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Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits
- Source :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2017, 49 (1), pp.11. ⟨10.1186/s12711-017-0288-3⟩, Genetics, Selection, Evolution : GSE, Genetics Selection Evolution (49), . (2017)
- Publication Year :
- 2017
- Publisher :
- HAL CCSD, 2017.
-
Abstract
- International audience; AbstractBackgroundSome genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits.MethodsThe single-trait SAD model assumes that a random effect at time tj\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$t_{j}$$\end{document} can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities.ResultsFor both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from −0.03 to −0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from −0.57 to −0.67).ConclusionsWe demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.
- Subjects :
- 0301 basic medicine
Litter (animal)
longitudinal data
Multivariate analysis
test-day record
Litter Size
Swine
[SDV]Life Sciences [q-bio]
different paritie
large white-pig
Biology
Quantitative trait locus
Genetic correlation
Correlation
03 medical and health sciences
holstein cow
Quantitative Trait, Heritable
function-valued trait
Statistics
Genetics
Animals
Birth Weight
Genetics(clinical)
Ecology, Evolution, Behavior and Systematics
Models, Genetic
0402 animal and dairy science
04 agricultural and veterinary sciences
General Medicine
Heritability
Random effects model
040201 dairy & animal science
random regression-model
genetic-parameter
reproduction trait
feed-intake
030104 developmental biology
Multivariate Analysis
Trait
Animal Science and Zoology
Rabbits
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 0999193X and 12979686
- Database :
- OpenAIRE
- Journal :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2017, 49 (1), pp.11. ⟨10.1186/s12711-017-0288-3⟩, Genetics, Selection, Evolution : GSE, Genetics Selection Evolution (49), . (2017)
- Accession number :
- edsair.doi.dedup.....3243405a6c8efb3dabf3125dc060c5bf