Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/9815
Title: Principal component approach in variance component estimation for international sire evaluation
Contributor(s): Tyriseva, A-M (author); Meyer, Karin  (author); Fikse, F (author); Ducrocq, V (author); Jakobsen, J (author); Lidauer, M H (author); Mantysaari, E A (author)
Publication Date: 2011
Open Access: Yes
DOI: 10.1186/1297-9686-43-21Open Access Link
Handle Link: https://hdl.handle.net/1959.11/9815
Abstract: 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.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, 43(May), p. 1-13
Publisher: BioMed Central Ltd
Place of Publication: United Kingdom
ISSN: 1297-9686
0999-193X
Fields of Research (FoR) 2008: 070201 Animal Breeding
060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
Socio-Economic Objective (SEO) 2008: 830302 Dairy Cattle
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article

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