Department of Genetic Epidemiology, University Medical Center, University of Goettingen, Goettingen, Germany.
Genet Epidemiol. 2010 Jul;34(5):469-78. doi: 10.1002/gepi.20500.
Current approaches for analysis of longitudinal genetic epidemiological data of quantitative traits are typically restricted to normality assumptions of the trait. We introduce the longitudinal nonparametric test (LNPT) for cohorts with quantitative follow-up data to test for overall main effects of genes and for gene-gene and gene-time interactions. The LNPT is a rank procedure and does not depend on normality assumptions of the trait. We demonstrate by simulations that the LNPT is powerful, keeps the type-1 error level, and has very good small sample size behavior. For phenotypes with normal residuals, loss of power compared to parametric approaches (linear mixed models) was small for the quite general scenarios, which we simulated. For phenotypes with non-normal residuals, gain in power by the LNPT can be substantial. In contrast to parametric approaches, the LNPT is invariant with respect to monotone transformations of the trait. It is mathematically valid for arbitrary trait distribution.
目前分析定量性状纵向遗传流行病学数据的方法通常仅限于该性状正态性假设。我们引入了用于具有定量随访数据的队列的纵向非参数检验（LNPT），以检验基因的总体主效应以及基因-基因和基因-时间相互作用。LNPT 是一种秩过程，不依赖于性状的正态性假设。我们通过模拟证明 LNPT 具有强大的功效，保持了第一类错误水平，并且具有很好的小样本量行为。对于具有正态残差的表型，与参数方法（线性混合模型）相比，对于我们模拟的相当普遍的情况，其功效损失很小。对于具有非正态残差的表型，LNPT 的功效增益可能很大。与参数方法不同，LNPT 对性状的单调变换具有不变性。它在数学上适用于任意性状分布。
