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A fast EM algorithm for genomic selection

conference contribution
posted on 06.12.2017, 00:00 authored by Ross ShepherdRoss Shepherd, T Meuwissen, J Woolliams
Genomic selection is being adopted in many livestock breeding programs. Some industry applications use BLUP methods (called GS-BLUP) which are computationally fast but assume each marker effect is normally distributed with the same variance. The accuracy of prediction can often be increased by using models which not only allow marker variance to vary but also allow a large proportion of markers to have no effect. Meuwissen et al. (2001) called these methods BayesA and BayesB respectively. However implementing these Bayesian methods is computationally slow, particularly for large SNP panels. This paper gives details of an Expectation Maximisation (EM) algorithm called emBayesB for implementing a BayesB-like model which is both fast and accurate.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Parent Title

9th World Congress on genetics applied to livestock production (WCGALP) : 1st - 6th August, Leipzig, Germany

Start Page

1

End Page

4

Number of Pages

4

Start Date

01/01/2010

ISBN-13

9783000316081

Location

Leipzig, Germany

Publisher

German Society for Animal Science

Place of Publication

Gießen

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; Institute for Resource Industries and Sustainability (IRIS); Roslin Institute; Universitetet for miljø- og biovitenskap;

Era Eligible

Yes

Name of Conference

World Congress on Genetics Applied to Livestock Production