Literature DB >> 8567456

Genetic correlation and heritabilities for purebred and crossbred performance in poultry egg production traits.

M Wei1, J H van der Werf.   

Abstract

Genetic correlations between purebred and crossbred performance and purebred and crossbred heritabilities were estimated for egg production traits of laying chickens using a multivariate sire model accounting for additive relationships between sires. Two sire lines, denoted lines 1 and 2, were crossed to one dam line to produce crossbred progeny. Records for egg weight, egg specific gravity, and egg number were collected on purebred and crossbred hens. In total, 99 sires in line 1 and 292 sires in line 2 were used in the analysis, each sire producing on average 45 purebred and 105 crossbred daughters. Estimates of purebred heritability in lines 1 and 2 were in range of .54 to .74 for egg number traits, .52 to .91 for egg weight traits, and .41 to .83 for egg specific gravity traits. Estimates of crossbred heritability were .04 to .51 for egg numbers, .23 to .45 for egg weight, and .13 to .31 for egg specific gravity. The sire component in crossbreds differed up to 78% from the sire component in purebreds depending on traits. The estimate of genetic correlation (rpc) between purebred and crossbred performance was .56 to .73 for egg number, .69 to .99 for egg weight, and .72 to .82 for egg specific gravity. Although crossbred parameters were strongly affected by environmental factors, the results tend to agree with the theory that traits with a larger dominance variation and a larger difference between sire components in purebreds and crossbreds show a lower rpc.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1995        PMID: 8567456     DOI: 10.2527/1995.7382220x

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  14 in total

1.  Genomic prediction for crossbred performance using metafounders.

Authors:  Elizabeth M van Grevenhof; Jérémie Vandenplas; Mario P L Calus
Journal:  J Anim Sci       Date:  2019-02-01       Impact factor: 3.159

2.  Standard error of the genetic correlation: how much data do we need to estimate a purebred-crossbred genetic correlation?

Authors:  Piter Bijma; John W M Bastiaansen
Journal:  Genet Sel Evol       Date:  2014-11-19       Impact factor: 4.297

3.  Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep.

Authors:  Nasir Moghaddar; Andrew A Swan; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2014-09-30       Impact factor: 4.297

4.  Genomic evaluation for a three-way crossbreeding system considering breed-of-origin of alleles.

Authors:  Claudia A Sevillano; Jeremie Vandenplas; John W M Bastiaansen; Rob Bergsma; Mario P L Calus
Journal:  Genet Sel Evol       Date:  2017-10-23       Impact factor: 4.297

5.  Genetic parameters and purebred-crossbred genetic correlations for growth, meat quality, and carcass traits in pigs.

Authors:  Hadi Esfandyari; Dinesh Thekkoot; Robert Kemp; Graham Plastow; Jack Dekkers
Journal:  J Anim Sci       Date:  2020-12-01       Impact factor: 3.159

6.  Design of reference populations for genomic selection in crossbreeding programs.

Authors:  Ilse E M van Grevenhof; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2015-03-07       Impact factor: 4.297

7.  Genomic selection for the improvement of antibody response to Newcastle disease and avian influenza virus in chickens.

Authors:  Tianfei Liu; Hao Qu; Chenglong Luo; Xuewei Li; Dingming Shu; Mogens Sandø Lund; Guosheng Su
Journal:  PLoS One       Date:  2014-11-17       Impact factor: 3.240

8.  Estimating the purebred-crossbred genetic correlation for uniformity of eggshell color in laying hens.

Authors:  Han A Mulder; Jeroen Visscher; Julien Fablet
Journal:  Genet Sel Evol       Date:  2016-05-05       Impact factor: 4.297

9.  Pedigree and genomic evaluation of pigs using a terminal-cross model.

Authors:  Llibertat Tusell; Hélène Gilbert; Juliette Riquet; Marie-José Mercat; Andres Legarra; Catherine Larzul
Journal:  Genet Sel Evol       Date:  2016-04-07       Impact factor: 4.297

10.  Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens.

Authors:  Tianfei Liu; Hao Qu; Chenglong Luo; Dingming Shu; Jie Wang; Mogens Sandø Lund; Guosheng Su
Journal:  BMC Genet       Date:  2014-10-15       Impact factor: 2.797

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