Bioinformatic Analysis of the Genetics of Common Complex Diseases

心血管疾病、自身免疫性疾病、神经系统疾病和代谢性疾病等复杂的多遗传疾病构成发达国家死亡率和发病率的大部分，并构成许多老龄和老年人疾病。遗传协会数据库（GAD） （Becker et al. 2004）是遗传协会研究的公共信息库，其中存档了已发表的各种人类疾病协会研究，重点是非孟德尔常见病。它目前包含大约130,000种疾病和基因特定记录，包括超过3,300种独特基因的信息，以及超过6,900种独特疾病表型描述，包括阿尔茨海默病，自身免疫性疾病，自闭症，感染，败血症，心血管疾病，神经退行性疾病和中风；还有很多人。GAD网站目前每天在全球范围内的访问量在2000到7000之间。http://geneticassociationdb.nih.gov/

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. http://geneticassociationdb.nih.gov/ 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. This is done in cooperation with CDC Human Genome Epidemiology Network and the HuGE Navigator database. 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.