Genome-wide Association and linkage Studies with Diverse Resources

与不同资源的全基因组关联和联系研究

• 批准号: 7647437
• 负责人: Li Hsu
• 金额: $ 16.25万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7647437
• 关键词: Address; Affect; Alleles; Biotechnology; Cloning; Complex; Computational Technique; Data; Data Analyses; Development; Disease; Environment; Epistatic Gene; Equilibrium; Etiology; Facility Construction Funding Category; Family; Fright; Genes; Genetic Epistasis; Genetic Models; Genetic Variation; Genome; Genotype; Hybrids; Individual; Linkage Disequilibrium; Maps; Meta-Analysis; Methods; Modeling; Numbers; Parents; Population; Positioning Attribute; Procedures; Research; Research Design; Research Personnel; Resources; Risk; Sample Size; Scheme; Signal Transduction; Single Nucleotide Polymorphism; Site; Source; Statistical Methods; Variant; base; case control; computerized tools; cost; density; design; gene discovery; gene environment interaction; gene interaction; genetic association; genetic linkage analysis; genome wide association study; genome-wide linkage; high throughput technology; improved; predictive modeling; pressure

Genome-wide association and linkage studies involving hundreds or thousands of Single Nucleotide Polymorphisms (SNPs) are becoming increasingly common due to the rapid development of biotechnologies. Among many statistical challenges arising from these studies, the typical limited sample size is of particular concern because of high genotyping cost putting pressure to limit the number of individuals genotyped; increased statistical significant level for fear of too many false positives due to multiple comparisons; and moderate risk from each disease-associated variant allele in complex diseases. This application considers two strategies to address this issue: (1) to increase sample size by pooling data obtained from several sources; (2) to devise better statistical and computational tools for more efficient usage of the data. Correspondly, the first aim is to develop estimation and inference procedures for genetic association using data obtained from both population-based case-control and family-based studies, accommodating diverse ascertainment schemes of cases and controls, whereas the second aim is to develop analysis and regularization methods that enhance the possibility that the disease-associated variants and their interactions can actually be identified. The second aim is also concerned with the construction of risk predictive models from these SNPs. The highly dense SNP markers also pose problems to a more traditional model-based linkage analysis for gene discovery, because the methods for this analysis were developed assuming markers in linkage equilibrium, an assumption that is likely violated with the density of the SNPs. The third aim is to develop and evaluate estimating procedures for multipoint linkage analysis in the presence of linkage disequilibrium among SNP markers.

全基因组的关联和连锁研究涉及数百或数千个单核苷酸 由于迅速发展，多态性（SNP）变得越来越普遍 生物技术。在这些研究引起的许多统计挑战中，典型的有限样本 大小特别关注，因为高基因分型成本施加压力以限制数量 个人基因分型；由于担心过多的假阳性，因此提高了统计水平 多重比较；以及复杂疾病中每个与疾病相关的等位基因的中等风险。 该应用程序考虑了解决此问题的两种策略：（1）通过汇总数据来增加样本量 从几个来源获得； （2）设计更好的统计和计算工具以提高效率 数据使用。相应地，第一个目的是制定遗传的估计和推理程序 使用从基于人群的病例对照和基于家庭的研究获得的数据， 适应各种案件和控制的确定性方案，而第二个目的是 开发分析和正则化方法，以增强疾病相关的可能性 实际上可以确定变体及其相互作用。第二个目标也关注 这些SNP的风险预测模型的构建。 高度密集的SNP标记也对基于模型的更传统的链接分析构成问题 基因发现，因为开发了该分析的方法，假设链接中的标记 平衡，这一假设可能会因SNP的密度而违反。第三个目标是发展 并评估在存在连锁不平衡的情况下多点连锁分析的估计程序 在SNP标记中。