Literature DB >> 20105546

Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

I Aguilar1, I Misztal, D L Johnson, A Legarra, S Tsuruta, T J Lawlor.   

Abstract

The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2010        PMID: 20105546     DOI: 10.3168/jds.2009-2730

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


  302 in total

1.  Genetic variances of SNP loci for milk yield in dairy cattle.

Authors:  Petr Pešek; Josef Přibyl; Luboš Vostrý
Journal:  J Appl Genet       Date:  2014-11-16       Impact factor: 3.240

2.  Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

Authors:  Alireza Nazarian; Salvador A Gezan
Journal:  J Hered       Date:  2015-12-27       Impact factor: 2.645

3.  Genome-wide association study for carcass quality traits and growth in purebred and crossbred pigs1.

Authors:  Matteo Bergamaschi; Christian Maltecca; Justin Fix; Clint Schwab; Francesco Tiezzi
Journal:  J Anim Sci       Date:  2020-01-01       Impact factor: 3.159

4.  Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection.

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

5.  Ancestral Relationships Using Metafounders: Finite Ancestral Populations and Across Population Relationships.

Authors:  Andres Legarra; Ole F Christensen; Zulma G Vitezica; Ignacio Aguilar; Ignacy Misztal
Journal:  Genetics       Date:  2015-04-14       Impact factor: 4.562

6.  Genomic selection strategies for breeding adaptation and production in dairy cattle under climate change.

Authors:  Ismo Strandén; Juha Kantanen; Isa-Rita M Russo; Pablo Orozco-terWengel; Michael W Bruford
Journal:  Heredity (Edinb)       Date:  2019-03-18       Impact factor: 3.821

7.  The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1.

Authors:  Bruna P Sollero; Jeremy T Howard; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-07-02       Impact factor: 3.159

8.  Genomic selection in American mink (Neovison vison) using a SSGBLUP model for size and quality traits graded on live mink.

Authors:  Trine M Villumsen; Guosheng Su; Bernt Guldbrandtsen; Torben Asp; Mogens S Lund
Journal:  J Anim Sci       Date:  2021-01-08       Impact factor: 3.159

9.  Sparse single-step genomic BLUP in crossbreeding schemes.

Authors:  Jérémie Vandenplas; Mario P L Calus; Jan Ten Napel
Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.