  In this article, we propose a covariance based method for combining partial
data sets in the genotype to phenotype spectrum. In particular, an
expectation-maximization algorithm that can be used to combine partially
overlapping relationship/covariance matrices is introduced. Combining data this
way, based on relationship matrices, can be contrasted with a feature
imputation based approach. We used several public genomic data sets to explore
the accuracy of combining genomic relationship matrices. We have also used the
heterogeneous genotype/phenotype data sets in the https://triticeaetoolbox.org/
to illustrate how this new method can be used in genomic prediction, phenomics,
and graphical modeling.
