Genetic Hotspots for Disease Risk

疾病风险的遗传热点

• 批准号: 7908317
• 负责人: Joel S. Bader
• 金额: $ 15.07万
• 依托单位: FABRIC GENOMICS INC.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-07 至 2012-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7908317
• 关键词: Affect; Agreement; Alleles; Area; Automobile Driving; Biological Markers; Biological Process; Biotechnology; Blood Pressure; Cardiovascular Diseases; Cardiovascular system; Caring; Complex; Computer software; Data; Data Set; Databases; Diagnostic; Disease; Disease Marker; Disease susceptibility; Epidemiologic Studies; Familial disease; Family; Framingham Heart Study; Future; Genes; Genetic; Genetic Heterogeneity; Genetic Markers; Genetic Variation; Genome; Genotype; Goals; Health; Human; Hypertension; Individual; Inheritance Patterns; Intellectual Property; Lead; Left; Legal patent; Licensing; Link; Malignant Neoplasms; Measures; Medicine; Methods; Modeling; Molecular Diagnostic Testing; Pathway Analysis; Pathway interactions; Patients; Pattern; Performance; Phase; Policies; Population; Population Sizes; Predisposition; Prevalence; Prevention; Preventive; Privacy; Proxy; Public Domains; Public Health; Publishing; Reporting; Research Design; Research Personnel; Risk; Running; Simulate; Single Nucleotide Polymorphism; Small Business Innovation Research Grant; Symptoms; System; Technology; Testing; Thumb structure; Variant; Work; base; cardiovascular disorder risk; clinically relevant; database of Genotypes and Phenotypes; design; disorder risk; genetic analysis; genetic variant; genome sequencing; genome wide association study; genome-wide; high risk; human data; improved; insight; novel; novel marker; phase 1 study; public health relevance; statistics; success; therapeutic development; tool

DESCRIPTION (provided by applicant): Genetic Hotspots: The goal of this Phase 1 SBIR project is to develop and apply new methods to identify genes that harbor genetic variants that affect disease risk. The long-term objective of this work is to identify genes and gene variants that can improve human health by providing greater information about individual susceptibility to disease risk. The hypothesis driving this work is that multiple variants within a single gene may contribute independently to the risk of a single disease. This genetic heterogeneity is known to exist for familial diseases, and is anticipated for complex disorders. When individuals in a study may have one of a number of risk-enhancing alleles, this genetic heterogeneity decreases the power to detect such variants. The proposed work aims to increase the power of genome wide association studies (GWAS) to detect such genetic hotspots by developing new types of gene-based tests of association. Specific Aim 1 is to develop Bayesian regularization strategies that can reliably identify the correct model for a gene: the number of independent disease-linked variants it contains (0 for most genes), and the genotyped marker most highly correlated with each effect. The power of these methods to detect real associations will be compared to traditional SNP-based tests. Specific Aim 2 is to generalize these methods for application to actual, published genotype data sets where personal information has been censored for privacy concerns (such as the SHARe dataset from the Framingham Heart Study), leaving only summary p-values or regression statistics available to the public domain for analysis. Specific Aim 3 is to test the proposed gene-based methods in real data sets. If the proposed work in Phase 1 is successful, the Phase 2 aims will be to increase the computational efficiency, to develop related methods for genetic studies using ultra-high- throughput sequencing (also called Nextgen-sequencing methods) to analyze genetic variation, and to integrate these gene-based tests with pathway-based tests that require gene-specific p- values as input. The proposed methods have the potential to increase the ability to link specific genetic variants with disease risk, a critical step in predicting individual disease risk especially for new complete genome sequence data. In Phase 2 the developed methods will be integrated into the Genome Interpretation System, a commercial workflow software suite developed at Omicia. As such, it will serve as licensable commercial technology for the company by helping other biotechnology companies to develop their genetic biomarkers for diagnostic and therapeutic developments (theranostics). In addition, any novel variants drawn from this Phase 1 study will be licensable intellectual property, useful both as the basis for future products in our internal pipeline, as well as potentially valuable additions to our patent portfolio. PUBLIC HEALTH RELEVANCE: Genetic Hotspots: Project Narrative A single gene can have multiple independent variants that all contribute to risk for cardiovascular disease, cancer, or other complex disorders. Current genetic analysis methods focus on individual markers, usually single-nucleotide polymorphisms (SNPs), and are not designed to detect gene-based patterns. This proposal will develop new methods that are able to detect the presence of multiple independent risk-enhancing alleles within a gene, increasing the ability to predict individual risk for disease susceptibility. In Aim3 we will be testing the methods with respect to performance in "known" datasets with the focus in the area of cardiovascular disease (CVD). The improved methods will be used as part of the Omicia/s Genome Interpretation System (GIS) product pipeline, and can be licensed to third parties. In addition, any novel genetic markers identified as part of the Aim3 study will themselves be valuable additions to the Omicia product and IP portfolio. Omicia's goal is to provide content and analysis tools for molecular diagnostic tests for cardiovascular conditions, with the promise of identifying patients at high risk to enable them to begin preventive care before symptoms appear. Given the prevalence of CVD in the developed world, these products are potentially a great boon to public health, as well as being significant commercial opportunities.

描述（由申请人提供）： 基因热点：SBIR项目的第一阶段目标是开发和应用新方法来识别携带影响疾病风险的遗传变异的基因。这项工作的长期目标是通过提供有关个人对疾病风险易感性的更多信息来确定可以改善人类健康的基因和基因变异。推动这项工作的假设是，单个基因中的多个变异可能独立地导致单一疾病的风险。已知这种遗传异质性存在于家族性疾病中，并且预期存在于复杂疾病中。当研究中的个体可能具有许多风险增强等位基因之一时，这种遗传异质性降低了检测此类变体的能力。拟议的工作旨在通过开发新类型的基于基因的关联测试来增加全基因组关联研究（GWAS）检测此类遗传热点的能力。具体目标1是开发贝叶斯正则化策略，可以可靠地识别基因的正确模型：它包含的独立疾病相关变体的数量（大多数基因为0），以及与每个效应最高度相关的基因型标记。这些方法检测真实的关联的能力将与传统的基于SNP的测试进行比较。具体目标2是将这些方法推广应用于实际的、已发表的基因型数据集，其中个人信息已因隐私问题而被删失（例如来自Fracket Heart研究的SHARe数据集），只留下汇总p值或回归统计数据供公共领域分析。具体目标3是在真实的数据集中测试所提出的基于基因的方法。如果第一阶段的工作取得成功，第二阶段的目标将是提高计算效率，开发使用超高通量测序（也称为Nextgen测序方法）分析遗传变异的遗传研究的相关方法，并将这些基于基因的测试与需要基因特异性p值作为输入的基于路径的测试相结合。所提出的方法有可能提高将特定遗传变异与疾病风险联系起来的能力，这是预测个体疾病风险的关键步骤，特别是对于新的完整基因组序列数据。在第二阶段，开发的方法将被整合到基因组解释系统中，这是Omicia开发的商业工作流程软件套件。因此，它将作为该公司的可许可商业技术，帮助其他生物技术公司开发用于诊断和治疗开发（治疗诊断学）的遗传生物标志物。此外，从第一阶段研究中提取的任何新变体都将是可许可的知识产权，既可作为我们内部管道中未来产品的基础，也可作为我们专利组合的潜在有价值的补充。 公共卫生相关性： 基因热点：一个基因可以有多个独立的变异，所有这些变异都有助于心血管疾病，癌症或其他复杂疾病的风险。目前的遗传分析方法主要集中在单个标记上，通常是单核苷酸多态性（SNP），而不是设计用于检测基于基因的模式。该提案将开发新的方法，能够检测基因内多个独立的风险增强等位基因的存在，提高预测疾病易感性个体风险的能力。在Aim3中，我们将测试这些方法在“已知”数据集中的性能，重点是心血管疾病（CVD）领域。改进后的方法将作为Omicia基因组解释系统（GIS）产品线的一部分，并可授权给第三方。此外，作为Aim3研究的一部分，任何新的遗传标记本身都将成为Omicia产品和IP组合的宝贵补充。Omicia的目标是为心血管疾病的分子诊断测试提供内容和分析工具，并承诺识别高风险患者，使他们能够在症状出现之前开始预防护理。考虑到发达国家CVD的流行，这些产品对公共卫生可能是一个巨大的布恩，也是重要的商业机会。