Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium

Comparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (co)variance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three sin...

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Main Authors: Pierre Coenraets, Nicolas Gengler
Format: Article
Language:English
Published: Presses Agronomiques de Gembloux 1997-01-01
Series:Biotechnologie, Agronomie, Société et Environnement
Subjects:
Online Access:http://www.pressesagro.be/base/text/v1n1/26.pdf
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spelling doaj-0973346acb664ba6b0ea2803baf4f3ee2020-11-24T21:40:28ZengPresses Agronomiques de GemblouxBiotechnologie, Agronomie, Société et Environnement1370-62331780-45071997-01-01112633Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in BelgiumPierre CoenraetsNicolas GenglerComparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (co)variance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three single-trait analyzes. Rank correlations between breeding values for 54,820 cows with records (for their 1,406 sires) estimated with the single-trait and multiple-trait models were over .98 (.99) in fat yield and over .99 (.99) in milk and protein yields. The relative gain expressed as reduction in mean prediction error variance was 3% (1%) in milk yield, 6% (3%) in fat yield, and .4% (.2%) in protein yield for cows (for sires). Relative genetic gains were 3% (1%), 6% (2%) and .5% (.2%) respectively in milk, fat and protein yields for cows (for sires). The use of multiple-trait models bas therefore the advantages of improved precision and reduced selection bics. Multiple-trait analysis could be extended for the analyzes of test-day records. Results show that this or similar multiple-trait animal model could be implemented immediately in Belgium at low computing cost, using the proposed algorithme and could be the first step to new, more advanced evaluation methods.http://www.pressesagro.be/base/text/v1n1/26.pdfDairy cattlemilk production traitsgenetic evaluationmultiple-traitcomputation time
collection DOAJ
language English
format Article
sources DOAJ
author Pierre Coenraets
Nicolas Gengler
spellingShingle Pierre Coenraets
Nicolas Gengler
Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
Biotechnologie, Agronomie, Société et Environnement
Dairy cattle
milk production traits
genetic evaluation
multiple-trait
computation time
author_facet Pierre Coenraets
Nicolas Gengler
author_sort Pierre Coenraets
title Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
title_short Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
title_full Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
title_fullStr Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
title_full_unstemmed Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium
title_sort use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in belgium
publisher Presses Agronomiques de Gembloux
series Biotechnologie, Agronomie, Société et Environnement
issn 1370-6233
1780-4507
publishDate 1997-01-01
description Comparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (co)variance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three single-trait analyzes. Rank correlations between breeding values for 54,820 cows with records (for their 1,406 sires) estimated with the single-trait and multiple-trait models were over .98 (.99) in fat yield and over .99 (.99) in milk and protein yields. The relative gain expressed as reduction in mean prediction error variance was 3% (1%) in milk yield, 6% (3%) in fat yield, and .4% (.2%) in protein yield for cows (for sires). Relative genetic gains were 3% (1%), 6% (2%) and .5% (.2%) respectively in milk, fat and protein yields for cows (for sires). The use of multiple-trait models bas therefore the advantages of improved precision and reduced selection bics. Multiple-trait analysis could be extended for the analyzes of test-day records. Results show that this or similar multiple-trait animal model could be implemented immediately in Belgium at low computing cost, using the proposed algorithme and could be the first step to new, more advanced evaluation methods.
topic Dairy cattle
milk production traits
genetic evaluation
multiple-trait
computation time
url http://www.pressesagro.be/base/text/v1n1/26.pdf
work_keys_str_mv AT pierrecoenraets useofmultipletraitanimalmodelsforgeneticevaluationofmilkfatandproteinlactationyieldsofdairycattleinbelgium
AT nicolasgengler useofmultipletraitanimalmodelsforgeneticevaluationofmilkfatandproteinlactationyieldsofdairycattleinbelgium
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