A METHOD TO CORRECT FOR POPULATION STRUCTURE USING A SEGREGATION MODEL

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 为了克服基于群体的关联研究中群体分层造成的“虚假”关联，我们提出了一种基于主成分的方法，该方法可以同时利用家庭样本和无关样本。更具体地说，我们采用了多变量Logistic模型，该模型经常用于分离分析，并可以考虑家庭相关结构，用于关联分析。为了修正隐藏群体结构的影响，根据标记基因数据矩阵计算的前十个主成分作为协变量被纳入模型中。为了测试关联性，感兴趣的标记也被作为协变量纳入模型中。我们将提出的方法应用于GAW16 Framingham心脏研究50k数据集的第二代，即后代队列，以评估该方法的性能。尽管如平坦似然所示，在最大化似然函数时可能存在收敛困难，但检验统计量的经验P值的分布确实表明，只要可以计算估计的方差-协方差矩阵，该方法就具有正确的第I类错误率。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. To overcome the "spurious" association caused by population stratification in population-based association studies, we propose a principal component based method that can utilize both family and unrelated samples at the same time. More specifically, we adapt a multivariate logistic model, which is often used in segregation analysis and can allow for the family correlation structure, for association analysis. To correct the effect of hidden population structure, the first ten principal components calculated from the matrix of marker genotype data are incorporated as covariates in the model. To test for the association, the marker of interest is also incorporated as a covariate in the model. We applied the proposed method to the second generation, i.e. the Offspring Cohort, in the GAW16 Framingham Heart Study 50k Data Set to evaluate the performance of the method. Although there may have been difficulty in the convergence while maximizing the likelihood function, as indicated by a flat likelihood, the distribution of the empirical P-values for the test statistic does show that the method has a correct Type I error rate whenever the variance-covariance matrix of the estimates can be computed.