Abstract

Understanding the molecular mechanisms underlying the connections between genotype and phenotype in human is one of the most important biological questions.By integrating genomic and genetic information to constructing network models, we demonstrate that potential gene regulatory mechanisms and key genes can be identified. We review multiple network algorithms that have been applied to discovering the key pathways linking the genetic and phenotype. Using two application examples, we show that novel hypotheses can be formed and experimentally validated to help us to better understand human complex diseases.

Introduction

A major goal in current biomedical research is to understand the mechanisms of various diseases to allow the development of novel or improved treatments. For complex diseases (e.g., cancers, diabetes, and cardiovascular diseases), multiple genetic factors interact with environmental factors to determine the disease development and progression (Hunter, 2005; Schadt, 2009). With recent large efforts in genome-wide association studies (GWAS), thousands of genetic variants have been identified for the association with various disease phenotypes. However, for most of these variants, little is known of their mechanisms of causing disease. This poses a major challenge for complex disease research in the post-GWAS era. For variants in the protein coding region that lead to nonsynonymous changes, it is reasonable to suspect that disease phenotypes are the consequence of the aberrant changes from the corresponding proteins’ function. However, more than 80% of these single nucleotide polymorphisms (SNPs) are located in noncoding regions, most of them are likely to function through gene expression regulation (Schaub et al., 2012). This is supported by the findings that GWAS SNPs are enriched within DNase I hypersensitive (DHS) sites (Maurano et al., 2012) and more likely to be eQTL (expression quantitative trait locus) SNPs (Nicolae et al., 2010). With the advancement of high-throughput technologies to allow quantitatively measuring the whole transcriptome, studying the genetics of genome-wide gene expression regulation became possible and was first performed in yeast (Brem et al., 2002) and then quickly applied to other species like mouse and human (Cheung et al., 2003; Schadt et al., 2003).