Literature DB >> 33942082

Short communication: investigation of the feasibility of genomic selection in Icelandic Cattle.

Egill Gautason1, Goutam Sahana1, Guosheng Su1, Baldur Helgi Benjamínsson2, Guðmundur Jóhannesson3, Bernt Guldbrandtsen1,4.   

Abstract

Icelandic Cattle is a local dairy cattle breed in Iceland. With about 26,000 breeding females, it is by far the largest among the indigenous Nordic cattle breeds. The objective of this study was to investigate the feasibility of genomic selection in Icelandic Cattle. Pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) were compared. Accuracy, bias, and dispersion of estimated breeding values (EBV) for milk yield (MY), fat yield (FY), protein yield (PY), and somatic cell score (SCS) were estimated in a cross validation-based design. Accuracy (r^) was estimated by the correlation between EBV and corrected phenotype in a validation set. The accuracy (r^) of predictions using ssGBLUP increased by 13, 23, 19, and 20 percentage points for MY, FY, PY, and SCS for genotyped animals, compared with PBLUP. The accuracy of nongenotyped animals was not improved for MY and PY, but increased by 0.9 and 3.5 percentage points for FY and SCS. We used the linear regression (LR) method to quantify relative improvements in accuracy, bias (Δ^), and dispersion (b^) of EBV. Using the LR method, the relative improvements in accuracy of validation from PBLUP to ssGBLUP were 43%, 60%, 50%, and 48% for genotyped animals for MY, FY, PY, and SCS. Single-step GBLUP EBV were less underestimated (Δ^), and less overdispersed (b^) than PBLUP EBV for FY and PY. Pedigree-based BLUP EBV were close to unbiased for MY and SCS. Single-step GBLUP underestimated MY EBV but overestimated SCS EBV. Based on the average accuracy of 0.45 for ssGBLUP EBV obtained in this study, selection intensities according to the breeding scheme of Icelandic Cattle, and assuming a generation interval of 2.0 yr for sires of bulls, sires of dams and dams of bulls, genetic gain in Icelandic Cattle could be increased by about 50% relative to the current breeding scheme.
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  accuracy; bias; cattle; genetic gain; selection; single-step

Mesh:

Year:  2021        PMID: 33942082      PMCID: PMC8489424          DOI: 10.1093/jas/skab139

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  12 in total

1.  Single-step methods for genomic evaluation in pigs.

Authors:  O F Christensen; P Madsen; B Nielsen; T Ostersen; G Su
Journal:  Animal       Date:  2012-04-05       Impact factor: 3.240

2.  Genomic prediction for Nordic Red Cattle using one-step and selection index blending.

Authors:  G Su; P Madsen; U S Nielsen; E A Mäntysaari; G P Aamand; O F Christensen; M S Lund
Journal:  J Dairy Sci       Date:  2012-02       Impact factor: 4.034

3.  Invited review: reliability of genomic predictions for North American Holstein bulls.

Authors:  P M VanRaden; C P Van Tassell; G R Wiggans; T S Sonstegard; R D Schnabel; J F Taylor; F S Schenkel
Journal:  J Dairy Sci       Date:  2009-01       Impact factor: 4.034

4.  Efficient methods to compute genomic predictions.

Authors:  P M VanRaden
Journal:  J Dairy Sci       Date:  2008-11       Impact factor: 4.034

5.  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

6.  Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows.

Authors:  X Ding; Z Zhang; X Li; S Wang; X Wu; D Sun; Y Yu; J Liu; Y Wang; Y Zhang; S Zhang; Y Zhang; Q Zhang
Journal:  J Dairy Sci       Date:  2013-06-05       Impact factor: 4.034

7.  Genomic inbreeding and selection signatures in the local dairy breed Icelandic Cattle.

Authors:  E Gautason; A A Schönherz; G Sahana; B Guldbrandtsen
Journal:  Anim Genet       Date:  2021-04-08       Impact factor: 3.169

8.  Genomic prediction when some animals are not genotyped.

Authors:  Ole F Christensen; Mogens S Lund
Journal:  Genet Sel Evol       Date:  2010-01-27       Impact factor: 4.297

9.  Weighted single-step genomic best linear unbiased prediction integrating variants selected from sequencing data by association and bioinformatics analyses.

Authors:  Aoxing Liu; Mogens Sandø Lund; Didier Boichard; Emre Karaman; Bernt Guldbrandtsen; Sebastien Fritz; Gert Pedersen Aamand; Ulrik Sander Nielsen; Goutam Sahana; Yachun Wang; Guosheng Su
Journal:  Genet Sel Evol       Date:  2020-08-14       Impact factor: 4.297

10.  De novo assembly of the cattle reference genome with single-molecule sequencing.

Authors:  Benjamin D Rosen; Derek M Bickhart; Robert D Schnabel; Sergey Koren; Christine G Elsik; Elizabeth Tseng; Troy N Rowan; Wai Y Low; Aleksey Zimin; Christine Couldrey; Richard Hall; Wenli Li; Arang Rhie; Jay Ghurye; Stephanie D McKay; Françoise Thibaud-Nissen; Jinna Hoffman; Brenda M Murdoch; Warren M Snelling; Tara G McDaneld; John A Hammond; John C Schwartz; Wilson Nandolo; Darren E Hagen; Christian Dreischer; Sebastian J Schultheiss; Steven G Schroeder; Adam M Phillippy; John B Cole; Curtis P Van Tassell; George Liu; Timothy P L Smith; Juan F Medrano
Journal:  Gigascience       Date:  2020-03-01       Impact factor: 6.524

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