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. 2013 Jan;3(1):119-29.
doi: 10.1534/g3.112.004788. Epub 2013 Jan 1.

Systems-level analysis of genome-wide association data

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Systems-level analysis of genome-wide association data

Charles R Farber. G3 (Bethesda). 2013 Jan.

Abstract

Genome-wide association studies (GWAS) have emerged as the method of choice for identifying common variants affecting complex disease. In a GWAS, particular attention is placed, for obvious reasons, on single-nucleotide polymorphisms (SNPs) that exceed stringent genome-wide significance thresholds. However, it is expected that many SNPs with only nominal evidence of association (e.g., P < 0.05) truly influence disease. Efforts to extract additional biological information from entire GWAS datasets have primarily focused on pathway-enrichment analyses. However, these methods suffer from a number of limitations and typically fail to lead to testable hypotheses. To evaluate alternative approaches, we performed a systems-level analysis of GWAS data using weighted gene coexpression network analysis. A weighted gene coexpression network was generated for 1918 genes harboring SNPs that displayed nominal evidence of association (P ≤ 0.05) from a GWAS of bone mineral density (BMD) using microarray data on circulating monocytes isolated from individuals with extremely low or high BMD. Thirteen distinct gene modules were identified, each comprising coexpressed and highly interconnected GWAS genes. Through the characterization of module content and topology, we illustrate how network analysis can be used to discover disease-associated subnetworks and characterize novel interactions for genes with a known role in the regulation of BMD. In addition, we provide evidence that network metrics can be used as a prioritizing tool when selecting genes and SNPs for replication studies. Our results highlight the advantages of using systems-level strategies to add value to and inform GWAS.

Keywords: coexpression network; genome-wide association study (GWAS); osteoporosis; systems biology.

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Figures

Figure 1
Figure 1
Overview of the systems-level analysis of GWAS data.
Figure 2
Figure 2
WGCNA coexpression network composed of BMD GWAS genes. Shown is the hierarchical clustering dendogram for all 1918 genes used in the analysis. Each line is an individual gene. Genes were clustered based on a dissimilarity measure (1 − TOM). The branches correspond to modules of highly interconnected groups of genes. The tips of the branches represent genes that are the least dissimilar and thus share the most similar network connections. Below the dendogram each gene is color coded to indicate its module assignment.
Figure 3
Figure 3
Network view of the turquoise module reveals a submodule of genes negatively correlated with BMD status. This network contains all turquoise module edges with TOM ≥ 0.15 and their corresponding nodes. Genes are shaded based on their correlation with BMD from white (no correlation) to dark green (strong negative correlation). Node sizes are proportional to each gene’s –log10 GWAS P (most significant unadjusted GWAS P-value for either HBMD or SBMD). The submodule of interest is on the right-hand side of the figure. Notice that this group of gene is highly interconnected and negatively correlated with BMD status.
Figure 4
Figure 4
Characterizing the coexpression relationships for a highly connected known BMD gene. This TNF centered network provides a view of all edges and their corresponding nodes connected to TNF with a TOM ≥ 0.15. Genes are color coded based on their correlation with BMD; white (−0.20 < r<0.20), blue (r ≥ 0.20), and yellow (r≤-0.20). Node sizes are proportional to each gene’s –log10 GWAS P (most significant unadjusted GWAS P-value for either HBMD or SBMD).
Figure 5
Figure 5
Correlation between MM and GS for each of the 13 distinct GWAS modules. MM (defined as the correlation between each gene’s expression and its module eigengene) for each module is plotted against GS (defined as each gene’s correlation with BMD status). MM in the blue, magenta, greenyellow and brown modules is significantly (P < 0.003) correlated with GS.

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References

    1. Alter O., Brown P. O., Botstein D., 2000. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97: 10101–10106 - PMC - PubMed
    1. Altshuler D., Daly M. J., Lander E. S., 2008. Genetic mapping in human disease. Science 322: 881–888 - PMC - PubMed
    1. Askland K., Read C., Moore J., 2009. Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Hum. Genet. 125: 63–79 - PubMed
    1. Baranzini S. E., Galwey N. W., Wang J., Khankhanian P., Lindberg R., et al. , 2009. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum. Mol. Genet. 18: 2078–2090 - PMC - PubMed
    1. Blair H. C., 1998. How the osteoclast degrades bone. Bioessays 20: 837–846 - PubMed

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