This paper uses original survey data from the 2010 UK General Election to examine two central questions about e-participation: how people engaged with the online campaign; and whether any mobilization effects of e-campaigning activity can be detected in terms of increasing individuals’ likelihood of participating after the election. Following our previous work, were-test and confirm a measurement model of e-campaign activities. Through exploratory factor analyses, we identify our three dimensions of e-campaign participation: e-information, e-party and e-expressive. We then show how these activities differentially predict subsequent online political participation: e-donate, e-contact, e-petition and e-discuss. We do this using a panel study design that allows us to impose robust controls on pre-existing levels of political engagement. Our results show that for the most part levels of pre-election engagement in e-donating, e-contacting and e-petitioning explain much of the post-election commitment to do so, and that online campaign involvement does not add significantly to this intention. However we have found that use of the internet during the campaign to obtain information does appear to have a lasting effect on the likelihood of discussing politics, even when prior propensity toward discussion is controlled for. We have further observed a significant negative association between engagement in formal e-campaign activities (e-party) and a later proclivity to take part in a direct democracy initiative online (e-petition). Our findings are in line with a growing conclusion that the internet’s role in stimulating participation is likely to be more complex than a simple direct effect andsupport the claim for adopting a more nuanced approach to the analysis of the mobilization effects.

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.