Statistical, population genetics and genetic epidemiology

统计、群体遗传学和遗传流行病学

• 批准号: 8336629
• 负责人: dmitri v zaykin
• 金额: $ 58.01万
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 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/8336629
• 关键词: Accounting; Collaborations; Data; Data Analyses; Data Set; Development; Disease susceptibility; Drowning; Environmental Exposure; Family; Follow-Up Studies; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Genotype; Haplotypes; Human; Human Genetics; Individual; Investigation; Linkage Disequilibrium; London; Maps; Medicine; Methodology; Methods; Phase; Population Genetics; Predisposition; Probability; Public Health; Reaction; Relative Risks; Research; Research Design; Sampling; Signal Transduction; Specific qualifier value; Statistical Methods; Variant; Work; base; design; disorder prevention; genetic association; genetic epidemiology; genome wide association study; health related quality of life; novel; research study; response; trait

The main theme of this research is haplotype, multilocus, general genetic association methods, and statistical issues that arise in large scale genetic data analysis, such as in genome-wide association scans. We have been developing methods to characterize and combine genetic association signals within and between studies, and researched approaches for design of discovery and replication phases of analysis. In recent years, genome-wide association studies have uncovered a large number of susceptibility variants. Without replication, large-scale studies provide only tentative evidence of association, and follow up studies focusing on top hits are required to establish their validity. The number of top hits to carry forward into the replication step is often determined ad hoc. We developed a novel statistical approach based on controlling the proportion of genuine associations among a specified number of top hits. This approach is useful for designing large-scale studies and for selection of promising results for following up. We derived very accurate approximations for probabilities governing rankings of true positives in a study. Using these approximations, we were able to accommodate genomic linkage disequilibrium and evaluate its influence on rankings of true positives. Based on our approach, a study-specific proportion of true associations among top hits can be estimated from P-values, and its expected value can be predicted for study design applications. While we primarily work with samples of unrelated individuals, some research also included investigation of methods for estimation of relative risk for imprecisely scored genotypes using family data (in collaboration with Drs. Weinberg and London). Ongoing research includes development of statistical approaches to estimate the distribution of effect sizes using information extracted form top association signals in a large scale study. Characterization of this distribution will be useful in a variety of statistical genetics applications, including estimation of false discovery rates and of individual probabilities that an association signal is a true positive.

这项研究的主要主题是单倍型、多位点、一般遗传关联方法，以及在大规模遗传数据分析中出现的统计问题，例如在全基因组关联扫描中。我们一直在开发方法来表征和组合研究内部和研究之间的遗传关联信号，并研究了设计发现和复制分析阶段的方法。近年来，全基因组关联研究发现了大量的易感变异。如果没有复制，大规模研究只提供了初步的相关性证据，需要关注热门药物的后续研究来确定其有效性。带入复制步骤的最高命中次数通常是临时确定的。我们开发了一种新的统计方法，该方法基于控制指定数量的热门点击量之间真实关联的比例。这种方法对于设计大规模研究和选择有希望的结果进行后续研究是有用的。在一项研究中，我们推导出了支配真阳性排名的概率的非常准确的近似。使用这些近似值，我们能够适应基因组连锁不平衡，并评估其对真阳性排名的影响。基于我们的方法，可以从P值中估计出最热门命中之间真实关联的研究特定比例，并且可以为研究设计应用预测其期望值。虽然我们主要研究的是无关个体的样本，但一些研究也包括使用家庭数据(与温伯格博士和伦敦博士合作)估计评分不准确的基因类型的相对风险的方法。正在进行的研究包括开发统计方法，以使用从大规模研究中的顶级关联信号中提取的信息来估计效果大小的分布。这种分布的特征在各种统计遗传学应用中将是有用的，包括估计错误发现率和个体关联信号为真阳性的概率。