Analysis Tool for Heritable and Envirnonmental Network Associations

描述（由申请人提供）： 人类基因组计划的努力正开始为人类健康提供重要的发现。实验室的技术进步，特别是在表征人类基因组变异方面，为研究人类基因组创造了新的方法。然而，目前的统计和计算策略只利用了这一丰富信息的一部分。在寻找常见、复杂疾病的易感基因方面，我们面临着许多挑战。随着高通量数据生成技术的发展，选择对感兴趣性状重要的遗传、临床和环境因素变得越来越困难。我们知道基因不会孤立地起作用，因此许多其他因素在复杂的疾病表型中可能很重要。然而，用于重要变量的稳健统计建模以预测临床结果的技术在其相互作用效应的能力方面是有限的。最终，我们想知道哪些因素对于提供人类疾病的上级预防、诊断和治疗是重要的。不幸的是，统计模型的解释，在生物医学研究的一个有意义的方式一直缺乏由于固有的困难，使这种连接。因此，一项技术，包括人类疾病的复杂性，并通过一个强大的分析框架，从公共领域的生物学知识，整合多个数据源，是解剖常见疾病的架构是必不可少的。- 你好遗传和环境网络关联分析工具是一个新颖的框架，它结合了变量选择、建模和解释，以了解更多关于公共卫生感兴趣的疾病。随着该领域在分析大规模基因组数据方面获得经验，我们相互学习并制定和编纂最佳策略至关重要。

DESCRIPTION (provided by applicant): The efforts of the human genome project are beginning to provide important findings for human health. Technological advances in the laboratory, particularly in characterizing human genomic variation, have created new approaches for studying the human genome. However, current statistical and computational strategies are taking only partial advantage of this wealth of information. In the quest for disease susceptibility genes for common, complex disease, we are faced with many challenges. Selecting genetic, clinical, and environmental factors important for the trait of interest is increasingly more difficult as high throughput data generation technologies are developed. We know that genes do not act in isolation, thus numerous other factors are likely important in complex disease phenotypes. However, techniques for robust statistical modeling of important variables to predict clinical outcomes are limited in their capability for interaction effects. Ultimately, we want to know what factors are important to provide superior prevention, diagnosis, and treatment of human disease. Unfortunately, interpretation of statistical models in a meaningful way for biomedical research has been lacking due to the inherent difficulty in making such connections. Thus, a technology that embraces the complexity of human disease and integrates multiple data sources including biological knowledge from the public domain, through a powerful analytical framework is essential for dissecting the architecture of common diseases. ATHENA: the Analysis Tool for Heritable and Environmental Network Associations is a novel framework that incorporates variable selection, modeling, and interpretation to learn more about diseases of public health interest. As the field gains experience in analyzing large scale genomic data, it is crucial that we learn from each other and develop and codify the best strategies.