Infectious diseases continue to represent a threat to human health. Due to global warming and travel, newly emerging diseases are spreading at an unprecedented rate around the world. Examples are the dissemination of antibioticsresistant mycobacteria, the new swine influenza virus variant, SARS, and West Nile virus (WNV). Several research groups are using mouse GRPs to identify complex genetic influences on the host susceptibility to infections. GRPs have been and will continue to be an important basis for understanding infectious diseases in humans. A very good example for translational research was presented by Pascal Rihet, who identified genomic susceptibility regions to malaria in human populations in Africa (Delahaye et al. 2007 ) and then continued to compare these results with studies in mouse GRPs. In this way, a region on human chromosome 5 and its homologous regions on mouse chromosomes 11 and 18 were identified. Subsequent expression studies in mice will now help to determine the molecular networks and genes involved. Paul Denny described the mapping of genetic susceptibility to Streptococcus pneumonia infections in mouse inbred strains to chromosomes 7 (Denny et al. 2003) and 4 (unpublished) . Infection susceptibility to influenza was described by Klaus Schughart, who also pointed out that high susceptibility includes a hyperreactive immune response in the host (Srivastava et al. 2009 ). Xavier Montagutelli generated a unique resource of Mus spretus 9 C57BL/6 J interspecific recombinant congenic strains that carry different genomic fragments of Mus spretus on a C57BL/6 J background (Burgio et al. 2007 ). This GRP was used to identify resistance and susceptibility regions to various pathogens, including Rift Valley fever, West Nile virus, Yersinia pestis, and influenza. The first lines of the Collaborative Cross strains have been screened by Fuad Iraqi for a number of susceptibility loci to various pathogens (unpublished). It was remarkable to see that several quantitative trait loci (QTL) showed high significance and that the genomic intervals for several loci were very narrow, which should make it possible to identify quickly the underlying quantitative trait genes.