Literature DB >> 22118107

Technical note: adjustment of traditional cow evaluations to improve accuracy of genomic predictions.

G R Wiggans1, T A Cooper, P M Vanraden, J B Cole.   

Abstract

Genomic evaluations are calculated using deregressed predicted transmitting abilities (PTA) from traditional evaluations to estimate effects of single nucleotide polymorphisms. The direct genomic value (sum of an animal's marker effects) should be consistent with traditional PTA, which is the case for bulls. However, traditional PTA of yield traits (milk, fat, and protein) for genotyped cows are higher than their direct genomic values. To ensure that characteristics of cow PTA for yield traits were more similar to those for bull PTA, mean and variance of cow Mendelian sampling (PTA minus parent average) were adjusted to be similar to those of bulls. The same adjustments were used for all genotyped cows in a breed. To determine gains in reliabilities, predictions were made for bulls with August 2010 evaluations that did not have traditional evaluations in August 2006. By adjusting cow PTA and parent averages of genotyped animals, Holstein and Jersey regressions of August 2010 deregressed PTA on genomic evaluations based on August 2006 data became closer to 1 for the adjusted predictor population compared with the unadjusted predictor population. Evaluation bias was decreased for Holsteins when the predictor population was adjusted. Mean gain in reliability over parent average increased 3.5 percentage points across yield traits for Holsteins and 0.9 percentage points for Jerseys when the predictor population was adjusted. The accuracy of genomic evaluations for Holsteins and Jerseys was increased through better use of information from cows.
Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 22118107     DOI: 10.3168/jds.2011-4481

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


  14 in total

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Authors:  Adriana García-Ruiz; John B Cole; Paul M VanRaden; George R Wiggans; Felipe J Ruiz-López; Curtis P Van Tassell
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-27       Impact factor: 11.205

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.  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

4.  Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment.

Authors:  Romain Dassonneville; Aurelia Baur; Sébastien Fritz; Didier Boichard; Vincent Ducrocq
Journal:  Genet Sel Evol       Date:  2012-12-27       Impact factor: 4.297

5.  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

6.  Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction.

Authors:  Liuhong Chen; Changxi Li; Mehdi Sargolzaei; Flavio Schenkel
Journal:  PLoS One       Date:  2014-07-15       Impact factor: 3.240

Review 7.  Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.

Authors:  C Egger-Danner; J B Cole; J E Pryce; N Gengler; B Heringstad; A Bradley; K F Stock
Journal:  Animal       Date:  2014-11-12       Impact factor: 3.240

8.  Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions.

Authors:  Kathryn E Kemper; Coralie M Reich; Philip J Bowman; Christy J Vander Jagt; Amanda J Chamberlain; Brett A Mason; Benjamin J Hayes; Michael E Goddard
Journal:  Genet Sel Evol       Date:  2015-04-17       Impact factor: 4.297

9.  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

10.  Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers.

Authors:  Guosheng Su; Bernt Guldbrandtsen; Gert P Aamand; Ismo Strandén; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2014-07-30       Impact factor: 4.297

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