Qihua Tan

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

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.

Recent advances in human gene-longevity association studies

This paper reviews the recent literature on genes and longevity. The influence of genes on human life span has been confirmed in studies of life span correlation between related individuals based on family and twin data. Results from major twin studies indicate that approximately 25% of the variation in life span is genetically determined. Taking advantage of recent developments in molecular biology, researchers are now searching for candidate genes that might have an influence on life span. The data on unrelated individuals emerging from an ever-increasing number of centenarian studies makes this possible. This paper summarizes the rich literature dealing with the various aspects of the influence of genes on individual survival. Common phenomena affecting the development of disease and longevity are discussed. The major methodological difficulty one is confronted with when studying the epidemiology of longevity involves the complexity of the phenomenon, which arises from the polygenic nature of life span and historical mortality change. We discuss this issue and suggest new methodological approaches.

Measuring the genetic influence in modulating the human life span: gene-environment interaction and the sex-specific genetic effect.

New approaches are needed to explore the different ways in which genes affect the human life span. One needs to assess the genetic effects themselves, as well as gene–environment interactions and sex dependency. In this paper, we present a new model that combines both genotypic and demographicinformation in the estimation of the geneticinfluence on life spans. Based on Coxsproportional hazard assumption, the modelmeasures the risks for each gene as well as forgene–environment and gene–sex interactions,while controlling for confounding factors. Atwo-step MLE is introduced to obtain anon-parametric form of the baseline hazardfunction. The model is applied to genotypicdata from Italian centenarian studies toestimate relative risks of candidate genes,risks due to interactions and initialfrequencies of different genes in thepopulation. Results from models that either door do not take into consideration individualheterogeneity are compared. It is shown thatignoring the existence of heterogeneity canlead to a systematic underestimation of geneticeffects and effects due to interactions.

Variations of cardiovascular disease associated genes exhibit sex-dependent influence on human longevity.

This article investigates the relationship between the polymorphic variations in genes associated with cardiovascular disease and longevity in the Danish population. A new procedure that combines both demographic and the individual genetic information in determining the relative risks of the observed genetic variations is applied. The sex-dependent influences can be found by introducing sex-specific population survival and incorporating the risk of gene-sex interaction. Three genetic polymorphisms, angiotensinogen M/T235, blood coagulation factor VII (FVII) R/Q353 and FVII-323ins10, manifest significant influences on survival in males, with reduced hazards of death for carriers of the angiotensinogen M235 allele, the F VII Q353 allele, and the FVII-323P10 allele. The results show that some of these genotypes associated with lower risk of CVD could also reduce the carriers death rate and contribute to longevity. However, the presence of sex-dependent effects and the fact that major CVD-associated genes failed to impose detrimental influence on longevity lead us to concur that the aging process is highly complicated.

Replication studies in longevity: puzzling findings in Danish centenarians at the 3'APOB-VNTR locus.

In Danes we replicated the 3APOB-VNTR gene/longevity association study previously carried out in Italians, by which the Small alleles (less than 35 repeats) had been identified as frailty alleles for longevity. In Danes, neither genotype nor allele frequencies differed between centenarians and 20-64-year-old subjects. However, when Danish and Italian data were compared, a significant difference (p = 0.0004) was found between the frequencies of Small alleles in youths, which disappeared in centenarians (p = 0.290). Furthermore, the demographic-genetic approach revealed in Danes a significant gene-sex interaction relevant to Long alleles (more than 37 repeats). The different findings in Denmark and Italy suggest that gene/longevity associations are population-specific, and heavily affected by the population-specific genetic and environmental history.

A logistic regression model for measuring gene–longevity associations

The logistic regression model is a popular model for data analysis in epidemiological research. In this paper, we use this model to analyze genetic data collected from gene–longevity association studies. This new approach models the probability of observing one genotype as a function of the age of investigated individuals. Applying the model to genotype data on the TH and 3′ApoB‐VNTR loci collected from an Italian centenarian study, we show how it can be used to model the different ways that genes affect survival, including sex‐ and age‐specific influences. We highlight the advantages of this application over other available models. The application of the model to empirical data indicates that it is an efficient and easily applicable approach for determining the influences of genes on human longevity.

GxE interactions between FOXO genotypes and drinking tea are significantly associated with prevention of cognitive decline in advanced age in China.

Logistic regression analysis based on data from 822 Han Chinese oldest old aged 92+ demonstrated that interactions between carrying FOXO1A-266 or FOXO3-310 or FOXO3-292 and tea drinking at around age 60 or at present time were significantly associated with lower risk of cognitive disability at advanced ages. Associations between tea drinking and reduced cognitive disability were much stronger among carriers of the genotypes of FOXO1A-266 or FOXO3-310 or FOXO3-292 compared with noncarriers, and it was reconfirmed by analysis of three-way interactions across FOXO genotypes, tea drinking at around age 60, and at present time. Based on prior findings from animal and human cell models, we postulate that intake of tea compounds may activate FOXO gene expression, which in turn may positively affect cognitive function in the oldest old population. Our empirical findings imply that the health benefits of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles.

Interaction Between the FOXO1A-209 Genotype and Tea Drinking Is Significantly Associated with Reduced Mortality at Advanced Ages

On the basis of the genotypic/phenotypic data from Chinese Longitudinal Healthy Longevity Survey (CLHLS) and Cox proportional hazard model, the present study demonstrates that interactions between carrying FOXO1A-209 genotypes and tea drinking are significantly associated with lower risk of mortality at advanced ages. Such a significant association is replicated in two independent Han Chinese CLHLS cohorts (pu2009=u20090.028-0.048 in the discovery and replication cohorts, and pu2009=u20090.003-0.016 in the combined dataset). We found the associations between tea drinking and reduced mortality are much stronger among carriers of the FOXO1A-209 genotype compared to non-carriers, and drinking tea is associated with a reversal of the negative effects of carrying FOXO1A-209 minor alleles, that is, from a substantially increased mortality risk to substantially reduced mortality risk at advanced ages. The impacts are considerably stronger among those who carry two copies of the FOXO1A minor allele than those who carry one copy. On the basis of previously reported experiments on human cell models concerning FOXO1A-by-tea-compounds interactions, we speculate that results in the present study indicate that tea drinking may inhibit FOXO1A-209 gene expression and its biological functions, which reduces the negative impacts of FOXO1A-209 gene on longevity (as reported in the literature) and offers protection against mortality risk at oldest-old ages. Our empirical findings imply that the health outcomes of particular nutritional interventions, including tea drinking, may, in part, depend upon individual genetic profiles, and the research on the effects of nutrigenomics interactions could potentially be useful for rejuvenation therapies in the clinic or associated healthy aging intervention programs.

Haplotype Effects on Human Survival: Logistic Regression Models Applied to Unphased Genotype Data: Inferring Haplotype Effects on Human Survival

Haplotype based linkage disequilibrium (LD) mapping exhibits higher power than the single locus approach because it makes use of the LD information contained in the flanking markers. New statistical methods have been proposed to help to infer haplotype effects on human diseases using multi‐locus genotype data collected from unrelated individuals. In this paper, we introduce a statistical procedure for measuring haplotype effects on human survival using the popular logistic regression model with haplotype based parameterizations. By modeling haplotype frequency as a function of age, our model infers haplotype effects by estimating and testing the slope parameters under different genetic mechanisms (multiplicative, dominant, or recessive). In addition, by estimating the sex‐specific slope parameters, our model allows the detection of sex‐specific haplotype effects or haplotype‐sex interactions. As an example, we apply our model to an empirical dataset on a stress related gene, interleukin‐6, to look for haplotypes that affect individual survival and for haplotype‐sex interactions. We show that our logistic regression based haplotype model can be a helpful tool for researchers interested in the genetics of human aging and longevity.