Statistical, population genetics and genetic epidemiology

我们的研究领域是基因图谱和多维数据统计方法的开发和应用。我们一直在研究用于遗传关联研究的函数线性模型，并继续通过许多测试来确定基因组研究中最热门的真实和虚假信号的比例。最近的技术进步为研究人员提供了超越传统基因分型的能力，这些基因分型已知在普通人群中是多态的。研究参与者的基因序列现在可以直接评估。这一能力消除了技术驱动的偏向，即评分主要是常见的多态，并让研究人员揭示了丰富的稀有和样本特定的变种。虽然罕见和常见的多态对性状变异的相对贡献仍在争论中，但研究人员面临着对新的统计工具的需求，以同时评估一个地区内的所有变异。几个研究小组展示了函数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开发，以允许包括多个性状，并提供对其他协变量进行统计调整的能力。我们的函数方法是独特的，因为它通过将模型中的变量表示为在遗传区域上变化的曲线来提供对模型中变量的影响和相互作用的细微差别描述。我们的统计研究通过将我们提出的方法的性能与常用的统计工具进行对比，证明了该方法的灵活性和竞争力。在与Diatchenko博士(麦吉尔大学)的合作下，我们探索了这种方法的应用，以揭示与慢性疼痛状况发展有关的遗传风险因素的遗传结构。在研究个体的基因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是通过广泛的不同但可能相关的疼痛表型来衡量的。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，可能会导致统计能力较低。此外，与疼痛相关的特征可能有共同的病因。我们的方法允许同时测试多种相关的表型，包括定量的、二进制的、分类的，并对其他协变量进行调整。我们的另一项研究是关于在最具统计意义的结果中估计虚假结果的比例。本专题涉及对科学发现可复制性低的关切，这在一定程度上与统计分析的误用有关。统计意义的度量(P值)是常用的。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，但遇到了困难。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。

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.