Software Relating Genes to Disease and Clinical Outcomes

将基因与疾病和临床结果相关的软件

• 批准号: 6693828
• 负责人: Christophe G. Lambert
• 金额: $ 41.65万
• 依托单位: GOLDEN HELIX, INC.
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2005-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6693828
• 关键词: artificial intelligence; computer data analysis; computer program /software; computer system design /evaluation; drug adverse effect; drug screening /evaluation; gene environment interaction; health care facility information system; molecular biology information system; statistics /biometry

DESCRIPTION (provided by applicant): The development of a software system is proposed that will combine statistical theory, computer science algorithms, and genetics expertise to take advantage of the great influx of data generated by the study of the human genome, clinical trials data and the creation of inexpensive genotyping techniques. This software will elucidate the complex relationship between drug efficacy and side effects, multiple interacting genes and environmental factors. Our Phase I results show it is feasible to link phenotype to genotype for a list of "candidate" genes. A novel haplotype trend test has been developed to aid in finding associations across large SNP maps. Commercialization of this technique is essential for companies that intend to use large public or private SNP maps to locate genes that are associated with disease and drug safety and efficacy. Our statistical methods are expected to be successful even if the disease mechanism can differ from one person to another. By analyzing and interpreting clinical trial data, the software will match drugs to target populations according to their specific genotype. This will enable pharmaceutical companies to create novel drugs that render maximum effectiveness and have minimum side effects, i.e. the right drug for the right person.

描述（由申请人提供）：建议开发一个软件系统，该系统将结合统计理论、计算机科学算法和遗传学专业知识，以利用人类基因组研究、临床试验数据和基因组研究产生的大量数据。创建廉价的基因分型技术。该软件将阐明药物疗效和副作用、多个相互作用的基因和环境因素之间的复杂关系。 我们的第一阶段结果表明，将表型与基因型联系起来以获得“候选”基因列表是可行的。一种新颖的单倍型趋势测试已被开发出来，以帮助寻找大型 SNP 图谱之间的关联。对于打算使用大型公共或私人 SNP 图谱来定位与疾病和药物安全性和功效相关的基因的公司来说，这项技术的商业化至关重要。即使每个人的疾病机制可能不同，我们的统计方法也有望取得成功。 通过分析和解释临床试验数据，该软件将根据目标人群的特定基因型将药物与目标人群相匹配。这将使制药公司能够创造出具有最大功效和最小副作用的新药，即对合适的人使用合适的药物。