Identifying Genetic Factors for Predisposition in Polygenic Diseases

描述（由申请人提供）：该提案侧重于开发一种算法，用于确定导致多基因疾病易感性或保护的潜在因素。该算法依赖于马尔可夫链蒙特卡罗探索的空间可能的遗传变异耦合到贝叶斯统计测试的基础上表型排名。开发的算法将提高我们的能力，以减少复杂的遗传相互作用，从不断增长的基因型和单核苷酸多态性数据库。鉴定相互作用的遗传变异使个体处于多基因疾病的风险中或提供保护以免受多基因疾病的发展仍然是我们对这些疾病的理解和我们开发新治疗方法的能力的问题。从根本上说，这个问题涉及到标准的统计方法，以实现权力，在我们的基因组学知识的巨大增长，所带来的各种基因组计划和高通量单核苷酸多态性（SNP）和基因型分析。类似的“维数灾难”问题也出现在其他领域，贝叶斯统计方法与马尔可夫链蒙特卡罗（MCMC）技术相结合，导致了理解的显着改善，这导致了我们在这里对这种技术的关注。由于多基因疾病比单基因疾病广泛得多，对健康的潜在影响是巨大的，许多常见疾病被认为具有多基因基础，包括肥胖症，心脏病和II型糖尿病。一种剖析多基因疾病易感性或保护多基因疾病的复杂遗传相互作用的方法将对改善健康产生重大影响。我们将通过出版物和演示文稿传播该算法， 两会我们还将通过联系专注于特定复杂疾病研究的基金会的个人，使算法能够对健康产生最大的影响。在这个项目成功完成后，我们计划更全面地开发算法。我们想模块化的算法，允许更容易地包括不同的先验分布，并实现一个更友好的接口，能够利用新兴的生物信息学标准进行数据交换。

DESCRIPTION (provided by applicant): This proposal focuses on the development of an algorithm for determining the underlying factors responsible for predisposition to or protection from polygenic diseases. The algorithm relies on Markov chain Monte Carlo exploration of the space of possible genetic variants coupled to a Bayesian statistical test based on phenotypic ranks. The developed algorithm will improve our ability to reduce complex genetic interactions from the growing genotypic and single nucleotide polymorphism databases. The identification of interacting genetic variants placing individuals at risk for or providing protection from the development of polygenic diseases remains a problem both for our understanding of these diseases and for our ability to develop new treatments. Fundamentally, the problem involves the inability of standard statistical approaches to achieve power in the face of the enormous growth in our knowledge of genomics, brought about by the various genome projects and high throughput single nucleotide polymorphism (SNP) and genotype analyses. Similar "curse of dimensionality" problems have arisen in other fields, and Bayesian statistical approaches coupled to Markov chain Monte Carlo (MCMC) techniques have led to significant improvements in understanding, which has led to our focus on this technique here. Because polygenic diseases are much more widespread than single gene diseases, the potential impact on health is substantial, and many common diseases are believed to have a polygenic basis, including obesity, cardiac disease, and Type II diabetes. A method to dissect the complex genetic interactions underlying predisposition to or protection from polygenic diseases would have a substantial effect on improving health. We will disseminate the algorithm through publications and presentations at conferences. We will also through contact individuals in foundations focused on research in specific complex diseases, so that the algorithm can have the maximal impact on health. Upon successful completion of this project, we plan to develop the algorithm more fully. We would like to modularize the algorithm, allowing easier inclusion of different prior distributions and implement a more friendly interface with the ability to utilize the emerging bioinformatics standards for data exchange.