Principal component approach in variance component estimation for international sire evaluation

Autor: Esa Mäntysaari, Vincent Ducrocq, Anna-Maria Tyrisevä, W. Freddy Fikse, Martin Lidauer, J. Jakobsen, Karin Meyer
Přispěvatelé: Tyrisevä, Anna-Maria, Biotechnology and Food Research, Biometrical Genetics, Agrifood Research Finland, Animal Genetics and Breeding Unit (AGBU), University of New England (UNE), Department of Animal Bredding and Genetics, Swedish University of Agricultural Sciences (SLU), Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Animal Genetics and Breeding Unit, Department of Animal Breeding and Genetics, Interbull Centre, Department of Animal Breeding and Genetics, Institut National de la Recherche Agronomique (INRA)-AgroParisTech
Jazyk: angličtina
Rok vydání: 2011
Předmět:
Genotype
lcsh:QH426-470
Restricted maximum likelihood
évaluation
[SDV]Life Sciences [q-bio]
Breeding
Biology
Genetic correlation
03 medical and health sciences
Statistics
Genetics
Animals
evaluation
dairy cattle
méthode de sélection
Genetics(clinical)
Selection
Genetic
Ecology
Evolution
Behavior and Systematics
lcsh:SF1-1100
030304 developmental biology
Principal Component Analysis
0303 health sciences
Models
Genetic
business.industry
Covariance matrix
Research
Rank (computer programming)
Sire
0402 animal and dairy science
04 agricultural and veterinary sciences
General Medicine
Covariance
040201 dairy & animal science
Biotechnology
Dairying
lcsh:Genetics
Phenotype
Standard error
bovin laitier
Principal component analysis
taureau
Cattle
Animal Science and Zoology
lcsh:Animal culture
business
Zdroj: Genetics Selection Evolution may (43, online), Non paginé. (2011)
Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2011, 43, online (may), Non paginé. ⟨10.1186/1297-9686-43-21⟩
Genetics Selection Evolution, Vol 43, Iss 1, p 21 (2011)
Genetics, Selection, Evolution : GSE
ISSN: 0999-193X
1297-9686
DOI: 10.1186/1297-9686-43-21⟩
Popis: Background The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model. Methods This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix. Results Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time. Conclusions In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.
Databáze: OpenAIRE
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