Abel Haley J, Thomas Alun
Division of Genetic Epidemiology, University of Utah, 391 Chipeta Way, Salt Lake City, UT 84105, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S62. doi: 10.1186/1753-6561-5-S9-S62.
We generalize recent work on graphical models for linkage disequilibrium to estimate the conditional independence structure between all variables for individuals in the Genetic Analysis Workshop 17 unrelated individuals data set. Using a stepwise approach for computational efficiency and an extension of our previously described methods, we estimate a model that describes the relationships between the disease trait, all quantitative variables, all covariates, ethnic origin, and the loci most strongly associated with these variables. We performed our analysis for the first 50 replicate data sets. We found that our approach was able to describe the relationships between the outcomes and covariates and that it could correctly detect associations of disease with several loci and with a reasonable false-positive detection rate.
我们将最近关于连锁不平衡图形模型的研究进行推广，以估计遗传分析研讨会17中无关个体数据集里个体所有变量之间的条件独立结构。为提高计算效率，我们采用逐步方法，并扩展了之前描述的方法，估计了一个描述疾病性状、所有定量变量、所有协变量、种族起源以及与这些变量关联最紧密的位点之间关系的模型。我们对前50个重复数据集进行了分析。我们发现我们的方法能够描述结果与协变量之间的关系，并且能够以合理的假阳性检测率正确检测疾病与多个位点的关联。
