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A fast EM algorithm for genomic selection
conference contribution
posted on 2017-12-06, 00:00 authored by Ross ShepherdRoss Shepherd, T Meuwissen, J WoolliamsGenomic 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, GermanyStart Page
1End Page
4Number of Pages
4Start Date
2010-01-01ISBN-13
9783000316081Location
Leipzig, GermanyPublisher
German Society for Animal SciencePlace of Publication
GießenPeer 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