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，用于确定易患多基因疾病或预防多基因疾病的潜在因素。该算法依赖于对可能的遗传变异空间的马尔可夫链蒙特卡罗探索，以及基于表型等级的贝叶斯统计检验。开发的算法将提高我们从不断增长的基因型别和单核苷酸多态数据库中减少复杂遗传交互作用的能力。识别相互作用的遗传变异，使个人面临多基因疾病的风险或提供保护，使其免受多基因疾病的发展，这对我们对这些疾病的理解和开发新疗法的能力来说仍然是一个问题。从根本上说，这个问题涉及到，面对各种基因组计划和高通量单核苷酸多态(SNP)和基因分析带来的基因组学知识的巨大增长，标准统计方法无法实现强大的能力。类似的“维度灾难”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)技术相结合已经导致了理解的显著改进，这导致了我们在这里关注这一技术。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、心脏病和II型糖尿病。一种分析易患多基因疾病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示文稿在 会议。我们还将通过联系基金会中的个人专注于特定复杂疾病的研究，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该算法。我们希望将算法模块化，允许更容易地包含不同的先前分发版本，并实现更友好的界面，从而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.