conditions at the top and crossbred animals under commercial production conditions at the bottom. Genetic correlations between the same phenotypes on nucleus and production animals can therefore be influenced by differences both in purebred-crossbred genotypes and in genotype-by-environment interactions across the two environments, known as macro-genetic environmental sensitivity (GES). Within each environment, genotype-by-environment interactions can also occur due to so-called micro-GES. Micro-GES causes heritable variation in phenotypes and decreases uniformity. In this study, genetic variances of body weight (BW) and of micro-GES of BW and the impacts of purebred-crossbred differences and macro-environmental differences on micro-GES of BW were estimated. The dataset contained three subpopulations of slow-growing broiler chickens: purebred chickens (PB) reared in France, and crossbred chickens reared in France (FR) under the same conditions as PB or reared in Burkina Faso (BF) under local conditions. The crossbred chickens were offspring of the same dam line and had PB as their sire line. Results Estimates of heritability of BW and micro-GES of BW were 0.54 (SE of 0.02) and 0.06 (0.01), 0.67 (0.03) and 0.03 (0.01), and 0.68 (0.04) and 0.02 (0.01) for the BF, FR, and PB subpopulations, respectively. Estimates of the genetic correlations for BW between the three subpopulations were moderately positive (0.37 to 0.53) and those for micro-GES were weakly to moderately positive (0.01 to 0.44). Conclusions The results show that the heritability of the micro-GES of BW varies with macro-environment, which indicates that responses to selection are expected to differ between macro-environments. The weak to moderate positive genetic correlations between subpopulations indicate that both macro-environmental differences and purebred-crossbred differences can cause re-ranking of sires based on their estimated breeding values for micro-GES of BW. Thus, the sire that produces the most variable progeny in one macro-environment may not be the one that produces the most variable offspring in another. Similarly, the sire that produces the most variable purebred progeny may not produce the most variable crossbred progeny. The results highlight the need for investigating micro-GES for all subpopulations included in the selection scheme, to ensure optimal genetic gain in all subpopulations.

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Section: Methodsmentioning

confidence: 99%

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

Madsen,

Duijvesteijn,

van der Werf

et al. 2023

Genet Sel Evol

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“…The spatial scale of environmental variation can extend from the individual animal (micro-environment) to the level of the cohort, farm, or geographic region (macro-environment; Strandberg 2009). Heritable variation in the stability of trait expression may differ between micro-and macro-environmental scales D www.publish.csiro.au/an Animal Production Science (Madsen et al 2021). Nonetheless, a degree of arbitrariness exists in drawing a distinction between robustness (macroenvironmental scale) and resilience (micro-environmental scale) (Friggens et al 2017(Friggens et al , 2021.…”

Section: Physiological Regulation Of Trait Expressionmentioning

confidence: 99%

Competence to thrive: resilience as an indicator of positive health and positive welfare in animals

Colditz

2022

Anim. Prod. Sci.

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A capacity for an animal to maintain or regain healthy functioning in the face of environmental disturbances is recognised as resilience. For the individual animal, dynamic properties of the trajectory of performance traits (e.g. daily milk yield), periodicity of physiological and behavioural variables (e.g. body temperature) and complexity of biological functions (e.g. behavioural repertoire) can provide indicators of its resilience. These indicators provide a view of the individual's attempts to cope physically (and emotionally) with its non-shared micro-environment. The concept of resilience is examined as a multifaceted attribute that is hidden from direct measurement. The challenges for establishing relationships between general resilience and context-specific resilience such as disease resilience are explored by comparison with the multifaceted construct of temperament (personality). Lower variance in deviations from performance trajectories and stronger periodicity in rhythmic physiological and behavioural variables are mostly heritable and favourably associated with health and longevity. In humans, wellbeing is associated with health outcomes, and biomarkers of health are more strongly associated with eudaimonic than hedonic wellbeing. The psychological state of eudaimonia is associated with the capacity to express agency, function well, fulfill biological potential and express environmental mastery. The need for indicators of eudaimonia in animals has been suggested previously. Links between environmental mastery and the capacity to develop and express physiological, immune, behavioural, cognitive and affective competencies are examined. It is suggested that longitudinal data on individual animals can provide a precision physical indicator of eudaimonic positive welfare. In this view, positive welfare entails both eudaimonic and hedonic aspects of wellbeing, neither of which is alone sufficient to describe or to maximise positive welfare. Cumulative scores of resilience may have utility for assessing the lifetime welfare experience of the individual and when summed at the farm level may provide a metric for benchmarking welfare performance. Breeding for resilience selects for normativity of biological functions assessed against the individual animal's own baseline of inherited and developmentally acquired potential. This differs from current approaches to selection for production, which aim to maximise production or production efficiency benchmarked against the group mean. Biological costs and trade-offs for resilience require further research.

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“…(2010) and Felleki et al. (2012) have been successfully applied since then and have been further extended to include genomic information and macro-environmental sensitivity, among other applications ( Mulder et al., 2013a ; Mulder et al., 2013b ; Iung et al., 2017 ; Ehsaninia et al., 2019 ; Madsen et al., 2021 ; Sell-Kubiak et al., 2022 ). In addition, the DHGLM methodology has proven to be a computationally efficient approach to estimating micro-environmental sensitivity ( Rönnegård et al., 2010 ; Felleki et al., 2012 ).…”

Section: Introductionmentioning

confidence: 99%

“…Plant species where inbred lines, single-crosses between inbred lines, or clones can be developed may present a high potential for studying micro-environmental sensitivity. That occurs because the availability of replications on the same individual contributes to a more accurate estimation of genetic variation and breeding values in micro-environmental variance ( Vandenplas et al., 2013 ; Iung et al., 2017 ; Madsen et al., 2021 ). In this sense, studies on micro-environmental sensitivity in wheat can represent an opportunity, as highly homozygous inbred lines can be developed (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Felleki et al (2012) extended the DHGLM model proposed by Rönnegård et al (2010) to include the correlation between random genetic effects for (expressed) phenotypes and residual dispersion. The models proposed by Rönnegård et al (2010) and Felleki et al (2012) have been successfully applied since then and have been further extended to include genomic information and macro-environmental sensitivity, among other applications (Mulder et al, 2013a;Mulder et al, 2013b;Iung et al, 2017;Ehsaninia et al, 2019;Madsen et al, 2021;Sell-Kubiak et al, 2022). In addition, the DHGLM methodology has proven to be a computationally efficient approach to estimating micro-environmental sensitivity (Rönnegård et al, 2010;Felleki et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

et al. 2023

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Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

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Estimation of macro- and micro-genetic environmental sensitivity in unbalanced datasets

Cited by 7 publications

References 17 publications

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

Micro-genetic environmental sensitivity across macro-environments of chickens reared in Burkina Faso and France

Competence to thrive: resilience as an indicator of positive health and positive welfare in animals

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

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