Statistical, population genetics and genetic epidemiology

本研究的主题是单倍型，多位点，一般遗传关联方法，以及大规模数据分析中出现的统计问题，如全基因组关联扫描（GWAS）。我们的一些研究集中在开发方法，以结合联合收割机在同一疾病的不同样本中的遗传关联信号，或在多种病因相似的疾病中的信号。这些方法将有助于确定参与几种具有共同发病机制的疾病的遗传位点。例如，遗传变异可能与多种自身免疫性疾病的易感性有关。关联信号可以是相关的。导致相关信号的一个例子是GWAS的共享控制设计，其中在测试与不同疾病的遗传关联时重复使用控制组的事实可能会在关联信号之间产生强相关性。我们开发的方法是通用的（Zaykin，Kozbur 2010），并且它们正在与NIH和校外科学家合作应用于各种问题（Costigan等人，2010年，Reimann等人，二○一○年;正在进行的研究包括开发统计方法，以解决全基因组扫描中的多重性问题。这项研究包括调查旨在提高全基因组扫描中真阳性率的新方法（与Jack Taylor博士合作）。我们一直在开发方法，允许评估的机会，一个真正的协会将排名在最好的结果在基因组扫描。GWAS设计中的标准计算是确定样本量，以在全基因组显著性水平上获得足够的功效。我们正在采取另一种方法：计算真阳性将在特定数量的最佳结果中排名的概率，当它们通过关联统计进行排序时。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，如由捕获真实关联的期望概率所确定的。基于秩的方法很有吸引力，因为它为复制研究中所需的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显著性水平。这里的问题是，排名概率的评估可能是非常困难的，因为它需要非标准的数值方法和模拟的连锁不平衡的现实模式。连锁不平衡可能是特定的扫描，因此必须进行定制的分析，涉及访问给定基因组扫描的个体基因型数据。在GWAS密度下，这种分析可能需要数周时间才能运行。我们一直关心的发展的实际方法，评估排名的概率。我们一直在开发一种方法，这是完全通用的，同样简单的方法适用于无论连锁不平衡的程度和结构。其他统计遗传学研究包括调查家庭数据和不精确评分基因型的相对风险估计方法（与温伯格、施、乌姆巴赫、伦敦和汉考克博士合作）。

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).