Literature DB >> 24750283

Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

L Zhou1, M S Lund, Y Wang, G Su.   

Abstract

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions.
© 2014 Blackwell Verlag GmbH.

Entities:  

Keywords:  Genomic selection; linkage disequilibrium phase consistence; marker-based relationship matrix; multibreed

Mesh:

Substances:

Year:  2014        PMID: 24750283     DOI: 10.1111/jbg.12089

Source DB:  PubMed          Journal:  J Anim Breed Genet        ISSN: 0931-2668            Impact factor:   2.380


  10 in total

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2.  Including crossbred pigs in the genomic relationship matrix through utilization of both linkage disequilibrium and linkage analysis.

Authors:  M W Iversen; Ø Nordbø; E Gjerlaug-Enger; E Grindflek; M S Lopes; T H E Meuwissen
Journal:  J Anim Sci       Date:  2017-12       Impact factor: 3.159

3.  Genomic predictions in purebreds with a multibreed genomic relationship matrix1.

Authors:  Yvette Steyn; Daniela A L Lourenco; Ignacy Misztal
Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

4.  Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models.

Authors:  Christina Lehermeier; Chris-Carolin Schön; Gustavo de Los Campos
Journal:  Genetics       Date:  2015-06-29       Impact factor: 4.562

5.  Genomic prediction using a reference population of multiple pure breeds and admixed individuals.

Authors:  Emre Karaman; Guosheng Su; Iola Croue; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2021-05-31       Impact factor: 4.297

6.  Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix.

Authors:  Francesco Tiezzi; Christian Maltecca
Journal:  Genet Sel Evol       Date:  2015-04-02       Impact factor: 4.297

7.  Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture.

Authors:  Guiyan Ni; David Cavero; Anna Fangmann; Malena Erbe; Henner Simianer
Journal:  Genet Sel Evol       Date:  2017-01-16       Impact factor: 4.297

8.  Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome.

Authors:  Emre Karaman; Mogens S Lund; Mahlet T Anche; Luc Janss; Guosheng Su
Journal:  G3 (Bethesda)       Date:  2018-11-06       Impact factor: 3.154

9.  Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.

Authors:  Guillaume P Ramstein; Michael D Casler
Journal:  G3 (Bethesda)       Date:  2019-03-07       Impact factor: 3.154

10.  Genomic correlation: harnessing the benefit of combining two unrelated populations for genomic selection.

Authors:  Laercio R Porto-Neto; William Barendse; John M Henshall; Sean M McWilliam; Sigrid A Lehnert; Antonio Reverter
Journal:  Genet Sel Evol       Date:  2015-11-02       Impact factor: 4.297

  10 in total

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