Identifying Genetic Factors for Predisposition in Polygenic Diseases

确定多基因疾病易感性的遗传因素

• 批准号: 7220047
• 负责人: Michael F Ochs
• 金额: $ 8.3万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-10 至 2009-04-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7220047
• 关键词: Address; Algorithms; Alleles; Bioinformatics; Code; Complex; Coupled; Data; Data Set; Databases; Development; Disease; Documentation; Face; Foundations; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Growth; Health; Heart Diseases; Individual; Informatics; Knowledge; Link; Manuscripts; Markov Chains; Measures; Methods; Multiple Sclerosis; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Phenotype; Play; Population; Predisposition; Probability; Publications; Research; Resistance; Risk; Role; Single Nucleotide Polymorphism; Space Explorations; Standards of Weights and Measures; Techniques; Testing; Variant; Work; base; design; genetic variant; improved; programs; protective effect; symposium

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.

描述（由申请人提供）：该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预防多基因疾病的潜在因素。该算法依赖于对可能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测试。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。鉴定使个体面临多基因疾病发展风险或提供保护以免受多基因疾病发展的相互作用的遗传变异仍然是我们对这些疾病的理解和开发新疗法的能力的一个问题。从根本上说，问题在于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，标准统计方法无法发挥作用。类似的“维数灾难”问题在其他领域也出现了，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上的显着提高，这导致我们在这里重点关注这项技术。由于多基因疾病比单基因疾病广泛得多，因此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。剖析多基因疾病易感性或预防多基因疾病背后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来传播该算法 会议。我们还将通过联系专注于特定复杂疾病研究的基金会中的个人，使算法能够对健康产生最大的影响。成功完成该项目后，我们计划更全面地开发该算法。我们希望模块化算法，以便更容易地包含不同的先验分布，并实现更友好的界面，能够利用新兴的生物信息学标准进行数据交换。