Neuropsychiatric Institute and Epilepsy Genetics/Genomics Laboratories, University of California at Los Angeles School of Medicine, Los Angeles, CA, USA.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S49. doi: 10.1186/1471-2156-6-S1-S49.
The purpose of these analyses was to determine if incorporating or adjusting for covariates in genetic analyses helped or hindered in genetic analyses, specifically heritability and linkage analyses. To study this question, two types of covariate models were used in the simulated Genetic Analysis Workshop 14 dataset in which the true gene locations are known. All four populations of one replicate were combined for the analyses. The first model included typical covariates of sex and cohort (population) and the second included the typical covariates and also those related endophenotypes that are thought to be associated with the trait (phenotypes A, B, C, D, E, F, G, H, I, J, K, and L). A final best fit model produced in the heritability analyses was used for linkage. Linkage for disease genes D1, D3, and D4 were localized using models with and without the covariates. The use of inclusion of covariates did not appear to have any consistent advantage or disadvantage for the different phenotypes in regards to gene localization or false positive rate.
这些分析的目的是确定在遗传分析中纳入或调整协变量是否有助于或阻碍遗传分析，特别是遗传力和连锁分析。为了研究这个问题，在已知真实基因位置的模拟遗传分析研讨会 14 数据集 中使用了两种类型的协变量模型。对一个重复的所有四个群体进行了分析。第一个模型包括性别和队列（群体）的典型协变量，第二个模型包括典型协变量以及那些与被认为与特征相关的内表型（表型 A、B、C、D、E、F、G、H、I、J、K 和 L）。在遗传力分析中产生的最终最佳拟合模型用于连锁。使用有和没有协变量的模型定位疾病基因 D1、D3 和 D4 的连锁。就基因定位或假阳性率而言，纳入协变量的使用似乎对不同表型没有任何一致的优势或劣势。
