Alzheimer's disease (AD) is the most prevalent and rapidly increasing neurodegenerative disorder in the elderly, with no effective therapy. Complete understanding of the biological basis of Alzheimer's disease is the key for early diagnosis and intervention. Recent applications of high throughput technologies, e.g. genome-wide genetic analyses and expression profiling, have obtained some insights of the genetic and molecular mechanisms underlying the disease. With the technology develops, the data throughput grows. We are now in the "Big Data" time ---- omics for complex disease. However, it is elusive to translate the big data to reliable knowledge. Here, we developed a one-stop database called AlzData, to make a full collection of current high-throughput omics data. What's more, AlzData could serve as an in-depth integrating system to integrate data of different levels, to generate a prioritized gene list for further characterization.
    AlzData will cover: 1) high-throughput omic data, e.g. Genomics (GWAS and Whole Exome Sequencing), Transcriptomes, Proteomics, and Functional genomics;   2) high-confident functional data, e.g. neuroimaging screening, population-based longitudinal studies, and transgenic mouse phenotyping.
    Currently, we provide a searchable & downloadable web entrance for results of normalized brain gene expression profiling and whole exome sequencing. Other data is under continuously updating. Submission of any high-throughput data relevant to AD to our database is welcome.
      Cite us: Xu M, Zhang D-F et al., 2018. A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized hub genes as important upstream regulators in Alzheimer's disease. Alzheimer's & Dementia, 14: 215 - 229.
               Zhang D-F, Fan Y, and Xu M et al., 2019. Complement C7 is a novel risk gene for Alzheimer's disease in Han Chinese.National Science Review, 6: 257 - 274.