Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies

Abstract

The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

Figure 1. Variations of the triangle method

eQTL: Expression quantitative trait loci

Figure 2. Decision tree example

For the SNP variables, the genotypes are represented as: 0: no minor alleles; 1: one minor allele; and 2: two minor alleles. The up and down dashed arrows indicate increased and decreased gene expression, respectively. EXP: Gene expression

Figure 3. Bayesian network example with direct and indirect effects

EXP: Gene expression; PHENO: Phenotype.

Figure 4. Single-layer artificial neural network