title: Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes.
abstract: BACKGROUND
  Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D). However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes. There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.
  
  
  METHODS
  We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes. Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. We then studied the relationship between these T2D genes and curated gene sets.
  
  
  RESULTS
  We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20. Using the T2D genetic network, we identified highly-interconnected network "hub" genes. By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories. We found that highly-interconnected T2D disease network "hub" genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1. The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list. Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer. We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport. Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.
  
  
  CONCLUSIONS
  Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors. T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks. The framework developed for T2D studies may serve as a guide for studying other complex diseases.
authors.affiliation: School of Informatics, Indiana University-Purdue University, Indianapolis, IN, USA.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Hale
authors.forename: Alfredo M
authors.initials: AM
authors.lastname: López-Yunez
authors.forename: Jake Y
authors.initials: JY
authors.lastname: Chen
mesh_terms: D000483:Alleles; D002877:Chromosomes, Human / Q000235:genetics; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D015870:Gene Expression; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012307:Risk Factors; D049490:Systems Biology; D058118:Transcription Factor 7-Like 2 Protein / Q000235:genetics / Q000378:metabolism
chemical_list: C544894:TCF7L2 protein, human; D058118:Transcription Factor 7-Like 2 Protein