Human Molecular Genetics Advance Access published online on March 13, 2009
Human Molecular Genetics, doi:10.1093/hmg/ddp120
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Pathway and network-based analysis of genome-wide association studies in multiple sclerosis
1 Department of Neurology, UCSF, San Francisco, CA, USA 2 GlaxoSmithKline Research and Development, Harlow, England 3 Departments of Neurology and Biomedicine, University Hospital Basel, Basel, Switzerland 4 Department of Neurology, Vrije Universiteit Medical Center, Amsterdam, Netherlands
* Corresponding author Sergio E. Baranzini, Ph.D., Department of Neurology, School of Medicine, University of California San Francisco, 513 Parnassus Ave. Room S-256, San Francisco, CA 94143-0435, TEL: +1-415-502-6865 FAX: +1-415-476-5229 Email: sebaran{at}cgl.ucsf.edu
Received November 19, 2008; Revised March 11, 2009; Accepted March 11, 2009
Genome-wide association studies (GWAS) testing several hundred thousand SNPs have been performed in multiple sclerosis (MS) and other complex diseases. Typically, the number of markers in which the evidence for association exceeds the genome-wide significance threshold is very small, and markers that do not exceed this threshold are generally neglected. Classical statistical analysis of these datasets in MS revealed genes with known immunological functions. However, many of the markers showing modest association may represent false negatives. We hypothesize that certain combinations of genes flagged by these markers can be identified if they belong to a common biological pathway. Here we conduct a pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (p<0.05). Gene-wise p-values were superimposed on a human protein interaction network and searches were conducted to identify sub-networks containing a higher proportion of genes associated with MS than expected by chance. These sub-networks, and others generated at random as a control, were categorized for membership of biological pathways. GWAS from eight other diseases were analyzed to assess the specificity of the pathways identified. In the MS datasets we identified sub-networks of genes from several immunological pathways including cell adhesion, communication, and signaling. Remarkably, neural pathways, namely axon-guidance and synaptic potentiation, were also over-represented in MS. In addition to the immunological pathways previously identified, we report here for the first time the potential involvement of neural pathways in MS susceptibility.