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Using cross-validation in a fast EM algorithm for genomic selection and complex trait prediction

conference contribution
posted on 06.12.2017, 00:00 authored by Ross ShepherdRoss Shepherd, Michael DrummMichael Drumm, J Yang
The paper reports on changes to the EM algorithm emBayesB which estimates QTL effects using dense genome-wide SNP marker data. To overcome convergence issues, modifications were made to the original algorithm which included cross-validation for the estimation of model parameters. The modified algorithm called emBayesB_CV was used to analyse a trait simulated on real human genotypes consisting of 294,831 SNP measured on 3925 individuals. Three datasets were simulated for a trait determined by 10, 100 or 1000 additive QTL. The results showed that the modified algorithm emBayesB_CV was not only computationally fast, but also more accurate than GBLUP in predicting breeding value. However prediction accuracy declined as the size of QTL effects decreased due to the result that although emBayesB_CV could accurately locate the chromosomal location of large QTL effects, this was not the case for small QTL effects.

History

Start Page

463

End Page

466

Number of Pages

4

Start Date

01/01/2013

Finish Date

01/01/2013

ISSN

1328-3227

ISBN-13

9780473260569

Location

Napier, New Zealand

Publisher

Association for the Advancement of Animal Breeding and Genetics

Place of Publication

Palmerston North, New Zealand

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

Association for the Advancement of Animal Breeding and Genetics. Conference