Literature DB >> 22916948

The impact of genotyping different groups of animals on accuracy when moving from traditional to genomic selection.

M Pszczola1, T Strabel2, J A M van Arendonk3, M P L Calus4.   

Abstract

Compared with traditional selection, the use of genomic information tends to increase the accuracy of estimated breeding values (EBV). The cause of this increase is, however, unknown. To explore this phenomenon, this study investigated whether the increase in accuracy when moving from traditional (AA) to genomic selection (GG) was mainly due to genotyping the reference population (GA) or the evaluated animals (AG). In it, a combined relationship matrix for simultaneous use of genotyped and ungenotyped animals was applied. A simulated data set reflected the dairy cattle population. Four differently designed (i.e., different average relationships within the reference population) small reference populations and 3 heritability levels were considered. The animals in the reference populations had high, moderate, low, and random (RND) relationships. The evaluated animals were juveniles. The small reference populations simulated difficult or expensive to measure traits (i.e., methane emission). The accuracy of selection was expressed as the reliability of (genomic) EBV and was predicted based on selection index theory using relationships. Connectedness between the reference populations and evaluated animals was calculated using the prediction error variance. Average (genomic) EBV reliabilities increased with heritability and with a decrease in the average relationship within the reference population. Reliabilities in AA and AG were lower than those in GG and were higher than those in GA (respectively, 0.039, 0.042, 0.052, and 0.048 for RND and a heritability of 0.01). Differences between AA and GA were small. Average connectedness with all animals in the reference population for all scenarios and reference populations ranged from 0.003 to 0.024; it was lowest when the animals were not genotyped (AA; e.g., 0.004 for RND) and highest when all the animals were genotyped (GG; e.g., 0.024 for RND). Differences present across designs of the reference populations were very small. Genomic relationships among animals in the reference population might be less important than those for the evaluated animals with no phenotypic observations. Thus, the main origin of the gain in accuracy when using genomic selection is due to genotyping the evaluated animals. However, genotyping only one group of animals will always yield less accurate estimates.
Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22916948     DOI: 10.3168/jds.2012-5550

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


  12 in total

1.  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
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2.  A comparison of principal component regression and genomic REML for genomic prediction across populations.

Authors:  Christos Dadousis; Roel F Veerkamp; Bjørg Heringstad; Marcin Pszczola; Mario P L Calus
Journal:  Genet Sel Evol       Date:  2014-11-05       Impact factor: 4.297

Review 3.  Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency.

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Journal:  Front Genet       Date:  2022-06-09       Impact factor: 4.772

4.  Optimizing the Construction and Update Strategies for the Genomic Selection of Pig Reference and Candidate Populations in China.

Authors:  Xia Wei; Tian Zhang; Ligang Wang; Longchao Zhang; Xinhua Hou; Hua Yan; Lixian Wang
Journal:  Front Genet       Date:  2022-06-08       Impact factor: 4.772

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.  Genomic Prediction of Seed Quality Traits Using Advanced Barley Breeding Lines.

Authors:  Nanna Hellum Nielsen; Ahmed Jahoor; Jens Due Jensen; Jihad Orabi; Fabio Cericola; Vahid Edriss; Just Jensen
Journal:  PLoS One       Date:  2016-10-26       Impact factor: 3.240

7.  Which Individuals To Choose To Update the Reference Population? Minimizing the Loss of Genetic Diversity in Animal Genomic Selection Programs.

Authors:  Sonia E Eynard; Pascal Croiseau; Denis Laloë; Sebastien Fritz; Mario P L Calus; Gwendal Restoux
Journal:  G3 (Bethesda)       Date:  2018-01-04       Impact factor: 3.154

8.  GCA: an R package for genetic connectedness analysis using pedigree and genomic data.

Authors:  Haipeng Yu; Gota Morota
Journal:  BMC Genomics       Date:  2021-02-15       Impact factor: 3.969

9.  Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.).

Authors:  Albert W Schulthess; Yusheng Zhao; C Friedrich H Longin; Jochen C Reif
Journal:  Theor Appl Genet       Date:  2017-12-02       Impact factor: 5.699

10.  Genomic Relatedness Strengthens Genetic Connectedness Across Management Units.

Authors:  Haipeng Yu; Matthew L Spangler; Ronald M Lewis; Gota Morota
Journal:  G3 (Bethesda)       Date:  2017-10-05       Impact factor: 3.154

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