Yashin A I, De Benedictis G, Vaupel J W, Tan Q, Andreev K F, Iachine I A, Bonafe M, DeLuca M, Valensin S, Carotenuto L, Franceschi C
Max Planck Institute for Demographic Research, Rostock, Germany.
Am J Hum Genet. 1999 Oct;65(4):1178-93. doi: 10.1086/302572.
In population studies on aging, the data on genetic markers are often collected for individuals from different age groups. The purpose of such studies is to identify, by comparison of the frequencies of selected genotypes, "longevity" or "frailty" genes in the oldest and in younger groups of individuals. To address questions about more-complicated aspects of genetic influence on longevity, additional information must be used. In this article, we show that the use of demographic information, together with data on genetic markers, allows us to calculate hazard rates, relative risks, and survival functions for respective genes or genotypes. New methods of combining genetic and demographic information are discussed. These methods are tested on simulated data and then are applied to the analysis of data on genetic markers for two haplogroups of human mtDNA. The approaches suggested in this article provide a powerful tool for analyzing the influence of candidate genes on longevity and survival. We also show how factors such as changes in the initial frequencies of candidate genes in subsequent cohorts, or secular trends in cohort mortality, may influence the results of an analysis.
在关于衰老的人群研究中，通常会收集不同年龄组个体的基因标记数据。此类研究的目的是通过比较选定基因型的频率，在最年长和较年轻的个体组中识别“长寿”或“虚弱”基因。为了解决关于基因对长寿影响的更复杂方面的问题，必须使用额外的信息。在本文中，我们表明，将人口统计学信息与基因标记数据结合使用，能够计算各个基因或基因型的风险率、相对风险和生存函数。本文讨论了整合基因和人口统计学信息的新方法。这些方法在模拟数据上进行了测试，然后应用于对人类线粒体DNA两个单倍群的基因标记数据的分析。本文提出的方法为分析候选基因对长寿和生存的影响提供了一个强大的工具。我们还展示了诸如后续队列中候选基因初始频率的变化或队列死亡率的长期趋势等因素可能如何影响分析结果。
