Complex multi-genetic diseases such as cardiovascular disease, autoimmune disorders, neurological disorders and metabolic diseases make up a majority of mortality and morbidity in developed countries and constitute many diseases of aging and of the elderly. The Genetic Association Database (GAD) (Becker et al. 2004) is a public repository of information from genetic association studies which archives published human disease association studies of all kinds with an emphasis on non-mendelian common disease. It currently contains approximately 130,000 disease and gene specific records, including information on over 3,300 unique genes, and over 6,900 unique disease phenotypic descriptions, including Alzheimers disease, autoimmune disease, autism, infection, sepsis, cardiovascular disorders, neurodegenerative disorders, and stroke;among many others. The GAD website is currently accessed worldwide by between 2,000 and 7,000 web hits per day. The goal of this effort is to archive, organize, and annotate published information on the genetics of common human diseases from published genetic association studies, including genome wide association studies (GWAS). The information we collect is published summary information and does not collect any information related to individual patients or family members. We are currently involved in systematic summation and analysis of the contents of this database. This involves summarizing the genetics of common disease with regard to gene based replication, comparisons to related and disparate diseases, as well as direct comparisons to the genetics of broad based mouse phenotypes. A part of this ongoing project involves integration of human and mouse genetic data with human and mouse microarray based gene expression data. Associated with this effort is the development of software tools to mediate data analysis and integration.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Scientific Cores Intramural Research (ZIC)
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National Institute on Aging
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De, Supriyo; Zhang, Yongqing; Wolkow, Catherine A et al. (2013) Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster. BMC Genomics 14:580
Gohlke, Julia M; Thomas, Reuben; Zhang, Yonqing et al. (2009) Genetic and environmental pathways to complex diseases. BMC Syst Biol 3:46