A METHOD TO CORRECT FOR POPULATION STRUCTURE USING A SEGREGATION MODEL

一种利用隔离模型修正人口结构的方法

• 批准号: 8171730
• 负责人: XIAOFENG ZHU
• 金额: $ 0.99万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171730
• 关键词: Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Family; Framingham Heart Study; Funding; Generations; Genotype; Grant; Institution; Likelihood Functions; Logistic Models; Methods; Modeling; Performance; Population; Research; Research Personnel; Resources; Sampling; Source; Stratification; Structure; Testing; Time; United States National Institutes of Health; base; cohort; interest; offspring; population based; segregation; statistics

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.

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 为了克服基于群体的关联研究中群体分层所造成的“虚假”关联，我们提出了一种基于主成分的方法，可以同时利用家庭和无关样本。 更具体地说，我们采用了多变量logistic模型，这是经常用于分离分析，可以考虑家庭相关结构，关联分析。 为了校正隐藏的群体结构的影响，从标记基因型数据矩阵计算的前10个主成分作为协变量纳入模型中。 为了检验相关性，还将目标标志物作为协变量纳入模型中。 我们将所提出的方法应用于第二代，即后代队列，在GAW 16心脏研究50 k数据集中评估该方法的性能。 虽然在最大化似然函数时可能存在收敛困难，如平坦似然所示，但检验统计量的经验P值的分布确实表明，只要可以计算估计值的方差-协方差矩阵，该方法就具有正确的I型错误率。