Data Integration and Disease Risk Modeling in the Context of Molecular Networks

分子生物学的进步使得高通量技术的发展成为可能， 分析基因表达、大量单核苷酸多态性（SNP）、蛋白质， 代谢谱等已经彻底改变了我们理解复杂的人类疾病的生物学基础的能力。 紊乱因此，我们现在已经开发出可以预测治疗的基因表达谱， 结果，并确定了与复杂人类疾病可重复相关的SNP， 在以前几乎是棘手的。但是，我们很难对我们取得的进展感到满意， 对于常见的疾病是如何产生和发展的，我们仍然有很多不知道或不理解的地方。 我们认为，我们可以从对所有信息的系统组织中学到更多的东西 通过对基因表达和DNA多态性的全基因组询问而发展起来的 赞赏.因此，我们建议将我们的专业知识集中在统计方法和算法上，用于挖掘 从全基因组平台产生的数据，以更好地了解 精神病表型我们将通过一个动态的数据采集、整合、 表型解构，并开发数据库，软件和相关的网络浏览器， 应该提供下一个逻辑步骤，合并已经开始从 大通量基因分型、测序、比较基因组杂交和表达技术。 数据库将包含所有已知遗传变异的详细注释（包括 位置，保护和本地重组率，人口特征，如频率和 选择的证据，以及关于临床和表达表型的关联数据），以及所有基因的 （包括位置和内部变体的一般特征，与 基因，以及与该基因相关的已知SNP和表型）。该项目将建立在一个 芝加哥大学现有的努力称为SCAN（SNP和CNV注释网络，www.scandb.org）。

Advances-in molecular biology that have enabled the development of high throughput technologies for assaying gene expression, very large numbers of single nucleotide polymorphisms (SNPs), proteins, metabolic profiles etc. have revolutionized our ability to understand the biological basis of complex human disorders. As a consequence, we have now developed gene expression profiles that can predict treatment outcomes, and identified SNPs that are reproducibly associated with complex human diseases, findings that had previously been all but intractable. But it is difficult to be satisfied with the progress we have made when there is still so much that we do not know or understand about how common disorders arise and develop. We believe that we can learn much more from the systematic organization of the totality of the information developed through genome-wide interrogation of gene expression and DNA polymorphism than we have yet appreciated. Thus we propose to focus our expertise in statistical methods and algorithms for mining the data generated from genome-wide platforms toward a better understanding of the molecular architecture of psychiatric phenotypes. We will achieve this through a dynamic process of data acquisition, integration, phenotype deconstruction, and the development of a database, software and associated web browser that should provide the next logical step in merging the information that has started to become available from large throughput genotyping , sequencing, comparative genomic hybridization, and expression technology. The database will contain detailed annotation of all known genetic variants (including general information on location, conservation and local recombination rates, population characteristics such as frequency and evidence for selection, as well as association data on clinical and expression phenotypes), and of all genes (including general characteristics of location and variants within, information on pathways associated to the gene, as well as known SNPs and phenotypes associated with the gene). This project will build on an existing effort at University of Chicago called SCAN (SNP and CNV Annotation Network, www.scandb.org).