Poly-Omic Prediction of Complex Traits: OmicKriging

TL;DR

This paper introduces OmicKriging, a novel systems approach that integrates multi-omics data to improve the prediction of complex traits, offering a computationally efficient alternative to Bayesian methods.

Contribution

OmicKriging is a new framework that leverages similarity in diverse omics data for trait prediction, enabling easy integration of prior information without heavy computation.

Findings

OmicKriging performs comparably to Bayesian methods with less computational time.

Integrating transcriptomic data enhances prediction accuracy for cellular growth phenotypes.

Combining genotype and expression data improves LDL cholesterol change prediction.

Abstract

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.