Literature DB >> 23477821

Short communication: genomic evaluations of final score for US Holsteins benefit from the inclusion of genotypes on cows.

S Tsuruta1, I Misztal, T J Lawlor.   

Abstract

Currently, the US Department of Agriculture Animal Improvement Programs Laboratory utilizes a multi-step procedure in genomic evaluations for US Holstein bulls and cows, with adjustments for cows. We used a single-step procedure to investigate whether adding cows' genotypes could increase reliability in genomic breeding values for bulls while minimizing bias. The first data set to 2007 was used to calculate genomic estimated breeding values (GEBV) for animals, including young genotyped bulls with no daughters and young cows (heifers) with no records in 2007. The second data set to 2011 was used to calculate GEBV for the same animals, including those young bulls with daughters and young cows with records in 2011. Genotypes (42,503 single nucleotide polymorphism markers) for 34,506 bulls and 5,235 cows from 356,413 bulls and 9,245,619 cows in pedigree were used to calculate single-step GEBV (ssGEBV) and multi-step GEBV (msGEBV). Regression coefficients of 2007 GEBV on 2011 progeny deviations and coefficients of determination were used as indicators of bias and reliability in 2007 GEBV for bulls with no daughters and for cows with no records in 2007, using bull genotypes only and using bull and cow genotypes. Parent averages were also calculated from estimated breeding values of parents to compare with GEBV. For genotyped bulls, inflation was larger for ssGEBV than for msGEBV, whereas reliability was higher for ssGEBV. Using all genotyped bulls and cows, reliabilities were increased by 2 to 3%. Use of genotypes of high-profile cows improves reliability in ssGEBV and msGEBV for bulls.
Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23477821     DOI: 10.3168/jds.2012-6272

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


  9 in total

1.  DAIRRy-BLUP: a high-performance computing approach to genomic prediction.

Authors:  Arne De Coninck; Jan Fostier; Steven Maenhout; Bernard De Baets
Journal:  Genetics       Date:  2014-04-15       Impact factor: 4.562

2.  Prediction ability for growth and maternal traits using SNP arrays based on different marker densities in Nellore cattle using the ssGBLUP.

Authors:  Juan Diego Rodriguez Neira; Elisa Peripolli; Maria Paula Marinho de Negreiros; Rafael Espigolan; Rodrigo López-Correa; Ignacio Aguilar; Raysildo B Lobo; Fernando Baldi
Journal:  J Appl Genet       Date:  2022-02-08       Impact factor: 3.240

3.  Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken.

Authors:  Daniela A L Lourenco; Breno O Fragomeni; Shogo Tsuruta; Ignacio Aguilar; Birgit Zumbach; Rachel J Hawken; Andres Legarra; Ignacy Misztal
Journal:  Genet Sel Evol       Date:  2015-07-02       Impact factor: 4.297

4.  Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data.

Authors:  Rafael Lara Tonussi; Rafael Medeiros de Oliveira Silva; Ana Fabrícia Braga Magalhães; Rafael Espigolan; Elisa Peripolli; Bianca Ferreira Olivieri; Fabieli Loise Braga Feitosa; Marcos Vinicíus Antunes Lemos; Mariana Piatto Berton; Hermenegildo Lucas Justino Chiaia; Angelica Simone Cravo Pereira; Raysildo Barbosa Lôbo; Luiz Antônio Framartino Bezerra; Cláudio de Ulhoa Magnabosco; Daniela Andressa Lino Lourenço; Ignácio Aguilar; Fernando Baldi
Journal:  PLoS One       Date:  2017-09-28       Impact factor: 3.240

5.  The effect of the H-1 scaling factors τ and ω on the structure of H in the single-step procedure.

Authors:  Johannes W R Martini; Matias F Schrauf; Carolina A Garcia-Baccino; Eduardo C G Pimentel; Sebastian Munilla; Andres Rogberg-Muñoz; Rodolfo J C Cantet; Christian Reimer; Ning Gao; Valentin Wimmer; Henner Simianer
Journal:  Genet Sel Evol       Date:  2018-04-13       Impact factor: 4.297

6.  Determining Heat Stress Effects of Multiple Genetic Traits in Tropical Dairy Cattle Using Single-Step Genomic BLUP.

Authors:  Piriyaporn Sungkhapreecha; Vibuntita Chankitisakul; Monchai Duangjinda; Sayan Buaban; Wuttigrai Boonkum
Journal:  Vet Sci       Date:  2022-02-03

7.  Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population.

Authors:  Piriyaporn Sungkhapreecha; Ignacy Misztal; Jorge Hidalgo; Daniela Lourenco; Sayan Buaban; Vibuntita Chankitisakul; Wuttigrai Boonkum
Journal:  Vet World       Date:  2021-12-15

8.  Genomic Prediction of Average Daily Gain, Back-Fat Thickness, and Loin Muscle Depth Using Different Genomic Tools in Canadian Swine Populations.

Authors:  Siavash Salek Ardestani; Mohsen Jafarikia; Mehdi Sargolzaei; Brian Sullivan; Younes Miar
Journal:  Front Genet       Date:  2021-06-03       Impact factor: 4.599

Review 9.  Single-Step Genomic Evaluations from Theory to Practice: Using SNP Chips and Sequence Data in BLUPF90.

Authors:  Daniela Lourenco; Andres Legarra; Shogo Tsuruta; Yutaka Masuda; Ignacio Aguilar; Ignacy Misztal
Journal:  Genes (Basel)       Date:  2020-07-14       Impact factor: 4.096

  9 in total

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