Natural resistance to Meningococcal Disease related to CFH loci: Meta-analysis of genome-wide association studies

Meningococcal disease (MD) remains an important infectious cause of life threatening infection in both industrialized and resource poor countries. Genetic factors influence both occurrence and severity of presentation, but the genes responsible are largely unknown. We performed a genome-wide association study (GWAS) examining 5,440,063 SNPs in 422 Spanish MD patients and 910 controls. We then performed a meta-analysis of the Spanish GWAS with GWAS data from the United Kingdom (combined cohorts: 897 cases and 5,613 controls; 4,898,259 SNPs). The meta-analysis identified strong evidence of association ($P$-value≤5×10$^{−8}$) in 20 variants located at the $\textit{CFH}$ gene. SNP rs193053835 showed the most significant protective effect (Odds Ratio (OR)=0.62, 95% confidence interval (C.I.)=0.52–0.73; $P$-value=9.62×10$^{−9}$). Five other variants had been previously reported to be associated with susceptibility to MD, including the missense SNP rs1065489 (OR=0.64, 95% C.I.)=0.55–0.76, $\textit{P-value}$=3.25×10$^{−8}$). Theoretical predictions point to a functional effect of rs1065489, which may be directly responsible for protection against MD. Our study confirms the association of $\textit{CFH}$ with susceptibility to MD and strengthens the importance of this link in understanding pathogenesis of the disease.This study received support from the Instituto de Salud Carlos III (Proyecto de Investigación en Salud, Acción Estratégica en Salud: proyecto GePEM PI16/01478) (A.S.); Instituto Carlos III (Intensificación de la actividad investigadora) (A.V.); Consellería de Sanidade, Xunta de Galicia (RHI07/2-intensificación actividad investigadora, PS09749 and 10PXIB918184PR), Instituto de Salud Carlos III (Intensificación de la actividad investigadora 2007–2012, PI16/01569), Convenio de colaboración de investigación (Wyeth España-Fundación IDICHUS 2007–2011), Convenio de colaboración de investigación (Novartis España-Fundación IDICHUS 2010–2011), Fondo de Investigación Sanitaria (FIS; PI070069/PI1000540) del plan nacional de I+ D+ I and ‘fondos FEDER’ (F.M.T.). More information at: www. esigem.org. The UK cohort was established with support of the Meningitis Research Foundation (UK), who provide ongoing support, and the European Society for Paediatric Infectious Diseases supported the establishment of the international collaboration. This study makes use of data generated by the Wellcome Trust Case-Control Consortium 2. A full list of the investigators who contributed to the generation of the data is available from www. wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 085475. The research leading to these results has received funding from the European Union’s Seventh Framework Programme under EC-GA No. 279185 (EUCLIDS)

Optimizing edge weights for distributed inference with Gaussian belief propagation

Distributed processing is becoming more important in robotics as low-cost ad hoc networks provide a scalable and robust alternative to tradition centralized processing. Gaussian belief propagation (GaBP) is an effective message-passing algorithm for performing inference on distributed networks, however, its accuracy and convergence can be significantly decreased as networks have higher connectivity and loops. This paper presents two empirically derived methods for weighting the messages in GaBP to minimize error. The first method uses uniform weights based on the average node degree across the network, and the second uses weights determined by the degrees of the nodes at either end of an edge. Extensive simulations show that this results in greatly decreased error, with even greater effects as the network scales. Finally, we present a practical application of this algorithm in the form of a multi-robot localization problem, with our weighting system improving the accuracy of the solution