Statistical, population genetics and genetic epidemiology

统计、群体遗传学和遗传流行病学

• 批准号: 9143481
• 负责人: dmitri v zaykin
• 金额: $ 30.83万
• 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/9143481
• 关键词: Accounting; Architecture; Area; Characteristics; Chromosome Mapping; Collaborations; Data; Data Set; Development; Disease susceptibility; Drowning; Environmental Exposure; Etiology; Evaluation; General Population; Genetic; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Genotype; Human; Human Genetics; Individual; Linear Models; Maps; Measures; Medicine; Methods; Modeling; Pain; Participant; Perception; Performance; Phenotype; Population Genetics; Probability; Public Health; Reaction; Relative (related person); Research; Research Personnel; Sampling; Signal Transduction; Statistical Methods; Technology; Testing; Universities; Variant; Work; chronic pain; design; disorder prevention; flexibility; genetic association; genetic epidemiology; genetic risk factor; health related quality of life; mathematical methods; meetings; novel; response; tool; trait

Our research have been in the area of development and application of statistical approaches for gene mapping and multidimensional data. We have been researching functional linear models for genetic association studies and continued to work on ascertaining the proportion of real and spurious signals among top hits in genomic studies with many tests. Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. While the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. We have been extending previous developments to allow inclusion of multiple traits and to provide capability to do statistical adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. Our statistical research demonstrated flexibility and competitive power of our proposed approach by contrasting its performance with commonly used statistical tools. In collaboration with Dr. Diatchenko (McGill University) we explored applications of this approach for uncovering genetic architecture of genetic risk factors involved in the development of chronic pain conditions. In studies of relative contribution of an individual's genetic composition to the perception of pain, the general characteristics of pain sensitivity are typically measured by a wide range of different, yet possibly related pain phenotypes. Testing each of these pain-perception traits individually is subject to problems of multiple testing and may result in low statistical power. Furthermore, pain-related traits may share common etiology. Our approach allowed both simultaneous testing of multiple correlated phenotypes, including quantitative, binary, categorical, with adjustment for additional covariates. Another line of our research is on estimation of proportion of spurious findings among most statistically significant results. This topic is related to concerns about low replicability of scientific findings, which is in part related to misapplications of statistical analysis. Measures of statistical significance (P-values) are commonly used. Attempts to design simple ways to convert an association P-value into the probability that a finding is spurious have been met with difficulties. In our research, we proposed a method that lets researchers extract probability that a finding is spurious directly from a P-value.

我们的研究一直在基因定位和多维数据的统计方法的开发和应用领域。我们一直在研究遗传关联研究的函数线性模型，并通过许多测试继续致力于确定基因组研究中最重要的命中中真实的和虚假信号的比例。最近的技术进步使研究人员具备了超越传统基因分型的能力，这些基因分型在一般人群中是多态的。现在可以直接评估研究参与者的基因序列。这种能力消除了技术驱动的对主要常见多态性进行评分的偏见，并让研究人员揭示了大量罕见和样本特异性变异。虽然罕见和常见的多态性性状变异的相对贡献正在争论，研究人员面临着需要新的统计工具，同时评估一个地区内的所有变异。几个研究小组证明了功能线性模型方法的灵活性和良好的统计能力。我们一直在扩展以前的发展，以允许包括多个性状，并提供能力做统计调整额外的协变量。我们的函数方法是独特的，因为它提供了一个微妙的描述的影响和相互作用的模型中的变量，将它们表示为曲线变化的遗传区域。我们的统计研究表明，我们提出的方法的灵活性和竞争力，通过对比其性能与常用的统计工具。与Diatchenko博士（麦吉尔大学）合作，我们探索了这种方法的应用，以揭示参与慢性疼痛疾病发展的遗传风险因素的遗传结构。在研究个体的遗传组成对疼痛感知的相对贡献时，疼痛敏感性的一般特征通常通过广泛的不同但可能相关的疼痛表型来测量。单独测试这些疼痛感知特征中的每一个都会遇到多次测试的问题，并且可能导致统计功效低。此外，疼痛相关的性状可能有共同的病因。我们的方法允许同时测试多个相关的表型，包括定量，二元，分类，并调整额外的协变量。我们研究的另一个方面是估计在最具统计学意义的结果中虚假发现的比例。这一专题涉及对科学发现的可复制性低的关切，而科学发现的可复制性低的部分原因是统计分析的误用。通常使用统计学显著性（P值）测量。 试图设计简单的方法来将关联P值转换为发现是虚假的概率已经遇到了困难。在我们的研究中，我们提出了一种方法，让研究人员直接从P值中提取发现是虚假的概率。