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Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models
- Source :
- Genetics, Selection, Evolution : GSE, Genetics, Selection, Evolution, 45, Genetics, Selection, Evolution 45 (2013)
- Publisher :
- Springer Nature
-
Abstract
- Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring. Open Access
- Subjects :
- dairy-cattle
genotype
phenotypic plasticity
Standard deviation
Statistics
Genetics(clinical)
Genetics
0303 health sciences
Linear model
04 agricultural and veterinary sciences
General Medicine
Variance (accounting)
Monte Carlo Method
Algorithms
Animal Breeding & Genomics
Generalized linear model
reaction norms
selection
milk-production
Biology
Animal Breeding and Genomics
Environment
03 medical and health sciences
Quantitative Trait, Heritable
Genetic model
Animals
developmental stability
Fokkerij en Genomica
Computer Simulation
Genetik
Fokkerij & Genomica
Selection (genetic algorithm)
Ecology, Evolution, Behavior and Systematics
030304 developmental biology
Models, Genetic
Model selection
Research
0402 animal and dairy science
Genetic Variation
040201 dairy & animal science
breeding values
WIAS
Linear Models
Cattle
Gene-Environment Interaction
Animal Science and Zoology
Akaike information criterion
residual variance
environment interactions
Subjects
Details
- Language :
- English
- ISSN :
- 12979686 and 0999193X
- Volume :
- 45
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Genetics Selection Evolution
- Accession number :
- edsair.doi.dedup.....0b5705d55f9e305cd136512e437cfb24
- Full Text :
- https://doi.org/10.1186/1297-9686-45-23