How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.
How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.
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DOI：
10.1080/10920277.2016.1178588
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Ukraintseva SV
中科院分区：
文献类型：
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作者：
Yashin AI;Arbeev KG;Wu D;Arbeeva L;Kulminski A;Kulminskaya I;Akushevich I;Ukraintseva SV
为了阐明人类衰老和长寿特征的遗传调控机制，人们对这些特征进行了许多全基因组关联研究（GWAS）。然而，这些分析的结果并没有达到研究人员的预期。大多数检测到的遗传关联尚未达到具有统计显着性的全基因组水平，并且在独立群体的研究中缺乏复制。该研究领域进展缓慢的原因包括数据分析中使用的统计方法效率低、衰老和长寿相关性状的遗传异质性、遗传变异对这些性状的多效性（例如年龄依赖性）影响的可能性、低估了（i）遗传异质群体中的死亡率选择、（ii）外部因素和个体遗传背景差异的影响 在所研究的人群中，概念生物学框架存在缺陷，未能充分考虑上述因素。所进行的研究的另一个局限性是，他们没有完全认识到纵向数据的潜力，这些数据可以用来评估生命过程中生理变量和其他生物标志物如何介导遗传对寿命的影响。本文的目的就是解决这些问题。我们使用来自原始弗雷明汉心脏研究队列的不同数据子集（对应于不同的质量控制（QC）程序）对人类寿命进行了 GWAS，并使用选定的遗传变异的一个子集进行进一步分析。我们使用模拟研究表明，组合数据的方法可以提高 GWAS 的质量。我们使用 FHS 纵向数据来比较所选遗传变异携带者和非携带者的生理变量的平均年龄轨迹。我们使用人类死亡率和衰老的随机过程模型来研究遗传对衰老的隐藏生物标志物以及衰老与寿命之间的动态相互作用的影响。我们研究了与选定变体相关的基因的特性及其在信号传导和代谢途径中的作用。我们表明，使用不同的质量控制程序会导致与寿命相关的不同组遗传变异。我们选择了 24 个与寿命负相关的基因变异。我们表明，对生物样本采集时的遗传数据和后续数据进行联合分析大大提高了所选 24 个 SNP 与寿命关联的显着性。我们还表明，对于选定变体的携带者和非携带者群体，生理变量和隐藏的衰老生物标志物的衰老相关变化有所不同。 。这些分析的结果证明了在这些性状的遗传关联研究中使用生物人口统计学模型和方法的好处。我们的研究结果表明，缺乏大量具有有害影响的遗传变异可能会对超长寿命做出重大贡献。这些影响是由许多生理变量和隐藏的衰老生物标志物动态介导的。这些研究的结果证明了在人类衰老和长寿的基因研究中使用死亡风险综合统计模型的好处。
To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of the researchers. Most detected genetic associations have not reached a genome-wide level of statistical significance, and suffered from the lack of replication in the studies of independent populations. The reasons for slow progress in this research area include low efficiency of statistical methods used in data analyses, genetic heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g., age dependent) effects of genetic variants on such traits, underestimation of the effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external factors and differences in genetic backgrounds of individuals in the populations under study, the weakness of conceptual biological framework that does not fully account for above mentioned factors. One more limitation of conducted studies is that they did not fully realize the potential of longitudinal data that allow for evaluating how genetic influences on life span are mediated by physiological variables and other biomarkers during the life course. The objective of this paper is to address these issues. We performed GWAS of human life span using different subsets of data from the original Framingham Heart Study cohort corresponding to different quality control (QC) procedures and used one subset of selected genetic variants for further analyses. We used simulation study to show that approach to combining data improves the quality of GWAS. We used FHS longitudinal data to compare average age trajectories of physiological variables in carriers and non-carriers of selected genetic variants. We used stochastic process model of human mortality and aging to investigate genetic influence on hidden biomarkers of aging and on dynamic interaction between aging and longevity. We investigated properties of genes related to selected variants and their roles in signaling and metabolic pathways. We showed that the use of different QC procedures results in different sets of genetic variants associated with life span. We selected 24 genetic variants negatively associated with life span. We showed that the joint analyses of genetic data at the time of bio-specimen collection and follow up data substantially improved significance of associations of selected 24 SNPs with life span. We also showed that aging related changes in physiological variables and in hidden biomarkers of aging differ for the groups of carriers and non-carriers of selected variants. . The results of these analyses demonstrated benefits of using biodemographic models and methods in genetic association studies of these traits. Our findings showed that the absence of a large number of genetic variants with deleterious effects may make substantial contribution to exceptional longevity. These effects are dynamically mediated by a number of physiological variables and hidden biomarkers of aging. The results of these research demonstrated benefits of using integrative statistical models of mortality risks in genetic studies of human aging and longevity.