Theo Meuwissen, Norwegian University of Life Sciences
20 January 2009
In the discussion section it is stated that ‘In simulations of breeding schemes and in cross-validation testing of GW-EBV, the large number of EBV evaluations required may make our fast algorithm the only means to implement BayesB type genome-wide breeding value estimation.’ However, an alternative fast and non-MCMC based algorithm, is published by VanRaden (2008). This algorithm uses a mixture of two normal distributions as a prior. Given this prior, approximate genomic EBVs are obtained by modifying the variance ratio for every SNP in the mixed model equations, which are, apart from the modified variance ratio, equal to the BLUP equations for SNP effects.<br><br>Theo Meuwissen<br><br>Reference:<br>VanRaden, PM (2008) Efficient Methods to Compute Genomic Predictions. J. Dairy Sci. 91:4414–4423.<br>
Alternative fast algorithm
20 January 2009
In the discussion section it is stated that ‘In simulations of breeding schemes and in cross-validation testing of GW-EBV, the large number of EBV evaluations required may make our fast algorithm the only means to implement BayesB type genome-wide breeding value estimation.’ However, an alternative fast and non-MCMC based algorithm, is published by VanRaden (2008). This algorithm uses a mixture of two normal distributions as a prior. Given this prior, approximate genomic EBVs are obtained by modifying the variance ratio for every SNP in the mixed model equations, which are, apart from the modified variance ratio, equal to the BLUP equations for SNP effects.<br><br>Theo Meuwissen<br><br>Reference:<br>VanRaden, PM (2008) Efficient Methods to Compute Genomic Predictions. J. Dairy Sci. 91:4414–4423.<br>
Competing interests
None declared