Statistical, population genetics and genetic epidemiology

我们的研究一直在基因定位和多维数据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功能线性模型，并继续致力于通过许多测试确定基因组研究中顶级命中的真实和虚假信号的比例。最近的技术进步使研究人员具备了超越传统的基因分型的能力，这些基因分型已知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偏见，即主要对常见多态性进行评分，并使研究人员能够揭示大量罕见和样本特定的变异。虽然罕见和常见多态性对性状变异的相对贡献仍在争论中，但研究人员面临着需要新的统计工具来同时评估一个地区内所有变异的需求。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，以允许包含多个特征，并提供对附加协变量进行统计调整的能力。我们的功能方法是独特的，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，提供了对它们的影响和相互作用的细致描述。我们的统计研究通过与常用统计工具的性能对比，证明了我们提出的方法的灵活性和竞争力。在与Diatchenko博士（麦吉尔大学）的合作中，我们探索了这种方法的应用，以揭示慢性疼痛疾病发展中涉及的遗传风险因素的遗传结构。在研究个体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测量的。单独测试这些疼痛感知特征会受到多重测试的问题的影响，并可能导致低统计能力。此外，与疼痛相关的特征可能具有共同的病因。我们的方法允许同时检测多种相关表型，包括定量、二元、分类，并对其他协变量进行调整。我们研究的另一条线是在最具统计意义的结果中估计虚假发现的比例。这个主题与对科学发现的低可复制性的担忧有关，这在一定程度上与统计分析的错误应用有关。通常使用统计显著性（p值）度量。试图设计简单的方法将关联p值转换为发现是虚假的概率，遇到了困难。在我们的研究中，我们提出了一种方法，可以让研究人员直接从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.