Meta-analysis approach identifies candidate genes and associated molecular networks for type-2 diabetes mellitus

Abstract

Background: Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide. The main goal of these studies is the detailed analysis of a particular aspect of the disease. Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches.

Results: Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM). Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes. The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study. Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction (128) of yet barely characterized novel candidate genes. Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks. Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any - yet non-associated - gene.

Conclusion: In our paper we have identified a core set of 213 T2DM candidate genes by a meta-analysis of existing data sources. We have explored the relation of these genes to disease relevant information and - using enrichment analysis - we have identified biological networks on different layers of cellular information such as signaling and metabolic pathways, gene regulatory networks and protein-protein interactions. The web interface is accessible via http://t2dm-geneminer.molgen.mpg.de.

Figure 1

Screen-shot of the web tool showing results on different genes with different amount of available information: (A) Adipoq, (B) Pdk4 and (C) Cfd.

Figure 2

Venn diagram of candidate gene lists. Abbreviations relate to the following references: TiffinHide2006 [22], DiabetesGenomeCG [23], ParikhGroop2004 [47] and LiuKasif2007 [24]. One gene in TiffinHide2006 has been neglected for the count since no transcriptional information was available for that gene. (*) Two genes are counted twice because the intersection of LiuKasif2007 and our study shares those genes with ParikhGroop2004.

Figure 3

Pathway crosstalk with respect to the T2DM candidate gene set. Pathways were derived from the KEGG database. Each pathway has been weighted according to the total disease score reflected by the size of the nodes. Only pathways with a total score > 20 were selected for display. The thickness of the edges between the different pathway nodes reflects the overlap score derived from the sum of the scores of the overlapping genes. The graph was generated with the graphviz package [62].

Figure 4

Scatterplot of the number of mouse protein interactions in IntAct and the T2DM gene score. The vertical red line indicates the significance cut-off value of the score. Mapk1 and Pik3r1 are highlighted as genes with more than 30 interactions.

Figure 5

Gene regulatory network composed of the significant genes. Significant TFs and TFs with enriched target sets with respect to the T2DM candidate gene list. Thick ends of the arrows point to TFs, thin ends point to target genes.

Figure 6

Histogram of gene scores (black line) and background distribution of scores derived from Bootstrap[26]sampling (blue line). The vertical red line marks the cut-off for the T2DM candidate gene list.