Literature DB >> 21787954

Genomic and pedigree-based genetic parameters for scarcely recorded traits when some animals are genotyped.

R F Veerkamp1, H A Mulder, R Thompson, M P L Calus.   

Abstract

Genetic parameters were estimated using relationships between animals that were based either on pedigree, 43,011 single nucleotide polymorphisms, or a combination of these, considering genotyped and non-genotyped animals. The standard error of the estimates and a parametric bootstrapping procedure was used to investigate sampling properties of the estimated variance components. The data set contained milk yield, dry matter intake and body weight for 517 first-lactation heifers with genotypes and phenotypes, and another 112 heifers with phenotypes only. Multivariate models were fitted using the different relationships in ASReml software. Estimates of genetic variance were lower based on genomic relationships than using pedigree relationships. Genetic variances from genomic and pedigree relationships were, however, not directly comparable because they apply to different base populations. Standard errors indicated that using the genomic relationships gave more accurate estimates of heritability but equally accurate estimates of genetic correlation. However, the estimates of standard errors were affected by the differences in scale between the 2 relationship matrices, causing differences in values of the genetic parameters. The bootstrapping results (with genetic parameters at the same level), confirmed that both heritability and genetic correlations were estimated more accurately with genomic relationships in comparison with using the pedigree relationships. Animals without genotype were included in the analysis by merging genomic and pedigree relationships. This allowed all phenotypes to be used, including those from non-genotyped animals. This combination of genomic and pedigree relationships gave the most accurate estimates of genetic variance. When a small data set is available it might be more advantageous for the estimation of genetic parameters to genotype existing animals, rather than collecting more phenotypes.
Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21787954     DOI: 10.3168/jds.2011-4223

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  20 in total

1.  Unraveling additive from nonadditive effects using genomic relationship matrices.

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Journal:  Genetics       Date:  2014-10-15       Impact factor: 4.562

2.  Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single-nucleotide polymorphism array.

Authors:  Mohammad S A Bhuiyan; Yeong Kuk Kim; Hyun Joo Kim; Doo Ho Lee; Soo Hyun Lee; Ho Baek Yoon; Seung Hwan Lee
Journal:  J Anim Sci       Date:  2018-09-29       Impact factor: 3.159

3.  Beef trait genetic parameters based on old and recent data and its implications for genomic predictions in Italian Simmental cattle.

Authors:  Alberto Cesarani; Jorge Hidalgo; Andre Garcia; Lorenzo Degano; Daniele Vicario; Yutaka Masuda; Ignacy Misztal; Daniela Lourenco
Journal:  J Anim Sci       Date:  2020-08-01       Impact factor: 3.159

4.  Comparison of genetic parameters and estimated breeding values for worm resistance in meat sheep obtained using traditional and genomic models.

Authors:  Gleyson Vieira Dos Santos; Natanael Pereira da Silva Santos; Luiz Antonio Silva Figueiredo Filho; Fábio Barros Britto; Luciano Silva Sena; Tatiana Saraiva Torres; Paulo Luiz Souza Carneiro; José Lindenberg Rocha Sarmento
Journal:  Trop Anim Health Prod       Date:  2021-04-23       Impact factor: 1.559

5.  Bias in variance component estimation in swine crossbreeding schemes using selective genotyping and phenotyping strategies.

Authors:  Garrett M See; Benny E Mote; Matthew L Spangler
Journal:  J Anim Sci       Date:  2021-11-01       Impact factor: 3.338

6.  Pedigree-free estimates of heritability in the wild: promising prospects for selfing populations.

Authors:  Laurene Gay; Mathieu Siol; Joelle Ronfort
Journal:  PLoS One       Date:  2013-06-25       Impact factor: 3.240

7.  Genomic prediction of disease occurrence using producer-recorded health data: a comparison of methods.

Authors:  Kristen L Parker Gaddis; Francesco Tiezzi; John B Cole; John S Clay; Christian Maltecca
Journal:  Genet Sel Evol       Date:  2015-05-08       Impact factor: 4.297

Review 8.  The Use of "Omics" in Lactation Research in Dairy Cows.

Authors:  Shanshan Li; Quanjuan Wang; Xiujuan Lin; Xiaolu Jin; Lan Liu; Caihong Wang; Qiong Chen; Jianxin Liu; Hongyun Liu
Journal:  Int J Mol Sci       Date:  2017-05-05       Impact factor: 5.923

9.  Genetic parameters for growth and faecal worm egg count following Haemonchus contortus experimental infestations using pedigree and molecular information.

Authors:  Fabrizio Assenza; Jean-Michel Elsen; Andrés Legarra; Clément Carré; Guillaume Sallé; Christèle Robert-Granié; Carole R Moreno
Journal:  Genet Sel Evol       Date:  2014-02-14       Impact factor: 4.297

10.  Identification of Promising Mutants Associated with Egg Production Traits Revealed by Genome-Wide Association Study.

Authors:  Jingwei Yuan; Congjiao Sun; Taocun Dou; Guoqiang Yi; LuJiang Qu; Liang Qu; Kehua Wang; Ning Yang
Journal:  PLoS One       Date:  2015-10-23       Impact factor: 3.240

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