SLC11A1, VDR, MAL/TIRAP, and CCL2. Further studies will be needed to determine genetic susceptibility to other infectious diseases and ways public health officials can prevent and test for these infections to enhance the concept of personalized medicine. Type 1 Diabetes, immunomics, and public health The term genomics, referring to the organism's whole genome, is also used to refer to gene informatics, or the collection and storage of genetic data, including the functional information associated with the genes, and the analysis of the data as combinations, patterns and networks by computer algorithms. Systems biology and genomics are natural partners, since the development of genomic information and systems naturally facilitates analysis of systems biology questions involving relationships between genes, their variants (SNPs) and biological function. Such questions include the investigation of signaling pathways, evolutionary trees, or biological networks, such as immune networks and pathways. For this reason, genomics and these approaches are particularly suited to studies in immunology. The study of immunology using genomics, as well as proteomics and transcriptomics (including gene profiles, either genomic or expressed gene mRNA profiles), has been termed immunomics. Accurate and sensitive prediction of disease, or detection during early stages of disease, could allow the prevention or arrest of disease development as immunotherapy treatments become available. Type-1 diabetes markers associated with disease susceptibility have been identified, for example HLA class II gene variants, however possession of one or more of these genomic markers does not necessarily lead to disease. Lack of progression to disease is likely due to the absence of environmental triggers, absence of other susceptibility genes, presence of protective genes, or differences in the temporal expression or presence of these factors. Combinations of markers have also been associated with susceptibility to type-1 diabetes however again, their presence may not always predict disease development, and conversely, disease may be present without the marker group. Potential variant genes (SNPs) or markers that are linked to the disease include genes for cytokines, membrane-bound ligands, insulin and immune regulatory genes. Meta-analyses have been able to identify additional associated genes, by pooling a number of large gene datasets. This successful study illustrates the importance of compiling and sharing large genome databases. The inclusion of phenotypic data in these databases will enhance discovery of candidate genes, while the addition of environmental and temporal data should be able to advance the disease progression pathways knowledge. HUGENet, which was initiated by the Centers