Having estimated variance components, you can then use these to estimate predictor effect sizes. The argument for this is

–calc-blups

to which the following options can be added (to restrict to a subset of the data see Data Filtering):

–remlfile <remlfile> – required to provide the .reml file produced by running REML. Alternatively, you can specify the .indi.blp file directly using –blupfile (and if using regions also the .reg.blup file using –regfile).

–grm <grmstem> or –mgrm <grmlist> – required if the REML analysis used one or more kinship matrices.

–bfile/–chiamo/–sp/–speed <prefix> – required to provide the data files corresponding to the kinship matrices and regions.

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The following files are produced:

<output>.blup – contains the overall estimates of effect sizes for each predictor. The first four columns provide the predictor name, Allele 1 (test allele), Allele 2 (reference allele) and the predictor centre (the mean of its allele count with respect to Allele 1), with the final column providing the (raw) effect size. To work out the contribution of a predictor, subtract from its value the centre then multiply this by the effect size.

<output>.blup.full – contains the individual estimates of effect sizes for each kinship matrix and region. The first three columns provide predictor name, Allele 1 and Allele 2, then pairs of columns provide the predictor centre and (raw) effect size for a kinship matrix or region. Typically, each predictor will only contribute towards one kinship matrix or region and so for most predictors all except one of the effect size estimates will be zero.

<output>.pred – contains the predicted phenotypes. The first two columns provide individual IDs, the third provides the overall prediction, while the remaining columns show the individual contributions of each kinship matrix and region.