Literature DB >> 22443868

Genomic breeding value prediction: methods and procedures.

M P L Calus1.   

Abstract

Animal breeding faces one of the most significant changes of the past decades - the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.

Year:  2010        PMID: 22443868     DOI: 10.1017/S1751731109991352

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  38 in total

1.  The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions.

Authors:  Johnna L Baller; Jeremy T Howard; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-04-03       Impact factor: 3.159

Review 2.  Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking.

Authors:  Hans D Daetwyler; Mario P L Calus; Ricardo Pong-Wong; Gustavo de Los Campos; John M Hickey
Journal:  Genetics       Date:  2012-12-05       Impact factor: 4.562

3.  Fitting and validating the genomic evaluation model to Polish Holstein-Friesian cattle.

Authors:  Joanna Szyda; Andrzej Zarnecki; Tomasz Suchocki; Stanisław Kamiński
Journal:  J Appl Genet       Date:  2011-05-07       Impact factor: 3.240

4.  Genomic prediction of agronomic traits in wheat using different models and cross-validation designs.

Authors:  Teketel A Haile; Sean Walkowiak; Amidou N'Diaye; John M Clarke; Pierre J Hucl; Richard D Cuthbert; Ron E Knox; Curtis J Pozniak
Journal:  Theor Appl Genet       Date:  2020-11-01       Impact factor: 5.699

Review 5.  Will genomic selection be a practical method for plant breeding?

Authors:  Akihiro Nakaya; Sachiko N Isobe
Journal:  Ann Bot       Date:  2012-05-29       Impact factor: 4.357

6.  Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix.

Authors:  Zhe Zhang; Jianfeng Liu; Xiangdong Ding; Piter Bijma; Dirk-Jan de Koning; Qin Zhang
Journal:  PLoS One       Date:  2010-09-09       Impact factor: 3.240

7.  Genome-wide association study identifies Loci for body composition and structural soundness traits in pigs.

Authors:  Bin Fan; Suneel K Onteru; Zhi-Qiang Du; Dorian J Garrick; Kenneth J Stalder; Max F Rothschild
Journal:  PLoS One       Date:  2011-02-24       Impact factor: 3.240

8.  Response to genomic selection: the Bulmer effect and the potential of genomic selection when the number of phenotypic records is limiting.

Authors:  Elizabeth M Van Grevenhof; Johan A M Van Arendonk; Piter Bijma
Journal:  Genet Sel Evol       Date:  2012-08-03       Impact factor: 4.297

9.  A fast EM algorithm for BayesA-like prediction of genomic breeding values.

Authors:  Xiaochen Sun; Long Qu; Dorian J Garrick; Jack C M Dekkers; Rohan L Fernando
Journal:  PLoS One       Date:  2012-11-09       Impact factor: 3.240

10.  A comparison of statistical methods for genomic selection in a mice population.

Authors:  Haroldo H R Neves; Roberto Carvalheiro; Sandra A Queiroz
Journal:  BMC Genet       Date:  2012-11-08       Impact factor: 2.797

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