Identifying Genetic Factors for Predisposition in Polygenic Diseases

描述（由申请人提供）：本提案的重点是开发一种算法，用于确定导致多基因疾病易感性或保护多基因疾病的潜在因素。该算法依赖于马尔可夫链蒙特卡罗对可能遗传变异空间的探索，并结合基于表型排名的贝叶斯统计检验。开发的算法将提高我们从不断增长的基因型和单核苷酸多态性数据库中减少复杂遗传相互作用的能力。识别相互作用的基因变异，使个体处于多基因疾病的风险或提供保护，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一个问题。从根本上说，这个问题涉及到标准统计方法在面对各种基因组计划和高通量单核苷酸多态性（SNP）和基因型分析带来的基因组学知识的巨大增长时无法发挥作用。类似的“维数诅咒”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，在理解方面取得了重大进展，这导致了我们在这里关注该技术。由于多基因疾病比单基因疾病更为普遍，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、心脏病和II型糖尿病。一种分析多基因疾病易感性或预防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讲来传播该算法

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.