Human Molecular Genetics Advance Access originally published online on March 13, 2009
Human Molecular Genetics 2009 18(11):2078-2090; 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, UK 3 Department of Neurology 4 Department of Biomedicine, University Hospital Basel, Basel, Switzerland 5 Department of Neurology, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
* To whom correspondence should be addressed at: Department of Neurology, School of Medicine, University of California San Francisco, 513 Parnassus Ave. Room S-256, San Francisco, CA 94143-0435, USA. Tel: +1 4155026865; Fax: +1 4154765229; Email: sebaran{at}cgl.ucsf.edu
Received November 19, 2008; 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.