Kulle Bettina, Köhler Karola, Rosenberger Albert, Loesgen Sabine, Bickeböller Heike
Department of Genetic Epidemiology, University of Göttingen, Humboldtallee 32, D-37073 Göttingen, Germany.
BMC Genet. 2003 Dec 31;4 Suppl 1(Suppl 1):S85. doi: 10.1186/1471-2156-4-S1-S85.
Basically no methods are available for the analysis of quantitative traits in longitudinal genetic epidemiological studies. We introduce a nonparametric factorial design for longitudinal data on independent sib pairs, modelling the phenotypic quadratic differences as the dependent variable. Factors are the number of alleles shared identically by descent (IBD) and the age categories at which the dependent variable is measured, allowing for dependence due to age. To identify a linked marker a rank statistic tests the influence of IBD group on phenotypic quadratic differences. No assumptions are made on normality or variances of the dependent variable. We apply our method to 71 sib pairs from the Framingham Heart Study data provided at the Genetic Analysis Workshop 13. For all 15 available markers on chromosome 17 we analyzed the influence on systolic blood pressure. In addition, different selection strategies to sample from the whole data are discussed.
基本上，纵向遗传流行病学研究中尚无用于分析数量性状的方法。我们针对独立同胞对的纵向数据引入了一种非参数析因设计，将表型二次差异作为因变量进行建模。因素包括通过血缘相同共享的等位基因数量（IBD）以及测量因变量的年龄类别，从而考虑到年龄引起的相关性。为了识别连锁标记，一个秩统计量检验IBD组对表型二次差异的影响。对因变量的正态性或方差不做任何假设。我们将我们的方法应用于遗传分析研讨会13提供的弗雷明汉心脏研究数据中的71对同胞对。对于17号染色体上所有15个可用标记，我们分析了它们对收缩压的影响。此外，还讨论了从整个数据中抽样的不同选择策略。
