Please note that we no longer recommend using MultiBLUP, and therefore this page exists only for completeness. We instead recommend you use the newer Prediction tools, which are generally faster and more accurate.

MultiBLUP is a generalisation of BLUP (Best Linear Unbiased Prediction), used for constructing prediction models. Full details of the method are provided in our Genome Research paper. In brief, MultiBLUP extends the BLUP model to allow for k+r genetic random effect terms:

Y = a + g_{1} + g_{2} + ... + g_{k+r} + e where g_{j}~N(0,K_{j}v_{j}^{2}) and e~N(0,Iv_{e}^{2})

where the first k random effects correspond to standard kinship matrices (typically full-rank), while the last r correspond to regional kinship matrices, each constructed from a subset of predictors (typically low-rank).

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There are two steps when constructing a MultiBLUP prediction model: first you perform REML to estimate the variance terms v_{1}^{2}, ..., v_{k+r}^{2}, v_{e}^{2}, then given these you calculate the BLUE estimates of SNP effect sizes.

MultiBLUP improves on BLUP when the kinship matrices correspond to subsets of predictors with distinct effect size variances. Either you can use Pre-specified Kinships (i.e., constructed based on your prior knowledge) or use Adaptive MultiBLUP to automatically decide the kinship matrices.

Note that when the focus is on prediction, rather than on estimating variance explained, we do not generally advise using SNP weightings (see here for our reasoning). Therefore, kinship matrices should be computed with the option --ignore-weights YES.