How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data

Abstract

Background and objective: 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.

Data and methods: 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.

Results: 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.

Conclusions: . 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.

Keywords: genetic model of mortality; genetics of aging; genetics of longevity; physiological variables; stress resistance.

Figure 1. Effects of sample call rate in GWAS

Note: The figure shows the effects of sample call rate on the number of genome-wide statistically significant genetic variants in GWAS of the Framingham original cohort. Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 2. Distribution of age at time of biospecimen collection

Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 3. Survival functions of individuals with different dose of vulnerability alleles

Note: The figure shows Kaplan-Meier estimates of conditional survival functions for carriers of two or less (dotted line) and more than two (dashed line) out of 24 vulnerability alleles. Solid line shows survival function for genotyped individuals. Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 4. Average age trajectories of physiological variables for genotyped males and females

Source: Framingham Heart Study (a limited access dataset), original cohort; pooled data from exams 1 to 28

Figure 5. Physiological variables in male carriers/non-carriers of minor allele of rs5491

Note: The figure shows the average age trajectories of eight physiological variables for male carriers and non-carriers of the minor allele (MA) of SNP rs5491. Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 6. Physiological variables in male carriers/non-carriers of minor allele of rs9925881

Note: The figure shows the average age trajectories of eight physiological variables for male carriers and non-carriers of the minor allele (MA) of SNP rs9925881. Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 7. Application of genetic stochastic process model to total cholesterol

Note: The figure shows estimates of the logarithm of the baseline hazard (ln μ₀(t,G), top left panel), the multiplier in the quadratic part of the hazard (μ₁ (t,G), top right panel), the adaptive capacity (the absolute value of the feedback coefficient, | a (t,G) |, bottom left panel) and the mean allostatic trajectory (f₁ (t,G), bottom right panel) for carriers (e4) and non-carriers (no e4) of the APOE e4 allele. Source: Framingham Heart Study (a limited access dataset), original cohort

Figure 8. Application of genetic stochastic process model to diastolic blood pressure

Note: The figure shows estimates of the logarithm of the baseline hazard (ln μ₀ (t,G), top left panel), the multiplier in the quadratic part of the hazard (μ₁ (t,G), top right panel), the adaptive capacity (the absolute value of the feedback coefficient, | a (t,G) |, bottom left panel) and the mean allostatic trajectory (f₁ (t,G), bottom right panel) for carriers (e4) and non-carriers (no e4) of the APOE e4 allele. Source: Framingham Heart Study (a limited access dataset), original cohort