纵向数据的连锁分析
Linkage analysis of longitudinal data.
作者信息
Suh Young Ju, Park Taesung, Cheong Soo Yeon
机构信息
Department of Statistics, Seoul National University, Seoul, South Korea.
出版信息
BMC Genet. 2003 Dec 31;4 Suppl 1(Suppl 1):S27. doi: 10.1186/1471-2156-4-S1-S27.
BACKGROUND
We propose a statistical model for linkage analysis of the longitudinal data. The proposed model is a mixed model based on the new Haseman and Elston model and allows several random effects. Specifically, the proposed model includes a random effect for correlation among sib pairs having one sibling in common, and one for the correlation among siblings from the same parents.
RESULTS
The proposed model was applied to the analysis of the Genetic Analysis Workshop 13 simulated data set for a quantitative trait of the systolic blood pressure. A simple independence model and two kinds of random effects models yielded good power for detecting linkage for these data sets, while the random effects models performed slightly better than the independence model. Both random effects models showed similar performance.
CONCLUSIONS
The proposed models seem not only quite useful in detecting linkage with the longitudinal data for the trait but also quite flexible. They can handle a wide class of correlation structures. Models with a more general class of covariance structure are desirable.
背景
我们提出了一种用于纵向数据连锁分析的统计模型。所提出的模型是基于新的哈斯曼和埃尔斯顿模型的混合模型，并允许有多个随机效应。具体而言，所提出的模型包括一个针对有一个共同兄弟姐妹的同胞对之间相关性的随机效应，以及一个针对来自同一父母的兄弟姐妹之间相关性的随机效应。
结果
将所提出的模型应用于遗传分析研讨会13模拟数据集的收缩压定量性状分析。一个简单的独立模型和两种随机效应模型对这些数据集检测连锁具有良好的效能，而随机效应模型的表现略优于独立模型。两种随机效应模型表现相似。
结论
所提出的模型似乎不仅在检测该性状纵向数据的连锁方面非常有用，而且相当灵活。它们可以处理广泛的相关结构。具有更一般协方差结构类别的模型是可取的。
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