How genes influence life span: the biodemography of human survival

Abstract

Background: In genome-wide association studies (GWAS) of human life span, none of the genetic variants has reached the level of genome-wide statistical significance. The roles of such variants in life span regulation remain unclear.

Data and method: A biodemographic analyses was done of genetic regulation of life span using data on low-significance longevity alleles selected in the earlier GWAS of the original Framingham cohort.

Results: Age-specific survival curves considered as functions of the number of longevity alleles exhibit regularities known in demography as "rectangularization" of survival curves. The presence of such pattern confirms observations from experimental studies that regulation of life span involves genes responsible for stress resistance.

Conclusion: Biodemographic analyses could provide important information about the properties of genes affecting phenotypic traits.

FIG. 1.

Different factors produce similar patterns of changes in survival. (A) Survival curves (conditional at age 60) in the U.S. total population (both sexes) in years 1933–2007 (data source: Human Mortality Database). (B) Patterns of changes in survival of carriers of different numbers of longevity alleles detected in our genome-wide association study (GWAS) of the original Framingham Heart Study (FHS) cohort corresponding to Gompertz approximations of corresponding mortality curves.

FIG. 2.

Different factors produce similar changes in mean life span. (A) The “genetic dose—phenotypic response” relationship between the numbers of selected “low-effect longevity” alleles (39 total) contained in individuals' genome and mean life span (LS) of individuals carrying a given number of longevity single-nucleotide polymorphisms (SNPs) in their genomes (analyses of 500K SNP data, original Framingham Heart Study [FHS] cohort). (Black dots) Observed data; (dashed line) fitted linear regression. Longevity alleles were selected using a linear regression procedure, which involved comparison of characteristics of life span distributions among carriers and noncarriers of each of 500K genetic variants. (B) Life expectancy (LE) at birth in the U.S. total population (both sexes), 1980–2007 (data source: Human Mortality Database). (Dots) Observed data; (dashed line) fitted linear regression.

FIG. 3.

Mortality rates for populations with different genetic background show Strehler–Mildvan (SM) correlation. The logarithms of estimated mortality rates approximated by the Gompertz curves in groups of Framingham Heart Study (FHS) original cohort members having different numbers of low-effect longevity alleles in their genomes.

FIG. 4.

Saving lives contributes to explanations of mortality and survival improvement. (A) Logarithms of mortality rates for U.S. total population (females and males) in 1950 (open circles) and in 2007 (open triangles). The 1950 mortality rate is approximated by the Gompertz function with n=0, where n is the number of times individual's life has been saved. The other curves correspond to mortality rates obtained by transforming the 1950 Gompertz mortality rate using the life saving equation with n=1, 2, 3, 4, and 5. (B) Survival functions for U.S. total population (females and males) in 1950 (open circles) and in 2007 (open triangles). Three other survival curves correspond to mortality rates obtained by transforming the 1950 Gompertz curve using the life saving equation with n=1, 2, and 3.