Association between Genetic Variants and Inherited Multifactorial DIseases: a Systems Biology Approach

Identification of candidate genes for inherited multifactorial diseases has historically been very difficult due to a variety of complicating factors.  The interactions of genes and gene networks with environmental factors, timing of gene expression and/or activation of specific gene pathways, and impact of genetic variations on perturbations of existing gene networks all may play a part in causing inherited susceptibility to a disease.  Here we demonstrate a method incorporating several components of a systems biology approach to tackle this problem.  Genomic areas that correlated significantly with traits of Non-insulin-dependent Diabetes Mellitus (NIDDM, or Type 2 Diabetes) were reviewed and subjected to a SNP (single nucleotide polymorphism) analysis.  All variants between NIDDM models and wildtype controls were collected from the Mouse Phenome Database and evaluated for the presence of coding non-synonymous SNPs within exons (i.e. results in a change of the amino acid/peptide sequence).  Expression of the gene candidates was tested in Tallyho (NIDDM model) and C57Bl6 (wildtype) mice using PCR in 4 different tissues physiologically important for NIDDM phenotypes; skeletal muscle, liver, adipose and pancreas.  Results showed that several of the gene candidates were differentially expressed between the 4 tissues.  These results show how metagenomics can be used to identify variants of candidate genes that may significantly impact inherited multifactorial diseases.