  High-confidence prediction of complex traits such as disease risk or drug
response is an ultimate goal of personalized medicine. Although genome-wide
association studies have discovered thousands of well-replicated polymorphisms
associated with a broad spectrum of complex traits, the combined predictive
power of these associations for any given trait is generally too low to be of
clinical relevance. We propose a novel systems approach to complex trait
prediction, which leverages and integrates similarity in genetic,
transcriptomic or other omics-level data. We translate the omic similarity into
phenotypic similarity using a method called Kriging, commonly used in
geostatistics and machine learning. Our method called OmicKriging emphasizes
the use of a wide variety of systems-level data, such as those increasingly
made available by comprehensive surveys of the genome, transcriptome and
epigenome, for complex trait prediction. Furthermore, our OmicKriging framework
allows easy integration of prior information on the function of subsets of
omics-level data from heterogeneous sources without the sometimes heavy
computational burden of Bayesian approaches. Using seven disease datasets from
the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging
allows simple integration of sparse and highly polygenic components yielding
comparable performance at a fraction of the computing time of a recently
published Bayesian sparse linear mixed model method. Using a cellular growth
phenotype, we show that integrating mRNA and microRNA expression data
substantially increases performance over either dataset alone. We also
integrate genotype and expression data to predict change in LDL cholesterol
levels after statin treatment and show that OmicKriging performs better than
the polygenic score method. We provide an R package to implement OmicKriging.
