A novel pattern-based framework for genetic analysis

一种新颖的基于模式的遗传分析框架

• 批准号: 6645126
• 负责人: ZHONG LI
• 金额: $ 10万
• 依托单位: FIRST GENETIC TRUST, INC.
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-03-01 至 2004-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6645126
• 关键词: computer data analysis; computer program /software; computer simulation; computer system design /evaluation; genetic mapping; genetic markers; genetic susceptibility; intermolecular interaction; mathematical model; mathematics; statistics /biometry

DESCRIPTION (provided by applicant): Genetic dissection of complex trait has become one of the most important genetic research topics in recent years, because of its healthcare implications. Although genome-wide association studies are thought to hold the promise to identify susceptibility genes responsible to complex diseases, methodologies to take full advantage of the genotyping data are still lacking. Standard DNA marker-based approaches typically only consider a limited number of hypotheses, most of which are on the effect of a single locus or a relative few loci, therefore do not accommodate the full range of genetic mechanisms that may contribute to the disease phenotype. First Genetic Trust Inc (FGT) is developing an innovative data analysis method to map complex traits that consider all possible genetic mechanisms. Tailored to family-based and population-based association studies, this method utilize a novel pattern discovery-based approach and a collection of theoretically and empirically derived statistics to identify multi-locus disease associations. Instead of assuming a few possible genetic models, this method considers correlations among multiple markers at genome scale and allows detection of complex genetic models of inheritance for a disease. As the result, it could have significant more power than conventional single-locus analysis methods. The proposed research will investigate whether this pattern discovery-based approach is able to detect known susceptibility loci for a disease, and whether other susceptibility loci and interactions among susceptibility loci can be discovered and confirmed. In this research proposal, the novel method as well as several conventional genetic analysis methods will be applied to real dataset collected from association study for comparison. Detailed genomic database search on identified susceptibility loci will provide biological insight to validate results. Upon the validation of this method, FGT will develop a software package including the novel algorithm and the corresponding statistical framework and use it to support its clinical genetic discovery services. If indeed the utility of this pattern-based multi-locus analysis method is confirmed by the proposed research and subsequent follow-ups, this work will open up a completely new direction in the hunt for the genes responsible for common diseases that are complex and heterogeneous in nature.

描述(申请人提供)：复杂性状的遗传解剖已成为近年来最重要的遗传学研究课题之一，因为它具有保健意义。尽管全基因组关联研究被认为有望识别导致复杂疾病的易感基因，但仍缺乏充分利用基因分型数据的方法。标准的基于DNA标记的方法通常只考虑有限数量的假设，其中大多数是关于单个基因座或相对少数几个基因座的影响，因此不能适应可能导致疾病表型的全部遗传机制。First Genetic Trust Inc(FGT)正在开发一种创新的数据分析方法，以绘制考虑所有可能的遗传机制的复杂特征图。这种方法是为基于家庭和基于人群的关联研究量身定做的，它利用一种新的基于模式发现的方法和一组理论和经验派生的统计数据来识别多基因座疾病关联。这种方法不是假设几个可能的遗传模型，而是考虑基因组水平上多个标记之间的相关性，并允许检测疾病遗传的复杂遗传模型。因此，它可能比传统的单基因座分析方法具有更大的能力。这项拟议的研究将调查这种基于模式发现的方法是否能够检测到一种疾病的已知易感基因座，以及是否可以发现和确认其他易感基因座和易感基因座之间的相互作用。在这个研究方案中，新方法以及几种传统的遗传分析方法将应用于从关联研究中收集的真实数据集进行比较。对已识别的易感基因座进行详细的基因组数据库搜索将为验证结果提供生物学见解。在该方法得到验证后，FGT将开发一个包含新算法和相应统计框架的软件包，并将其用于支持其临床基因发现服务。如果这种基于模式的多基因座分析方法的实用性确实得到了拟议的研究和后续研究的证实，这项工作将为寻找导致常见疾病的基因开辟一个全新的方向，这些疾病具有复杂性和异质性。