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Genetic Association Analysis of Human Longevity in Cohort Studies of Elderly Subjects: An Example of the PON1 Gene in the Danish 1905 Birth Cohort

机译:老年人队列研究中人类寿命的遗传关联分析:丹麦1905年出生队列中PON1基因的示例

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摘要

Although the case-control or the cross-sectional design has been popular in genetic association studies of human longevity, such a design is prone to false positive results due to sampling bias and a potential secular trend in gene–environment interactions. To avoid these problems, the cohort or follow-up study design has been recommended. With the observed individual survival information, the Cox regression model has been used for single-locus data analysis. In this article, we present a novel survival analysis model that combines population survival with individual genotype and phenotype information in assessing the genetic association with human longevity in cohort studies. By monitoring the changes in the observed genotype frequencies over the follow-up period in a birth cohort, we are able to assess the effects of the genotypes and/or haplotypes on individual survival. With the estimated parameters, genotype- and/or haplotype-specific survival and hazard functions can be calculated without any parametric assumption on the survival distribution. In addition, our model estimates haplotype frequencies in a birth cohort over the follow-up time, which is not observable in the multilocus genotype data. A computer simulation study was conducted to specifically assess the performance and power of our haplotype-based approach for given risk and frequency parameters under different sample sizes. Application of our method to paraoxonase 1 genotype data detected a haplotype that significantly reduces carriers' hazard of death and thus reveals and stresses the important role of genetic variation in maintaining human survival at advanced ages.
机译:尽管病例对照或横断面设计在人类寿命长寿的遗传关联研究中很流行,但由于抽样偏倚和基因-环境相互作用的潜在长期趋势,这种设计易于产生假阳性结果。为避免这些问题,建议进行队列研究或随访研究设计。根据观察到的个体生存信息,Cox回归模型已用于单基因座数据分析。在本文中,我们提出了一种新颖的生存分析模型,该模型将人群生存与个体基因型和表型信息相结合,以评估队列研究中与人类寿命的遗传关联。通过监测出生队列随访期内观察到的基因型频率的变化,我们能够评估基因型和/或单倍型对个体生存的影响。利用估计的参数,可以计算基因型和/或单倍型特异性生存和危害函数,而无需对生存分布进行任何参数假设。此外,我们的模型估算了随访期间出生队列中的单倍型频率,这在多基因座基因型数据中无法观察到。进行了计算机仿真研究,以针对不同样本量下给定的风险和频率参数,专门评估我们基于单体型方法的性能和功效。将我们的方法应用于对氧磷酶1基因型数据,可以检测出单倍型,该单倍型显着降低了携带者的死亡危险,从而揭示并强调了遗传变异在维持人类高龄生存方面的重要作用。

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