The most basic use of MultiBLUP is when we have already computed a number of kinship matrices (which because the aim is prediction we advise doing with weightings turned off) and wish to perform generalised BLUP. First we estimate the variance components.

–reml <output> –pheno <phenofile> –mgrm <grmlist>

where grmlist provides the stems of the pre-computed kinship matrices and <phenofile> provides the phenotypic values (stored in PLINK format). The variance estimates will be saved in <output>.reml, with the random effects in <output>.indi.blp.

To include covariates add the option –covar. Use –keep to consider only a subset of samples; this is useful if wishing to fit the model on a training sample and then measure predictive accuracy for a test sample.

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Next we obtain estimates of predictor effect sizes.

–calc-blups <output> –mgrm <grmlist> –remlfile <output>.reml –bfile test

This step requires one of –bfile/–chiamo/–sp/–speed <prefix> – to provide the data files from which the kinship matrices were calculated (see File Formats).

The SNP effect sizes will be saved in <output>.blup, with the random effects in <output>.pred.

If <grmlist> contains only one kinship matrix (or if instead of –mgrm you use –grm), then the above commands will be equivalent to running (standard) BLUP.

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The following example uses the files provided in the Test Datasets. The kinship matrices kins/kinshipA and kins/kinshipB were created from the binary PLINK files test.bed, test.bim and test.fam. Phenotypes are stored in phen.pheno and the file mlist.txt contains

kins/kinshipA

kins/kinshipB

The commands

../ldak.out –reml out –mgrm mlist.txt –pheno phen.pheno

../ldak.out –calc-blups out –mgrm mlist.txt –remlfile out.reml –bfile test

will perform 2-way MultiBLUP, saving the effect size estimates in out.blup and the predictions in out.pred. Column 4 of out.blup will report cumulative effect sizes, which are the sum of the effect sizes corresponding to each kinship matrix (Columns 6 and 8). Similarly, Column 3 of out.pred will report cumulative predictions, which are the sum of predictions corresponding to each kinship matrix (Columns 5 and 7).