Statistical, population genetics and genetic epidemiology

这项研究的主要主题是单倍型、多位点、一般的遗传关联方法，以及在大规模数据分析中出现的统计问题，例如在基因组范围关联扫描中。我们的一些研究集中在开发方法，以组合同一疾病不同样本的遗传关联信号，或多个病因相似疾病的信号。这些方法将有助于确定与几种共同发病机制的疾病有关的遗传位点。例如，基因变异可能与几种自身免疫性疾病的易感性有关。关联信号可以被关联。导致相关信号的一个例子是为GWAS设计的共享对照，在这种情况下，在测试与不同疾病的遗传关联时重复使用一个对照组的事实可能会在关联信号之间产生强烈的关联。我们开发的方法是通用的(Zaykin，Kozbur 2010)，它们正在与NIH和校外科学家(Costigan等人，2010，Reimann等人，2010；与Raja Jothi博士，正在进行的研究)的合作中应用于各种问题，正在进行的研究包括开发统计方法来解决全基因组扫描中的多样性问题。这项研究包括对旨在提高全基因组扫描中真阳性等级的新方法的调查(与杰克·泰勒博士合作)。我们一直在开发方法，以评估真正的关联在基因组扫描中跻身最佳结果之列的可能性。GWAS设计中的一个标准计算是在全基因组意义水平上实现足够能力所需的样本量确定。我们正在采取另一种方法：计算当按关联统计进行排序时，真正积极的结果将排在特定数量的最佳结果中的概率。基于排名的方法允许人们找到要跟进的最重要结果的数量，这由捕获真实关联的期望概率确定。基于排名的方法很有吸引力，因为它为重复研究所需的SNP数量提供了指导。与基于功率的方法不同，它不需要指定特定的重要性级别。这里的问题是，排名概率的评估可能非常困难，因为它需要非标准的数值方法和模拟现实的连锁不平衡模式。连锁不平衡可能是特定扫描所特有的，因此必须执行定制的分析，其中包括访问给定基因组扫描的个体基因数据。在GWAS密度下，这样的分析可能需要数周时间才能运行。我们一直在关注评估排名概率的实用方法的发展。我们一直在开发一种完全通用的方法，因为无论连锁不平衡的程度和结构如何，相同的简单方法都适用。其他统计遗传学研究包括对家系数据和不精确评分的基因类型的相对风险估计方法的调查(与Weinberg、Shih、Umbach、London和Hancock博士合作)。

The main theme of this research is haplotype, multilocus, general genetic association methods, and statistical issues that arise in large scale data analysis, such as in genome-wide association scans (GWAS). Some of our research is focusing on developing of methods to combine genetic association signals across different samples of the same disease, or signals across multiple, etiologically similar diseases. These methods will help to identify genetic loci involved in several diseases with shared pathogenesis. For example, the genetic variant can be involved in susceptibility to several autoimmune diseases. Association signals can be correlated. One example that leads to correlated signals is shared controls design for GWAS, where the fact of reusing a control group while testing for genetic association with different diseases may create strong correlation between association signals. The methods we have developed are general (Zaykin, Kozbur 2010), and they are being applied to diverse problems in collaborations with NIH and extramural scientists (Costigan et al., 2010, Reimann et al., 2010; with Dr. Raja Jothi, ongoing) Ongoing research include development of statistical approaches to address multiplicity issues in whole genome scans. This research includes investigation of novel approaches aimed to improve ranks of true positives in whole genome scans (in collaboration with Dr. Jack Taylor). We have been developing methods that allow evaluation of chances that a true association will rank among best results in a genome scan. A standard calculation in the design of GWAS is a sample size determination needed to achieve adequate power at the genome-wide level of significance. We are taking an alternative approach: to calculate the probability that a true positive will rank among a specific number of best results, when they are sorted by an association statistic. The rank-based approach allows one to find the number of most significant results to follow up on, as determined by the desired probability of capturing a true association. The rank-based approach is appealing, since it provides guidance for the number of SNPs needed in a replication study. Unlike the power-based approach, it does not require specification of a particular significance level. The problem here is that evaluation of ranking probabilities can be very difficult, because it requires non-standard numerical methods and simulations that model realistic patterns of linkage disequilibrium. Linkage disequilibrium may be specific to a particular scan, thus one would have to perform a customized analysis that involves access to the individual genotype data for a given genome scan. At GWAS densities such analysis can take many weeks to run. We have been concerned with development of practical methods for evaluation of ranking probabilities. We have been developing a method that is completely general in that the same simple approach applies regardless of the extent and structure of linkage disequilibrium. Other statistical genetics research included investigation of methods for estimation of relative risk for family data and imprecisely scored genotypes (in collaboration with Drs. Weinberg, Shi, Umbach, London and Hancock).