Genomic prediction from observed and imputed high-density ovine genotypes

Moghaddar, Nasir; Swan, Andrew; Van Der Werf, Julius H

doi:10.1186/s12711-017-0315-4

Author(s)

Moghaddar, Nasir

Swan, Andrew

Van Der Werf, Julius H

Publication Date

2017

Abstract

Background: Genomic prediction using high-density (HD) marker genotypes is expected to lead to higher prediction accuracy, particularly for more heterogeneous multi-breed and crossbred populations such as those in sheep and beef cattle, due to providing stronger linkage disequilibrium between single nucleotide polymorphisms and quantitative trait loci controlling a trait. The objective of this study was to evaluate a possible improvement in genomic prediction accuracy of production traits in Australian sheep breeds based on HD genotypes (600k, both observed and imputed) compared to prediction based on 50k marker genotypes. In particular, we compared improvement in prediction accuracy of animals that are more distantly related to the reference population and across sheep breeds. Methods: Genomic best linear unbiased prediction (GBLUP) and a Bayesian approach (BayesR) were used as prediction methods using whole or subsets of a large multi-breed/crossbred sheep reference set. Empirical prediction accuracy was evaluated for purebred Merino, Border Leicester, Poll Dorset and White Suffolk sire breeds according to the Pearson correlation coefficient between genomic estimated breeding values and breeding values estimated based on a progeny test in a separate dataset. Results: Results showed a small absolute improvement (0.0 to 8.0% and on average 2.2% across all traits) in prediction accuracy of purebred animals from HD genotypes when prediction was based on the whole dataset. Greater improvement in prediction accuracy (1.0 to 12.0% and on average 5.2%) was observed for animals that were genetically lowly related to the reference set while it ranged from 0.0 to 5.0% for across-breed prediction. On average, no significant advantage was observed with BayesR compared to GBLUP.

Citation

Genetics Selection Evolution, 49(1), p. 1-10

ISSN

1297-9686

0999-193X

Link

https://hdl.handle.net/1959.11/22513

Language

en

Publisher

BioMed Central Ltd

Title

Genomic prediction from observed and imputed high-density ovine genotypes

Type of document

Journal Article

Entity Type

Publication

Author(s)	Moghaddar, Nasir Swan, Andrew Van Der Werf, Julius H
Publication Date	2017
Abstract	Background: Genomic prediction using high-density (HD) marker genotypes is expected to lead to higher prediction accuracy, particularly for more heterogeneous multi-breed and crossbred populations such as those in sheep and beef cattle, due to providing stronger linkage disequilibrium between single nucleotide polymorphisms and quantitative trait loci controlling a trait. The objective of this study was to evaluate a possible improvement in genomic prediction accuracy of production traits in Australian sheep breeds based on HD genotypes (600k, both observed and imputed) compared to prediction based on 50k marker genotypes. In particular, we compared improvement in prediction accuracy of animals that are more distantly related to the reference population and across sheep breeds. Methods: Genomic best linear unbiased prediction (GBLUP) and a Bayesian approach (BayesR) were used as prediction methods using whole or subsets of a large multi-breed/crossbred sheep reference set. Empirical prediction accuracy was evaluated for purebred Merino, Border Leicester, Poll Dorset and White Suffolk sire breeds according to the Pearson correlation coefficient between genomic estimated breeding values and breeding values estimated based on a progeny test in a separate dataset. Results: Results showed a small absolute improvement (0.0 to 8.0% and on average 2.2% across all traits) in prediction accuracy of purebred animals from HD genotypes when prediction was based on the whole dataset. Greater improvement in prediction accuracy (1.0 to 12.0% and on average 5.2%) was observed for animals that were genetically lowly related to the reference set while it ranged from 0.0 to 5.0% for across-breed prediction. On average, no significant advantage was observed with BayesR compared to GBLUP.
Citation	Genetics Selection Evolution, 49(1), p. 1-10
ISSN	1297-9686 0999-193X
Link	https://hdl.handle.net/1959.11/22513
Language	en
Publisher	BioMed Central Ltd
Title	Genomic prediction from observed and imputed high-density ovine genotypes
Type of document	Journal Article
Entity Type	Publication

Genomic prediction from observed and imputed high-density ovine genotypes

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