Dato S, Carotenuto L, De Benedictis G
Department of Cell Biology, University of Calabria, 87030 Rende, Italy.
Biogerontology. 2007 Feb;8(1):31-41. doi: 10.1007/s10522-006-9030-1. Epub 2006 Jul 29.
Association analyses between gene variability and human longevity carried out by comparing gene frequencies between population samples of different ages (case/control design) may provide information on genes and pathways playing a role in modulating survival at old ages. However, by dealing with cross-sectional data, the gene-frequency (GF) approach ignores cohort effects in population mortality changes. The genetic-demographic (GD) approach adds demographic information to genetic data and allows the estimation of hazard rates and survival functions for candidate alleles and genotypes. Thus mortality changes in the cohort to which the cross-sectional sample belongs are taken into account. In this work, we applied the GD method to a dataset relevant to two genes, APOE and HSP70.1, previously shown to be related to longevity by the GF method. We show that the GD method reveals sex- and age-specific allelic effects not shown by the GF analysis. In addition, we provide an algorithm for the implementation of a non-parametric GD analysis.
通过比较不同年龄人群样本之间的基因频率（病例/对照设计）来进行基因变异性与人类长寿之间的关联分析，可能会提供有关在调节老年生存中起作用的基因和途径的信息。然而，通过处理横断面数据，基因频率（GF）方法忽略了人群死亡率变化中的队列效应。遗传人口统计学（GD）方法将人口统计学信息添加到遗传数据中，并允许估计候选等位基因和基因型的危险率和生存函数。因此，考虑了横断面样本所属队列中的死亡率变化。在这项工作中，我们将GD方法应用于与两个基因APOE和HSP70.1相关的数据集，这两个基因先前已通过GF方法显示与长寿相关。我们表明，GD方法揭示了GF分析未显示的性别和年龄特异性等位基因效应。此外，我们提供了一种用于实施非参数GD分析的算法。
