OPTBR: computationally efficient genomic predictions and QTL mapping in multi-breed populations — ASN Events
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  2. Concurrent 4 - Statistical methods & computation I
  3. OPTBR: computationally efficient genomic predictions and QTL mapping in multi-breed populations

OPTBR: computationally efficient genomic predictions and QTL mapping in multi-breed populations (#23)

Tingting Wang 1 2 3 , Yi-Ping Phoebe Chen 1 , Kathryn E. Kemper 4 , Michael E. Goddard 4 , Ben J. Hayes 1 2 3
  1. Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, VIC, Australia
  2. Dairy Futures Cooperative Research Centre, Melbourne, VIC, Australia
  3. DEDJTR, Bundoora, VIC, Australia
  4. Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC, Australia
As  genomic data used for prediction of complex traits rapidly expand in size, the importance of computational efficiency of genomic prediction algorithms becomes paramount.  In this paper we describe an expectation-maximisation algorithm for genomic prediction (OptBR) with the speed-up scheme that is up to 30 times faster than MCMC implementations. The algorithm is suitable for joint analysis of data from different sources, as it includes weightings for the accuracy of phenotype, and can accommodate effects of factors such as breed, age, sex and additional covariates.  A further advantage of the method is that QTL mapping is performed simultaneously with genomic prediction.
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