Human Genetics Unit, Indian Statistical Institiute, 203 B,T, Road, Kolkata 700 108, India.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S19. doi: 10.1186/1471-2156-6-S1-S19.
Multivariate phenotypes underlie complex traits. Thus, instead of using the end-point trait, it may be statistically more powerful to use a multivariate phenotype correlated to the end-point trait for detecting linkage. In this study, we develop a reverse regression method to analyze linkage of Kofendrerd Personality Disorder affection status in the New York population of the Genetic Analysis Workshop 14 (GAW14) simulated dataset. When we used the multivariate phenotype, we obtained significant evidence of linkage near four of the six putative loci in at least 25% of the replicates. On the other hand, the linkage analysis based on Kofendrerd Personality Disorder status as a phenotype produced significant findings only near two of the loci and in a smaller proportion of replicates.
多变量表型是复杂特征的基础。因此，与使用终点特征相比，使用与终点特征相关的多变量表型进行检测可能在统计学上更具优势。在这项研究中，我们开发了一种反向回归方法来分析 Kofendrerd 人格障碍影响状态在遗传分析工作坊 14（GAW14）模拟数据集的纽约人群中的连锁。当我们使用多变量表型时，我们在至少 25%的重复中，在六个假定基因座中的四个附近获得了显著的连锁证据。另一方面，基于 Kofendrerd 人格障碍状态作为表型的连锁分析仅在两个基因座附近和更小比例的重复中产生了显著的结果。
