Figure 1.
Workflow of the systems genetics approach for trans-eQTL identification.
The new systems genetics approach is based on four steps that are performed before the trans-eQTL analysis in order to efficiently reduce the number of analyzed genes: 1) Identification of the gene expression modules (M) that characterize CD4+ T cell gene expression 2) Enrichment analysis of a specific biological process that is related to the trait of interest 3) Construction of the functional-based networks using biological knowledge within the significantly enriched modules 4) Network analysis to identify those genes that are likely to play a central role in the functional-based networks 5) Trans-eQTL analysis using the subset of genes that show the highest centrality in each module. Abbreviations: AT, association transfer between organisms; BK, curated biological pathway knowledge; CP, computational predictions; G, gene; M, module; PI, physical interactions; TM, text-mining.
Figure 2.
Building of functional-based networks for M9 and M12 CD4+ gene expression modules using biological knowledge.
A: Functional-based network obtained from the immunologically enriched M9 gene expression module where 15 genes display a connected network. B: Functional-based network obtained from the immunologically enriched M12 gene expression module, which shows a complex network structure with different subnetworks involving 247 interconnected genes.
Table 1.
Gene expression modules identified in RA CD4+ T cells.
Figure 3.
Functional-based networks analyzed in each enriched CD4+ T cell gene expression module.
A: Functional-based network obtained from the immunologically enriched M9 gene expression module. B: Functional-based network obtained from the immunologically enriched M12 gene expression module. The dimensions of each node (i.e. gene) are proportional to its DC value and its color is based on its BC value, ranging from green (lowest BC values) to red (highest BC values). The edge width is proportional to the strength of the functional association evidences between two genes. The edge betweenness determines the edge color, ranging from green (edges connecting nodes with the lowest BC values) to red (edges connecting nodes with the highest BC values).
Table 2.
Network statistics of M9 and M12 RA CD4+ gene expression modules.
Table 3.
Trans-eQTL associations revealed by the application of the systems genetics approach.
Figure 4.
Transcript Complexity Value analysis of RA risk genes.
TCV from the RA CD4+ T cell and control LCLs analyzed in RA risk genes. A: JAK-STAT signaling pathway. B: T cell receptor signaling pathway. C: TNF signaling pathway. D: Cell adhesion pathway. E: Toll-like receptor signaling pathway. F: Antigen processing and presentation pathway. G: B cell development function pathway. The genes that are framed represent those genes showing significant genomic regulation profiles between RA CD4+ T cells and LCLs (P<0.05).
Figure 5.
Transcript Complexity Value analysis of T cell differentiation genes.
The type and quantity of significant eQTLs in RA CD4+ T cells and LCL cells are compared in the genes from the Th1, Th2, Th17 and Treg differentiation pathways. Only those genes showing a significantly different genomic regulation profile (P<0.05) are shown.