Genetic Influences in Later Life
Greetings,
Here is the first draft of our invited chapter "Genetic Influences in Later Life", written for the upcoming Encyclopedia of the Life Course and Human Development.
Comments and suggestions are welcome! Please post your comments below by clicking here.
Genetic influences are the influences that can be attributed to heredity (family likeness). Heredity is the passing of characteristics (traits) from parents to offspring. Genetic influences in later life are attributed to traits related to aging such as: lifespan and longevity, age at menopause, age at onset of specific diseases in late life (Alzheimer's disease, heart disease, etc.), physical health and cognitive functioning in later life, rate of aging (estimated through tests for biological age), rate-of-change traits, and biomarkers of aging (Finch, 2007).
Genetic influences are also related to effects of the fundamental chemical units of heredity called genes. Gene is a segment of deoxyribonucleic acid, DNA, carrying coded hereditary information. The number of gerontogenes (genes involved in the aging process) remains to be established, but there are no doubts of their existence. For example, in humans one of the forms of a gene coding apolipoprotein E (APOE2) is associated with exceptional longevity (more prevalent among centenarians) and decreased susceptibility to Alzheimer's disease (Finch, 2007; Martin, Bergman, & Barzilai, 2007).
Genetic influences operate through the mechanism of gene action - the way in which genes produce their effect on an organism by influencing biochemical processes during development and aging. Many of the genes within a given cell are inactive much or even all the time (repressed). Different genes can be switched on or off depending on cell specialization (differentiation) - a phenomenon called differential gene expression. Gene expression may change over time within a given cell during development and aging. Changes in differential gene expression are vitally important for cell differentiation during early child development, but they may persist further in later life and become the driving force of the aging process.
Although genes determine the features an organism may develop, the features that actually develop depend upon the complex interaction between genes and their environment, called gene-environment interaction. Gene-environment interactions are important because genes produce their effects in an indirect way (through proteins) and, therefore, the ultimate outcome of gene action may be different in different circumstances (Ryff & Singer, 2005). Although genes do not change over the life course (creating impression of causal links) many traits in later life demonstrate very high environmental plasticity (Ryff & Singer, 2005).
Older adults on average experience poorer health compared to younger adults, so genetic contribution to health, functional status, and cognition are among the most thoroughly studied traits in later life.
Methods and designs in the studies of genetic influences on later life
Most studies of genetic influences use quantitative genetics (or ACE) models to separate the sources of phenotypic variability into an Additive genetic component (A), a Common or shared environment component (C), and a non-shared Environment element (E). Shared environmental influences are shared nongenetic factors that are transmitted from parents to offspring or are shared by the members of the same family (like lifestyle or diet). Nonshared environmental influences are nongenetic factors that are different among family members. Genetic contribution to phenotypic variability of trait is measured using heritability estimates. Heritability estimates represent the proportion of phenotypic variation of trait that can be explained by genetic effects. A heritability value of 1.0 (or 100%) means that the trait is fully genetically determined while the zero value means that the trait is fully environmentally determined.
Quantitative genetics uses a number of designs in the study of genetic influences:
Family design compares the incidence of disease (or other trait) among biological and non-biological relatives of an affected individual (called proband). Famous statistician Karl Pearson and telephone inventor Graham Bell were among the first researchers who tried to estimate contribution of genetic factors into human lifespan at the beginning of the twentieth century (see review in Gavrilov, Gavrilova, Olshansky, & Carnes, 2002). The first comprehensive studies of familial resemblance and longevity go back to the 1930s when American biostatistician Raymond Pearl published his seminal book “The Ancestry of the Long-Lived,” which showed that close relatives of nonagenarians live longer than relatives of shorter-lived persons (Pearl & Pearl, 1934). This initial finding was later replicated by numerous studies of persons with exceptional longevity including the most recent studies of centenarians (Martin et al., 2007).
The twin design is based on comparison of identical (monozygotic, MZ) twins and fraternal (dizygotic, DZ) twins. MZ twins are assumed to be genetically identical to each other because they developed from the same fertilized egg. DZ twins are formed from two different fertilized eggs and share only half of genes. If the trait is genetically influenced then MZ twins should have better resemblance to each other compared to DZ twins. Franz Kallmann was the first researcher who applied twin design to the study of late life traits and conducted a survey of old twin pairs (Kallmann & Sander, 1948).
The adoption method is a quasi-experimental design that is based on cases when children are adopted away from their biological parents early in life giving an opportunity for researchers to separate the effects of nature and nurture. The Swedish Adoption/Twin Study of Aging (SATSA) is probably the largest repository of data on adopted twins (Pedersen & Svedberg, 2000).
In addition to the methods of quantitative genetics, molecular genetic methods are used to identify specific genes responsible for genetic influence. In molecular genetic studies of human aging traits, the gene association studies remain the most common research approach (De Benedictis et al., 2001). In these studies the effect of candidate genes on longevity is analyzed by comparing gene frequencies between affected individuals (cases) and unaffected control individuals. Comparison of candidate gene frequencies among centenarians and younger controls is a typical example of such studies. Another molecular genetics approach – the genome-wide linkage scan of genes, is a relatively new direction of research. Linkage analysis is a mapping of genetic loci using observations of related individuals (pairs of affected and nonaffected siblings, for example). This direction of research has a potential for obtaining interesting results, although the success of genome-wide scans of complex human diseases requires large sample sizes, considerable effort and expense.
In addition to common phenotypic traits (like presence or absence of disease) genetic epidemiology of aging incorporates age in the specification of traits under study. The traits that are specific for later life are survival traits and rate-of-change traits (Hadley et al., 2000). A survival trait is defined in terms of specific age interval over which an individual is at risk for specific outcome. For example, early-onset and late-onset variants of Alzheimer’s disease are associated with different genes and modes of their action. Rate-of-change traits are defined as changes in physiological, cognitive or behavioral traits over a period of time. Study of genetic influences on rate-of-change traits is now a rapidly developing area of research (Pedersen & Svedberg, 2000).
Research on genetic influences in later life
Longevity is one of the most widely studied broad survival trait. It was shown that siblings and parents of persons with exceptional longevity have significantly lower mortality compared to population-based controls and offspring of long-lived parents live longer than the offspring of short-lived parents (Gavrilov et al., 2002; Martin et al., 2007). Genetic influences on longevity found in family studies were confirmed in twin and adoption studies (see Gavrilov et al., 2002). Although a strong familial clustering of longevity is now a well-established fact, heritability estimates for lifespan using standard methods of quantitative genetics are moderate: 20-30% (Cournil & Kirkwood, 2001). Heritability estimates by standard methods of quantitative genetics are based on the assumption of linear dependence between offspring and parental traits. However a study of more than 15,000 adult men and women from the European aristocracy demonstrated that familial resemblance in lifespan between parents and children is very small when parents live shorter lives (30-70 years) and very strong in the case of longer-lived parents (80+), suggesting an unusual non-linear pattern of lifespan inheritance (Gavrilov et al., 2002). These findings may explain the existing longevity paradox: although the heritability estimates for lifespan were reported to be rather low (Cournil & Kirkwood, 2001), it is well known that longevity runs in families (Martin et al., 2007). Recent study of Danish and Finnish twin cohorts confirmed that genetic influences on human lifespan are minimal before age 60 but increase thereafter (Hjelmborg et al., 2006).
A review of gene-longevity association studies revealed that different studies often produced inconsistent and even contradictory results (De Benedictis et al., 2001). The APOE locus is the only one that demonstrated consistency in different case-control gene-longevity studies (Finch, 2007). Apolipoprotein E (APOE) is a protein involved in cholesterol transport that binds to LDL receptor and is crucial to blood cholesterol levels (Finch, 2007). APOE4 allele was found to be associated with heart disease, Alzheimer’s disease and longevity. APOE3 is the most prevalent allele in human populations, while APOE4 may vary being higher than 15% in Northern Europe and among aboriginal populations of New Guinea and Australia (Finch, 2007). A post hoc meta analysis of eight APOE findings showed a net odds ratio for extreme longevity of 0.51 for the E3/E4 against the common E3/E3 genotype (Melzer, Hurst, & Frayling, 2007). Prevalence of APOE4 decreases with age due to differential survival - mortality of E4 carriers is 10-14% higher and mortality of E2 carriers 4-12% lower compared to E3/E3 and E4/E2 genotypes. It was estimated that individuals with E4/E4 genotype may have 5 years shorter life expectancy at age 65 compared to individuals with E2/E2 and E2/E3 genotypes (Ewbank, 2004). Linkage studies of longevity genes are less common compared to association studies. One genome-wide linkage study of the U.S. centenarians found a suggestive locus at chromosome 4, although this finding was not replicated in other populations (Melzer et al., 2007).
Parental age is another genetically-linked factor affecting longevity. Children conceived by fathers at older age have more inborn mutations (Vogel & Motulsky, 1997) and may be at higher risk of Alzheimer's disease and prostate cancer in later life. Daughters conceived by fathers age 45 and older live shorter lives (on average), while sons seems to be unaffected, suggesting the possible role of mutations on paternal X chromosome (inherited by daughters only) in the aging process (Gavrilov et al., 2002).
Most chronic diseases in later life are complex multifactorial disorders. Multifactorial disorders are influenced by multiple genes, often coupled with the effects of environmental factors. Many diseases common to old age, such as late-onset Alzheimer’s disease, heart disease, diabetes are now considered to be multifactorial disorders. Most genes associated with multifactorial disorders have low penetrance, which means that the likelihood of developing disease among genotype carriers is low. Thus, the individuals with disease-related genes do not necessarily succumb to disease (Ryff & Singer, 2005). With favorable lifestyle and environment there is an opportunity for individual with genetic risk factor to delay and even to avoid the disease. For example, in the 1960s and 1970s, population of North Karelia in Finland had very high levels of heart disease and a significant proportion of people carrying mutations predisposing to familial hypercholesterolemia. However, an intensive community-based intervention program directed to lifestyle improvement resulted in significantly (by 60-70%) reduced heart disease and cancer rates over the span of 25 years (Ryff & Singer, 2005). Thus, the genetic risks of diseases in later life can be substantially reduced by proper behavioral, social and economic measures.
Alzheimer’s disease is the most common cause of severe memory loss at older ages. For late life forms of dementia, APOE4 allele was found to be strongly associated with both late onset Alzheimer’s disease and accelerated cognitive decline after age 65 (Finch, 2007). The highest cognitive decline was observed in APOE4 carriers with diabetes, carotid atherosclerosis and peripheral vascular disease. APOE4 carriers with mild cognitive impairment were 2-5 times more likely to develop Alzheimer’s disease compared to the most common APOE3 genotype. Another APOE allele, the APOE2, was found to be protective against Alzheimer’s disease (Finch, 2007).
Many biomarkers of physiological or functional status including grip strength, walk speed, systolic blood pressure, pulmonary function, fasting glucose and bone degeneration demonstrate a high heritability at older ages (Melzer et al., 2007). Integral estimates of biological age have also been shown to have a strong genetic component, with heritability estimates ranging from 27% to 57%. Age at natural menopause was found to be highly heritable: data from the two generations of the Framingham Heart Study showed that the crude and multivariable-adjusted heritability estimates for age at natural menopause were 0.49 and 0.52 (Murabito, Yang, Fox, Wilson, & Cupples, 2005). Sex hormone levels play an important role in health and survival at older ages. Study of male twins aged 59-70 found that plasma testosterone levels have substantial genetic variation while estrogen concentrations were largely influenced by environmental factors.
Cognitive functioning also shows a significant genetic component. Most studies of cognitive abilities in later life were conducted using studies of older twins, which showed that the overall cognitive functioning in older age is highly heritable with estimates of heritability equal to 76% in Danish twins aged 70+ and 62% in Swedish twins aged 80+ (Melzer et al., 2007).
Recently researchers started to collect information on genetic influences at different ages as well as on rate-of-change traits (Pedersen & Svedberg, 2000). Studies show that phenotypic variability has a tendency to increase with age for the majority of traits due to nonshared environmental effects. Genetic contribution to variability in cognitive abilities shows stability until ages 65-70 and decline thereafter. A similar pattern was found for self-rated health. Rate-of-change traits usually demonstrate a lower heritability compared to the absolute levels of studied traits. This was found to be the case for such traits as cognitive performance, body mass index and lipid and lipoprotein levels. Thus, the rate-of-change traits apparently are not significantly affected by genetic factors.
Gene-environment interactions, denoted GxE, represent one of the most important and promising areas for the studies of life course. Gene-environment interactions refer to differential genetic sensitivity to specific environmental factors. Genetic factors often act as effect modifiers (or moderators) when effects of socio-economic or behavioral factors are analyzed. For example, there is no increase in risk for Alzheimer’s disease among persons with head injury if they do not carry APOE4 gene. However, for carriers of APOE4 head injury results in a 10-fold increase in the risk of Alzheimer’s disease. Similarly, APOE4 was found to be a risk factor for ischemic heart disease but this applied mainly to smokers (Ryff & Singer, 2005).
It should be noted that heritability estimates for late life traits may vary significantly across populations and populations living in less favorable environments generally demonstrate smaller effects of genetic factors on variability of late life traits. For example, heritability of forced respiratory volume in Russian twins was found to be much smaller compared to Swedish twins most likely due to differences in environmental influences between Russian and Swedish samples (Whitfield, Brandon, & Wiggins, 2002).
Research challenges and future directions
Studies of genetic influences in later life face many methodological challenges. The main problem of these studies is the fact that many individuals do not survive to late ages and this survival is affected by both environmental and genetic factors. Twin studies often suffer from the limitations of cross-sectional design when it is impossible to distinguish between selection effects (genetically determined differential survival) from true aging changes (Pedersen & Svedberg, 2000). Studies on exceptional longevity often suffer from the lack of data on living relatives including parents (Hadley et al., 2000). Inconsistency in the findings of many gene-longevity association studies may be due to the lack of proper control groups in these case-control studies, because cases (centenarians) and controls (young adults) belong to different birth cohorts with different past histories. Thus, comparison of centenarians and young adults is susceptible to artifacts resulting from differences in genetic makeup between different age cohorts unrelated to differential survival. Collection of longitudinal data for twins and adoptees will alleviate the problems posed by cross-sectional designs and help to discriminate between selection processes and true aging changes. Collection of biomarkers (including genetic markers) that is currently underway in many population surveys and longitudinal studies will fill the gap in our knowledge about association between specific genetic markers and later life traits. The most promising areas of research – gene-environment interactions in later life and early-life genetic influences on late life traits are at the beginning of their development and will shape the future life course research of genetic influences in later life (Pedersen & Svedberg, 2000; Ryff & Singer, 2005).
Policy issues
Rapid development of molecular genetics and possibilities of individual genome scans in the nearest future raise serious ethical concerns about proper use of individual genetic information. Should individuals be informed about genetic risks for chronic diseases in later life or this information may result in unnecessary stress? Older persons having genes predisposing to risk of certain diseases (like APO4 gene) may be unfairly treated by insurance companies. These problems require both confidentiality protection of sensitive genetic information and education of public that having genes predisposing to late onset diseases is not a destiny and individuals with unfavorable genotypes may never develop a disease (Ryff & Singer, 2005). On the other hand, knowledge about genetic markers predisposing to late life diseases may help to develop intervention measures specific for individual genetic make-up. Such personalized approach may become a core of medical care in the nearest future.
References
Cournil, A., & Kirkwood, T. B. L. (2001). If you would live long, choose your parents well. Trends in Genetics, 17(5), 233-235.
De Benedictis, G., Tan, Q. H., Jeune, B., Christensen, K., Ukraintseva, S. V., Bonafe, M., et al. (2001). Recent advances in human gene-longevity association studies. Mechanisms of Ageing and Development, 122(9), 909-920.
Ewbank, D. (2004). From Alzheimer's disease to a demography of chronic disease: the development of demographic synthesis for fitting multistate models. In L. J. Waite (Ed.), Aging, Health, and Public Policy: Demographic and Economic Perspectives.
Supplement to Population and Development Review 30. Malden, MA: Blackwell.
Finch, C. E. (2007). The Biology of Human Longevity: Inflammation, Nutrition, and Aging in the Evolution of Lifespans. Amsterdam, etc: Elsevier.
Gavrilov, L. A., Gavrilova, N. S., Olshansky, S. J., & Carnes, B. A. (2002). Genealogical data and the biodemography of human longevity. Social Biology, 49(3-4), 160-173.
Hadley, E. C., Rossi, W. K., Albert, S., Bailey-Wilson, J., Baron, J., Cawthon, R., et al. (2000). Genetic epidemiologic studies on age-specified traits. American Journal of Epidemiology, 152(11), 1003-1008.
Hjelmborg, J. V., Iachine, I., Skytthe, A., Vaupel, J. W., Mcgue, M., Koskenvuo, M., et al. (2006). Genetic influence on human lifespan and longevity. Human Genetics, 119(3), 312-321.
Kallmann, F. J., & Sander, G. (1948). Twin Studies on Aging and Longevity. Journal of Heredity, 39(12), 349-356.
Martin, G. M., Bergman, A., & Barzilai, N. (2007). Genetic determinants of human health span and life span: Progress and new opportunities. Plos Genetics, 3(7), 1121-1130.
Melzer, D., Hurst, A. J., & Frayling, T. (2007). Genetic variation and human aging: Progress and prospects. Journals of Gerontology Series a-Biological Sciences and Medical Sciences, 62(3), 301-307.
Murabito, J. M., Yang, Q., Fox, C., Wilson, P. W. F., & Cupples, L. A. (2005). Heritability of age at natural menopause in the Framingham Heart Study. Journal of Clinical Endocrinology and Metabolism, 90(6), 3427-3430.
Pearl, R., & Pearl, R. D. W. (1934). The Ancestry of the Long-Lived.
Baltimore: The John Hopkins Press.
Pedersen, N. L., & Svedberg, P. (2000). Behavioral genetics, health, and aging. Journal of Adult Development, 7(2), 65-71.
Ryff, C. D., & Singer, B. H. (2005). Social environments and the genetics of aging: Advancing knowledge of protective health mechanisms. Journal of Gerontology, 60B(Special Issue I), 12-23.
Vogel, F., & Motulsky, A. G. (1997). Human Genetics: Problems and Approaches.
(3 ed.). Berlin: Springer-Verlag.
Whitfield, K. E., Brandon, D. T., & Wiggins, S. A. (2002). Sociocultural influences in genetic designs of aging: Unexplored perspectives. Experimental Aging Research, 28(4), 391-405.
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Here is the first draft of our invited chapter "Genetic Influences in Later Life", written for the upcoming Encyclopedia of the Life Course and Human Development.
Comments and suggestions are welcome! Please post your comments below by clicking here.
Genetic Influences in Later Life
Natalia S. Gavrilova, Ph.D. and Leonid A. Gavrilov, Ph.D., University of Chicago
Genetic influences are the influences that can be attributed to heredity (family likeness). Heredity is the passing of characteristics (traits) from parents to offspring. Genetic influences in later life are attributed to traits related to aging such as: lifespan and longevity, age at menopause, age at onset of specific diseases in late life (Alzheimer's disease, heart disease, etc.), physical health and cognitive functioning in later life, rate of aging (estimated through tests for biological age), rate-of-change traits, and biomarkers of aging (Finch, 2007).
Genetic influences are also related to effects of the fundamental chemical units of heredity called genes. Gene is a segment of deoxyribonucleic acid, DNA, carrying coded hereditary information. The number of gerontogenes (genes involved in the aging process) remains to be established, but there are no doubts of their existence. For example, in humans one of the forms of a gene coding apolipoprotein E (APOE2) is associated with exceptional longevity (more prevalent among centenarians) and decreased susceptibility to Alzheimer's disease (Finch, 2007; Martin, Bergman, & Barzilai, 2007).
Genetic influences operate through the mechanism of gene action - the way in which genes produce their effect on an organism by influencing biochemical processes during development and aging. Many of the genes within a given cell are inactive much or even all the time (repressed). Different genes can be switched on or off depending on cell specialization (differentiation) - a phenomenon called differential gene expression. Gene expression may change over time within a given cell during development and aging. Changes in differential gene expression are vitally important for cell differentiation during early child development, but they may persist further in later life and become the driving force of the aging process.
Although genes determine the features an organism may develop, the features that actually develop depend upon the complex interaction between genes and their environment, called gene-environment interaction. Gene-environment interactions are important because genes produce their effects in an indirect way (through proteins) and, therefore, the ultimate outcome of gene action may be different in different circumstances (Ryff & Singer, 2005). Although genes do not change over the life course (creating impression of causal links) many traits in later life demonstrate very high environmental plasticity (Ryff & Singer, 2005).
Older adults on average experience poorer health compared to younger adults, so genetic contribution to health, functional status, and cognition are among the most thoroughly studied traits in later life.
Methods and designs in the studies of genetic influences on later life
Most studies of genetic influences use quantitative genetics (or ACE) models to separate the sources of phenotypic variability into an Additive genetic component (A), a Common or shared environment component (C), and a non-shared Environment element (E). Shared environmental influences are shared nongenetic factors that are transmitted from parents to offspring or are shared by the members of the same family (like lifestyle or diet). Nonshared environmental influences are nongenetic factors that are different among family members. Genetic contribution to phenotypic variability of trait is measured using heritability estimates. Heritability estimates represent the proportion of phenotypic variation of trait that can be explained by genetic effects. A heritability value of 1.0 (or 100%) means that the trait is fully genetically determined while the zero value means that the trait is fully environmentally determined.
Quantitative genetics uses a number of designs in the study of genetic influences:
Family design compares the incidence of disease (or other trait) among biological and non-biological relatives of an affected individual (called proband). Famous statistician Karl Pearson and telephone inventor Graham Bell were among the first researchers who tried to estimate contribution of genetic factors into human lifespan at the beginning of the twentieth century (see review in Gavrilov, Gavrilova, Olshansky, & Carnes, 2002). The first comprehensive studies of familial resemblance and longevity go back to the 1930s when American biostatistician Raymond Pearl published his seminal book “The Ancestry of the Long-Lived,” which showed that close relatives of nonagenarians live longer than relatives of shorter-lived persons (Pearl & Pearl, 1934). This initial finding was later replicated by numerous studies of persons with exceptional longevity including the most recent studies of centenarians (Martin et al., 2007).
The twin design is based on comparison of identical (monozygotic, MZ) twins and fraternal (dizygotic, DZ) twins. MZ twins are assumed to be genetically identical to each other because they developed from the same fertilized egg. DZ twins are formed from two different fertilized eggs and share only half of genes. If the trait is genetically influenced then MZ twins should have better resemblance to each other compared to DZ twins. Franz Kallmann was the first researcher who applied twin design to the study of late life traits and conducted a survey of old twin pairs (Kallmann & Sander, 1948).
The adoption method is a quasi-experimental design that is based on cases when children are adopted away from their biological parents early in life giving an opportunity for researchers to separate the effects of nature and nurture. The Swedish Adoption/Twin Study of Aging (SATSA) is probably the largest repository of data on adopted twins (Pedersen & Svedberg, 2000).
In addition to the methods of quantitative genetics, molecular genetic methods are used to identify specific genes responsible for genetic influence. In molecular genetic studies of human aging traits, the gene association studies remain the most common research approach (De Benedictis et al., 2001). In these studies the effect of candidate genes on longevity is analyzed by comparing gene frequencies between affected individuals (cases) and unaffected control individuals. Comparison of candidate gene frequencies among centenarians and younger controls is a typical example of such studies. Another molecular genetics approach – the genome-wide linkage scan of genes, is a relatively new direction of research. Linkage analysis is a mapping of genetic loci using observations of related individuals (pairs of affected and nonaffected siblings, for example). This direction of research has a potential for obtaining interesting results, although the success of genome-wide scans of complex human diseases requires large sample sizes, considerable effort and expense.
In addition to common phenotypic traits (like presence or absence of disease) genetic epidemiology of aging incorporates age in the specification of traits under study. The traits that are specific for later life are survival traits and rate-of-change traits (Hadley et al., 2000). A survival trait is defined in terms of specific age interval over which an individual is at risk for specific outcome. For example, early-onset and late-onset variants of Alzheimer’s disease are associated with different genes and modes of their action. Rate-of-change traits are defined as changes in physiological, cognitive or behavioral traits over a period of time. Study of genetic influences on rate-of-change traits is now a rapidly developing area of research (Pedersen & Svedberg, 2000).
Research on genetic influences in later life
Longevity is one of the most widely studied broad survival trait. It was shown that siblings and parents of persons with exceptional longevity have significantly lower mortality compared to population-based controls and offspring of long-lived parents live longer than the offspring of short-lived parents (Gavrilov et al., 2002; Martin et al., 2007). Genetic influences on longevity found in family studies were confirmed in twin and adoption studies (see Gavrilov et al., 2002). Although a strong familial clustering of longevity is now a well-established fact, heritability estimates for lifespan using standard methods of quantitative genetics are moderate: 20-30% (Cournil & Kirkwood, 2001). Heritability estimates by standard methods of quantitative genetics are based on the assumption of linear dependence between offspring and parental traits. However a study of more than 15,000 adult men and women from the European aristocracy demonstrated that familial resemblance in lifespan between parents and children is very small when parents live shorter lives (30-70 years) and very strong in the case of longer-lived parents (80+), suggesting an unusual non-linear pattern of lifespan inheritance (Gavrilov et al., 2002). These findings may explain the existing longevity paradox: although the heritability estimates for lifespan were reported to be rather low (Cournil & Kirkwood, 2001), it is well known that longevity runs in families (Martin et al., 2007). Recent study of Danish and Finnish twin cohorts confirmed that genetic influences on human lifespan are minimal before age 60 but increase thereafter (Hjelmborg et al., 2006).
A review of gene-longevity association studies revealed that different studies often produced inconsistent and even contradictory results (De Benedictis et al., 2001). The APOE locus is the only one that demonstrated consistency in different case-control gene-longevity studies (Finch, 2007). Apolipoprotein E (APOE) is a protein involved in cholesterol transport that binds to LDL receptor and is crucial to blood cholesterol levels (Finch, 2007). APOE4 allele was found to be associated with heart disease, Alzheimer’s disease and longevity. APOE3 is the most prevalent allele in human populations, while APOE4 may vary being higher than 15% in Northern Europe and among aboriginal populations of New Guinea and Australia (Finch, 2007). A post hoc meta analysis of eight APOE findings showed a net odds ratio for extreme longevity of 0.51 for the E3/E4 against the common E3/E3 genotype (Melzer, Hurst, & Frayling, 2007). Prevalence of APOE4 decreases with age due to differential survival - mortality of E4 carriers is 10-14% higher and mortality of E2 carriers 4-12% lower compared to E3/E3 and E4/E2 genotypes. It was estimated that individuals with E4/E4 genotype may have 5 years shorter life expectancy at age 65 compared to individuals with E2/E2 and E2/E3 genotypes (Ewbank, 2004). Linkage studies of longevity genes are less common compared to association studies. One genome-wide linkage study of the U.S. centenarians found a suggestive locus at chromosome 4, although this finding was not replicated in other populations (Melzer et al., 2007).
Parental age is another genetically-linked factor affecting longevity. Children conceived by fathers at older age have more inborn mutations (Vogel & Motulsky, 1997) and may be at higher risk of Alzheimer's disease and prostate cancer in later life. Daughters conceived by fathers age 45 and older live shorter lives (on average), while sons seems to be unaffected, suggesting the possible role of mutations on paternal X chromosome (inherited by daughters only) in the aging process (Gavrilov et al., 2002).
Most chronic diseases in later life are complex multifactorial disorders. Multifactorial disorders are influenced by multiple genes, often coupled with the effects of environmental factors. Many diseases common to old age, such as late-onset Alzheimer’s disease, heart disease, diabetes are now considered to be multifactorial disorders. Most genes associated with multifactorial disorders have low penetrance, which means that the likelihood of developing disease among genotype carriers is low. Thus, the individuals with disease-related genes do not necessarily succumb to disease (Ryff & Singer, 2005). With favorable lifestyle and environment there is an opportunity for individual with genetic risk factor to delay and even to avoid the disease. For example, in the 1960s and 1970s, population of North Karelia in Finland had very high levels of heart disease and a significant proportion of people carrying mutations predisposing to familial hypercholesterolemia. However, an intensive community-based intervention program directed to lifestyle improvement resulted in significantly (by 60-70%) reduced heart disease and cancer rates over the span of 25 years (Ryff & Singer, 2005). Thus, the genetic risks of diseases in later life can be substantially reduced by proper behavioral, social and economic measures.
Alzheimer’s disease is the most common cause of severe memory loss at older ages. For late life forms of dementia, APOE4 allele was found to be strongly associated with both late onset Alzheimer’s disease and accelerated cognitive decline after age 65 (Finch, 2007). The highest cognitive decline was observed in APOE4 carriers with diabetes, carotid atherosclerosis and peripheral vascular disease. APOE4 carriers with mild cognitive impairment were 2-5 times more likely to develop Alzheimer’s disease compared to the most common APOE3 genotype. Another APOE allele, the APOE2, was found to be protective against Alzheimer’s disease (Finch, 2007).
Many biomarkers of physiological or functional status including grip strength, walk speed, systolic blood pressure, pulmonary function, fasting glucose and bone degeneration demonstrate a high heritability at older ages (Melzer et al., 2007). Integral estimates of biological age have also been shown to have a strong genetic component, with heritability estimates ranging from 27% to 57%. Age at natural menopause was found to be highly heritable: data from the two generations of the Framingham Heart Study showed that the crude and multivariable-adjusted heritability estimates for age at natural menopause were 0.49 and 0.52 (Murabito, Yang, Fox, Wilson, & Cupples, 2005). Sex hormone levels play an important role in health and survival at older ages. Study of male twins aged 59-70 found that plasma testosterone levels have substantial genetic variation while estrogen concentrations were largely influenced by environmental factors.
Cognitive functioning also shows a significant genetic component. Most studies of cognitive abilities in later life were conducted using studies of older twins, which showed that the overall cognitive functioning in older age is highly heritable with estimates of heritability equal to 76% in Danish twins aged 70+ and 62% in Swedish twins aged 80+ (Melzer et al., 2007).
Recently researchers started to collect information on genetic influences at different ages as well as on rate-of-change traits (Pedersen & Svedberg, 2000). Studies show that phenotypic variability has a tendency to increase with age for the majority of traits due to nonshared environmental effects. Genetic contribution to variability in cognitive abilities shows stability until ages 65-70 and decline thereafter. A similar pattern was found for self-rated health. Rate-of-change traits usually demonstrate a lower heritability compared to the absolute levels of studied traits. This was found to be the case for such traits as cognitive performance, body mass index and lipid and lipoprotein levels. Thus, the rate-of-change traits apparently are not significantly affected by genetic factors.
Gene-environment interactions, denoted GxE, represent one of the most important and promising areas for the studies of life course. Gene-environment interactions refer to differential genetic sensitivity to specific environmental factors. Genetic factors often act as effect modifiers (or moderators) when effects of socio-economic or behavioral factors are analyzed. For example, there is no increase in risk for Alzheimer’s disease among persons with head injury if they do not carry APOE4 gene. However, for carriers of APOE4 head injury results in a 10-fold increase in the risk of Alzheimer’s disease. Similarly, APOE4 was found to be a risk factor for ischemic heart disease but this applied mainly to smokers (Ryff & Singer, 2005).
It should be noted that heritability estimates for late life traits may vary significantly across populations and populations living in less favorable environments generally demonstrate smaller effects of genetic factors on variability of late life traits. For example, heritability of forced respiratory volume in Russian twins was found to be much smaller compared to Swedish twins most likely due to differences in environmental influences between Russian and Swedish samples (Whitfield, Brandon, & Wiggins, 2002).
Research challenges and future directions
Studies of genetic influences in later life face many methodological challenges. The main problem of these studies is the fact that many individuals do not survive to late ages and this survival is affected by both environmental and genetic factors. Twin studies often suffer from the limitations of cross-sectional design when it is impossible to distinguish between selection effects (genetically determined differential survival) from true aging changes (Pedersen & Svedberg, 2000). Studies on exceptional longevity often suffer from the lack of data on living relatives including parents (Hadley et al., 2000). Inconsistency in the findings of many gene-longevity association studies may be due to the lack of proper control groups in these case-control studies, because cases (centenarians) and controls (young adults) belong to different birth cohorts with different past histories. Thus, comparison of centenarians and young adults is susceptible to artifacts resulting from differences in genetic makeup between different age cohorts unrelated to differential survival. Collection of longitudinal data for twins and adoptees will alleviate the problems posed by cross-sectional designs and help to discriminate between selection processes and true aging changes. Collection of biomarkers (including genetic markers) that is currently underway in many population surveys and longitudinal studies will fill the gap in our knowledge about association between specific genetic markers and later life traits. The most promising areas of research – gene-environment interactions in later life and early-life genetic influences on late life traits are at the beginning of their development and will shape the future life course research of genetic influences in later life (Pedersen & Svedberg, 2000; Ryff & Singer, 2005).
Policy issues
Rapid development of molecular genetics and possibilities of individual genome scans in the nearest future raise serious ethical concerns about proper use of individual genetic information. Should individuals be informed about genetic risks for chronic diseases in later life or this information may result in unnecessary stress? Older persons having genes predisposing to risk of certain diseases (like APO4 gene) may be unfairly treated by insurance companies. These problems require both confidentiality protection of sensitive genetic information and education of public that having genes predisposing to late onset diseases is not a destiny and individuals with unfavorable genotypes may never develop a disease (Ryff & Singer, 2005). On the other hand, knowledge about genetic markers predisposing to late life diseases may help to develop intervention measures specific for individual genetic make-up. Such personalized approach may become a core of medical care in the nearest future.
References
Cournil, A., & Kirkwood, T. B. L. (2001). If you would live long, choose your parents well. Trends in Genetics, 17(5), 233-235.
De Benedictis, G., Tan, Q. H., Jeune, B., Christensen, K., Ukraintseva, S. V., Bonafe, M., et al. (2001). Recent advances in human gene-longevity association studies. Mechanisms of Ageing and Development, 122(9), 909-920.
Ewbank, D. (2004). From Alzheimer's disease to a demography of chronic disease: the development of demographic synthesis for fitting multistate models. In L. J. Waite (Ed.), Aging, Health, and Public Policy: Demographic and Economic Perspectives.
Supplement to Population and Development Review 30. Malden, MA: Blackwell.Finch, C. E. (2007). The Biology of Human Longevity: Inflammation, Nutrition, and Aging in the Evolution of Lifespans. Amsterdam, etc: Elsevier.
Gavrilov, L. A., Gavrilova, N. S., Olshansky, S. J., & Carnes, B. A. (2002). Genealogical data and the biodemography of human longevity. Social Biology, 49(3-4), 160-173.
Hadley, E. C., Rossi, W. K., Albert, S., Bailey-Wilson, J., Baron, J., Cawthon, R., et al. (2000). Genetic epidemiologic studies on age-specified traits. American Journal of Epidemiology, 152(11), 1003-1008.
Hjelmborg, J. V., Iachine, I., Skytthe, A., Vaupel, J. W., Mcgue, M., Koskenvuo, M., et al. (2006). Genetic influence on human lifespan and longevity. Human Genetics, 119(3), 312-321.
Kallmann, F. J., & Sander, G. (1948). Twin Studies on Aging and Longevity. Journal of Heredity, 39(12), 349-356.
Martin, G. M., Bergman, A., & Barzilai, N. (2007). Genetic determinants of human health span and life span: Progress and new opportunities. Plos Genetics, 3(7), 1121-1130.
Melzer, D., Hurst, A. J., & Frayling, T. (2007). Genetic variation and human aging: Progress and prospects. Journals of Gerontology Series a-Biological Sciences and Medical Sciences, 62(3), 301-307.
Murabito, J. M., Yang, Q., Fox, C., Wilson, P. W. F., & Cupples, L. A. (2005). Heritability of age at natural menopause in the Framingham Heart Study. Journal of Clinical Endocrinology and Metabolism, 90(6), 3427-3430.
Pearl, R., & Pearl, R. D. W. (1934). The Ancestry of the Long-Lived.
Baltimore: The John Hopkins Press.Pedersen, N. L., & Svedberg, P. (2000). Behavioral genetics, health, and aging. Journal of Adult Development, 7(2), 65-71.
Ryff, C. D., & Singer, B. H. (2005). Social environments and the genetics of aging: Advancing knowledge of protective health mechanisms. Journal of Gerontology, 60B(Special Issue I), 12-23.
Vogel, F., & Motulsky, A. G. (1997). Human Genetics: Problems and Approaches.
(3 ed.). Berlin: Springer-Verlag.Whitfield, K. E., Brandon, D. T., & Wiggins, S. A. (2002). Sociocultural influences in genetic designs of aging: Unexplored perspectives. Experimental Aging Research, 28(4), 391-405.
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