Genes, Demography, and Life Span: The Contribution of Demographic Data in Genetic Studies on Aging and Longevity

Abstract

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.

A/ASP/VAL allele combination of IGF1R, IRS2, and UCP2 genes is associated with better metabolic profile, preserved energy expenditure parameters, and low mortality rate in longevity

“…On the basis of literature data (Thatcher et al 1998;Yashin et al 1999), in order to assess the impact of specific IRS-2, IGF1R and UCP2 gene variants on longevity, subjects, were then sub-categorized in two groups, by splitting the whole sample at the age of 85: healthy people aged <85 years of age (n=514, mean age=49±16 year) were grouped under the denomination of "control"; healthy people aged from 86 to 104 year; (n=208, mean age=96±4), were collected in the group of "long-lived people".…”

Power of non‐parametric linkage analysis in mapping genes contributing to human longevity in long‐lived sib‐pairs

“…For any individual, defining the hazard of death at age x (the risk of dying after surviving to age x) as mðxÞ ¼ r i zm 0 ðxÞ; where r i is the relative risk for genotype i ði ¼ 0; 1 or 2Þ; z is an unobserved individual frailty that also affects survival, and m 0 ðxÞ is the baseline hazard function for an individual with both frailty and genotype relative risk set to 1. The corresponding individual survival function is sðxÞ ¼ e Àr i z R x 0 m 0 ðsÞds : To describe the relationship of the three genotypespecific survival functions, Yashin et al [1999] introduced a model that assumes that the unobserved frailty follows a gamma-distribution with mean 1 and variance s 2 [Vaupel et al, 1979;Vaupel and Yashin, 1985], so that mean survival for the subpopulation carrying genotype i is…”

“…The implementation of newly developed statistical methods [Yashin et al, 1999;Tan et al, 2001aTan et al, ,b, 2003 has helped the association approaches to gain preference due to increased statistical power [Pletcher and Stumpf, 2002]. In addition, important interaction terms (gene-sex, gene-environment, gene-gene) can also be considered within the framework of association [Tan et al, 2001a[Tan et al, , 2002a.…”

“…We first introduce a recently developed survival model [Yashin et al, 1999;Tan et al, 2001a] to simulate siblings' life spans for given risk and frequency parameters. By dealing with survival models, we try to make the simulation sensible to the practical situation and the results instructive to empirical studies.…”

“…Data were simulated by introducing a recently developed statistical model for measuring marker-longevity associations [Yashin et al, 1999: Am J Hum Genet 65:1178-1193, enabling direct power comparison between linkage and association approaches. The non-parametric linkage (NPL) scores estimated in the region harboring the causal allele are evaluated to assess the statistical power for different genetic (allele frequency and risk) and heterogeneity parameters under various sampling schemes (age-cut and sample size).…”