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Aging and Genetic Epidemiology Working Group Report

Monday, November 15, 1999

Bethesda, MD

Genetic Epidemiology Studies on Age-Specified Traits

November 1999

Evan C. Hadley (Rapporteur) 1 , Steven M. Albert 2 , Joan Bailey-Wilson 3 , John Baron 4 , Richard Cawthon 5 , Joe C. Christian 6 , Elizabeth H. Corder 7 , Claudio Franceschi 8 , Bert Kestenbaum 9 , Leonid Kruglyak 10 , Diane Lauderdale 11 , James Lubitz 12 , George M. Martin 13 , Gerald E. McClearn 14 , Matt McGue 15 , Toni Miles 16 , Geraldine Mineau 17 , Gail Ouellette 18 , Nancy L. Pedersen 19 , Samuel H. Preston 20 , William F. Page 21 , Michael Province 22 , Francois Schächter 23 , Nicholas J. Schork 24 , James W. Vaupel 25 , Jan Vijg 26 , Robert Wallace 27 , Eugenia Wang 28 , Ellen M. Wijsman 29

1 Geriatrics and Clinical Gerontology Program, National Institute on Aging, Bethesda, MD.

2 Gertrude H. Sergievsky Center, Columbia University, New York, NY.

3 National Human Genome Research Institute, Baltimore, MD.

4 Departments of Medicine and Community and Family Practice, Dartmouth Medical School, Hanover, NH.

5 Department of Human Genetics, University of Utah, Salt Lake City, UT.

6 Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN.

7 Center for Genetic Epidemiology, Odense University, Odense, Denmark.

8 Department of Biomedical Sciences, Section of General Pathology, Modena, Italy.

9 Social Security Administration, Baltimore, MD.

10 Whitehead Institute/MIT, Center for Genome Research, Cambridge, MA.

11 Department of Health Studies, University of Chicago, Chicago, IL.

12 Office of Strategic Planning, Health Care Financing Administration, Baltimore, MD.

13 Department of Pathology, University of Washington, Seattle, WA.

14 Center for Developmental and Health Genetics, Pennsylvania State University, University Park, PA.

15 Department of Psychology, University of Minnesota in Minneapolis, Minneapolis, MN.

16 Department of Family Practice, University of Texas Health Science Center, San Antonio, TX.

17 Division of Public Health Science, Department of Oncological Sciences, University of Utah, Salt Lake City, UT.

18 Genetic Epidemiology Lab., Algene Biotechnologies, Montreal, Quebec, Canada.

19 Institute for Environmental Medicine, The Karolinska Institute, Stockholm, Sweden.

20 Population Study Center, University of Pennsylvania, Philadelphia, PA.

21 NAS-NRC Twin Registry, Medical Follow-up Agency, Institute of Medicine, National Academy of Sciences, Washington, DC.

22 Division of Biostatistics,Washington Univ. School of Medicine at Washington Univ. Medical Center, St. Louis, MO.

23 CESTI-ISMCM, Universitè Lèonard de Vinci, France.

24 Department of Epidemiology and Biostatistics, MetroHealth Medical Center, Cleveland, OH.

25 Max Planck Institute for Demographic Research, Rostock, Germany.

26 Department of Physiology, University of Texas Health Science Center, San Antonio, TX

27 Department of Preventive Medicine, University of Iowa, Iowa City, IA.

28 The Bloomfield Center for Research in Aging, Lady Davis Institute for Medical Research, Montreal, Quebec, Canada.

29 Division of Medical Genetics, Department of Medicine; and Department of Biostatistics, University of Washington, Seattle, WA.


Genetic epidemiologic studies on diseases of aging are increasing rapidly, and will increase understanding of how genetic factors affect risks for them (1). Most have defined the trait of interest simply as presence or absence of a disease (or occurrence of an adverse event). However, individuals vary not only in whether or not they develop a disease, but also in the ages when they do, and in the rate at which pre-morbid changes progress to disease. Further, success in avoiding a disease cannot be fully characterized without knowing the age range being considered. These issues can be addressed by including age in the specification of the traits for which risk factors are to be studied.

The purpose of this report is to call attention to the value of epidemiologic studies to identify genetic effects on two types of age-specified traits : the age at which a specified outcome occurs or has not occurred ("survival traits"), and the rate at which a property changes over a specified age interval ("rate-of-change" traits). Information on an individual's genetic characteristics (genotype) is particularly useful for studies of age-specified traits because, unlike many risk factors, one's inherited genotype is stable over time; its ascertainment does not require sequential measurements or retrospective data. (This report does not address effects of somatic mutations that an individual may acquire over the life span.) We discuss insights to be gained by studying genetic factors affecting age-specified traits, and methodologic issues and resources relating to them.

When referring to studies on genetic effects, we include both 1) studies to identify regions of the genome that contain genes affecting a trait (genome scans), followed by identification of these genes, and 2) studies of effects of specific alleles on a trait. (In this report, we use genotype when referring to an individual's genetic characteristics in a general sense, and allele when referring to a particular gene polymorphism or mutation.)

This report is based on information prepared by the Aging and Genetic Epidemiology Working Group, convened by the National Institute on Aging to review opportunities for research on the genetic epidemiology of aging-related outcomes.


As a prelude to the following comments, we note that studying age-specified traits can identify protective factors as well as risk factors. The identification of genetic factors contributing to "successful aging" such as extended survival without an adverse outcome, or lack of decline in function, is an important potential contribution of genetic epidemiologic studies of aging. We also note that epidemiologic approaches can be valuable not only by identifying genetic determinants of the variance of age-specified traits in populations, but also genotypes responsible for rare extreme values of these traits, which can illuminate mechanisms affecting them in greater numbers of persons.

Survival Traits

Including age of onset in definitions of traits can allow one to distinguish factors that affect risk for a disease at different ages. A survival trait can be defined in terms of the age range over which an individual is at risk for an outcome such as disease onset or a morbid event. Here, survivorship could be defined as a continuous variable (years surviving from birth, or from a given age, until the outcome) or as a dichotomous variable (e.g., occurrence or non-occurrence of the outcome before a given age, or between one age and another).

This approach has commonly used two age ranges (e.g., early- and late onset of a disease). However, adequate sample sizes could allow several age ranges (e.g. onset before age 60 years, onset between age 60 and 75 years, onset after age 75 years) to be compared. Differences between genetic risk factors contributing to early-onset familial cases of conditions such as Alzheimer's disease and those contributing to late- onset nonfamilial cases are well-appreciated, suggesting that analogous differences could exist among age ranges for non-familial forms of other age-related diseases.

Distinguishing survival traits by the age intervals over which survival is measured has other potential advantages. Many age-related conditions are likely to be highly polygenic, posing difficulties for identifying genes with effects. The number of genes with effects on risk within any one age interval could be fewer than the number affecting risk over the entire life span, and thus more readily detectable. Studying age-specific survival effects may also avoid confusion due to genes that have beneficial effects in one age range and harmful ones in another. The importance of such genes in aging has been predicted based on evolutionary biological considerations (2).

The importance of differentiating among age intervals in searches for genetic risk factors for disease in old age is reinforced by the substantial mortality over this age range. To be at risk for a disease in very old age, one must first survive to it. Evidence discussed below indicates that the likelihood of survival to advanced age is influenced by genetic factors. This would lead to genetic differences among successively older age cohorts. In view of probable gene-gene interactions affecting disease risks, these cohort differences could affect the risks or protection conferred by any one allele.

Broad Survival Traits

Identifying a genotype's effects on time to onset of a particular condition does not determine its effects on overall survival or risk for other conditions (3). This is particularly important because risk factors for one age-related disease can protect against another (e.g., obesity predisposes to osteoarthritis but protects against osteoporosis). This may be true for many genetic risk factors. A lack of effect on life span and/or disability by a genotype with large effects on disability and/or mortality from a common disease suggests opposite effects on disability, morbidity or mortality from some other disease. Broad survival traits such as longevity, active life expectancy, or health expectancy can clarify the public health significance of disease-specific findings:

Longevity. Longevity per se is of medical and societal interest, and usually relatively easy to measure. Longevity may be characterized simply by age of death, or by more sophisticated measure such as integrated mortality risk (4), derived from the sum of yearly mortality risks until death, which adjusts for birth cohort and other factors.

Active Life Expectancy and Health Expectancy. Using longevity as a trait, without additional information on functional status, cannot resolve whether a factor promotes healthy, functional survival, or survival with disability. This has stimulated interest in measures of active life expectancy (ALE), the duration of survival without disability (5,6). The ratio of ALE to life span is also of interest, particularly of its effects on resources needed to support dependent older persons, and could be addressed by measuring both ALE and longevity in the same study (7). Health expectancy (survival without any one of a specified set of diseases) is also a trait of public health interest (8,9).

Outcome rates for these broad survival traits generally rise exponentially with age, although in extreme old age the rate of rise may lessen, at least for mortality (10). The pattern of this rise in risk with age is an important aspect of these traits. For example, a genotype's effects would have differing biologic, medical, and demographic implications depending on whether it 1) primarily affects outcomes in middle-age or early old age, but had little effect on survival to very advanced age, or 2) affects the rate of exponential increase in outcomes across all ages, thus substantially increasing the proportion surviving to very advanced age.

Study of broad survival traits could also facilitate detection of genes affecting two important characteristics whose genetic influence might be missed in studies on disease-specific survival traits:

Delayed or accelerated risk for multiple diseases. A genotype that delayed onset of several diseases (or one that accelerated several diseases) could affect survival significantly, but be undetected in disease-specific studies if its effect on any one disease were small.

Unrecognized age-related pathologies. Many important pathologies of aging may remain to be defined. Recent findings indicate pathologic significance of age-related changes such as vascular stiffening and loss of muscle mass (11) which were until recently considered "normal" aging. Genes with effects on unrecognized pathologies might not be detected in studies on disease-specific survival but could be detected through genome scans to identify loci affecting broader outcomes.

Rate-of-change Traits

Genetic factors may influence not only physiologic functions at one time point, but also their rates of change with age, which can influence whether and when disease occurs. Rates of change with age in many physiologic functions directly affect risk of age-related morbidity (e.g., changes in bone density, vital capacity, cognitive function, lens opacity, and blood pressure). Another important group includes rates of decline in homeostatic functions, such as glucose tolerance, blood pressure stability, and balance. Rates of change with age in cellular and biochemical properties implicated in age-related pathologies could also be studied (12-16), but data are needed on the predictive validity of such changes for mortality, age-related diseases, or functional status.

As with survival traits, there are benefits from distinguishing among different age ranges, including intervals before pathologic consequences occur. The latter are of particular interest regarding disease prevention strategies. The feasibility of using rate of change of a function as a trait is affected not only by the function's measurement properties at one time point, but also by the properties of measures of its change. Methodologies to analyze such data are being refined (17,18).


Many issues applying to genetic epidemiologic studies of aging are shared with those applying to genetic epidemiology in general, which have been thoroughly reviewed elsewhere (19). In this section, we confine ourselves to topics especially relevant to aging studies.

Current data on genetic contributions to age-specified traits

The limited current data presented below on genetic contributions to age-specified traits suggest that it is feasible to identify specific genetic effects on such traits in epidemiologic studies. However, for many age-specified traits, more data on their heritability and/or degree of familial aggregation are needed to determine which are suitable for study.

Studies of age-related diseases have found a relationship between age of onset and the magnitude of genetic contributions to risk (20,21), and contributions of specific genes (22,23). The only broad survival trait for which extensive data on genetic contributions exist is longevity. Twin studies indicate that it is moderately heritable, estimates generally falling between 25 and 35 percent. (24-29) Data also suggest a genetic contribution to extended longevity (30). The moderate heritability of longevity in these studies reinforces the importance of environmental and other factors. Nonetheless, in terms of attributable risk, genetic effects on longevity in the population are important: Because of the breadth of the outcome (all-cause mortality), their share of the variance in life span accounts for a number of person-years of life equivalent to that of a much higher share of the variance of any disease-specific mortality.

The gene with most evidence for a relationship to longevity is APOE . Studies in different populations noted differential survival as a function of APOE genotype (31-36). Increased longevity has been associated with other genetic conditions affecting lipoproteins (32,37-47), while one study found no association with lipoprotein polymorphisms (48). Other studies noted associations between longevity and alleles with other cardiovascular effects (31,49,50), with others showing no relationship (51,52).

The few studies on heritability of rate-of-change traits indicate that genetic factors significantly affect several aspects of physiologic aging, including changes in body mass index (53), LDL- and HDL-cholesterol (54), and weight (55). A study found no significant heritability of rate of bone loss in men (56). APOE genotype is related to rates of age-related change in total and LDL-cholesterol (57) and to rate of cognitive decline (58,59).

Most relationships between specific alleles and these traits have been found in association rather than linkage or allele-sharing studies. Caveats in interpreting such studies are discussed in the following section.

Design issues

General issues affecting design choices in genetic epidemiologic studies have been thoroughly reviewed elsewhere (60-63). Certain aging-related issues are especially germane to these choices:

Age-cohort-related artifacts in association studies . There is a high risk of false-positive results in association studies on the effects of a particular allele, due to differences in the genetic background of cases and controls unrelated to the outcome being studied. The risk is especially high for association studies of genetic effects on longevity based on comparisons of allele frequencies in the very old with those in the young or middle-aged. Immigration, internal migration, and intermarriage have caused large differences in the genetic makeup of different age cohorts in most areas, unrelated to their longevity. These problems probably have contributed to inconsistent results among many of these studies, e.g., on associations between HLA alleles and longevity (64-73). Concerns about the above problems can be alleviated by confirmatory association studies in a variety of populations, as with the APOE locus and longevity.

Lack of parental genotype data in studies on traits expressed in old age . Study designs using comparisons of relatives (linkage studies, allele-sharing studies, and family-based association studies) can avoid the above population-stratification artifacts (60,74,75). However, these can be hampered by lack of parental genotype data, which are usually unavailable for studies of traits expressed in old age. Methods have been developed to improve inferences in the absence of parental genotype data (76-78).

Characterization of longevity as a trait , Treating longevity as a continuous variable poses practical challenges in genetic epidemiologic studies. It is difficult to obtain DNA from subjects if one waits until their trait (i.e., age at death) is known. Though DNA could be collected prospectively and the study completed after enough individuals die, this would take longer. An alternative is to study deceased participants in family studies in which DNA was stored, but such subjects are presently few. Another option is to deduce genotypes of the deceased by genotyping their spouses and children. A common strategy, used for studies of extended longevity, defines longevity as a dichotomous trait (e.g. survival to age 90 years). DNA can be collected from persons who exceed the age chosen for the dichotomizing cut-point.

Age differences within relative pairs . The effects of a particular gene on an outcome may vary with age. This poses potential problems in applying common methods using relative pairs, such as siblings, to identify genes with effects on age-specified traits. Because of this consideration, there are advantages to studying relatives of very similar ages. Dizygotic twins are particularly useful because they usually also share the same environment in early life (79).

Though both extreme concordant- and discordant- relative pair comparisons can have high statistical power (80), the former may often be preferable for studies of survival outcomes, even if DNA from both members of extreme discordant pairs were available: Secular changes in causes of death and morbidity can confound interpretation of findings from siblings who vary greatly in the ages at which an event occurs. This problem is exacerbated for broad survival traits, because of differences between older and younger persons in causes of morbidity and mortality. However, extremely discordant pairs of similar ages may be very desirable for studying rate-of-change traits.

Issues for studies on broad survival traits. These traits are "broader" than disease-specific traits in that each is determined by the occurrence of any one of a heterogeneous set of outcomes. (For example, longevity is determined by any cause of death, and disability may be caused by a variety of diseases.) They hence are likely to be affected by many more genes than those that affect risk for a single disease, making effects of any one gene hard to detect. (However, because genotypes that increase risk for one fatal or disabling disease may decrease risk for another, the number of genes affecting a broad survival trait could be less than the sum of genes affecting all the diseases that influence it.)

Nonetheless, broad survival traits are likely to be highly genetically complex. To maximize the chance of identifying genes with effects, it is useful to collect a broad range of information on other more specific traits, including life history data. This can be used to minimize heterogeneity by permitting narrowly defined selection criteria, or to stratify the sample. Subjects who share multiple phenotypic features may also share a simpler and more detectable genetic etiology. The reduction in sample size that this permits may counterbalance extra costs per subject of obtaining detailed trait information.

Candidate genes

Given the numerous genes that might affect age-specified traits, and the shortage of information on which might be most important, there is a strong case to begin investigations of effects of genetic polymorphisms on these traits with genome-wide searches. Following linkage with a genomic region, candidate genes in this region could be tested. In the absence of information from genome scans, candidate gene approaches could be useful nonetheless if there is strong evidence that a gene might affect an age-specified trait in humans.

For disease-specific survival traits, studies on genes implicated by earlier human genetic epidemiologic studies that did not consider age of onset could further clarify their effects. Studies on genes identified in animal models of age- related diseases could also be useful. The above types of genes could also be examined for effects on broad survival traits and rate-of-change traits as well.

Potential candidate genes for epidemiologic studies of these traits can be identified from biological studies, and studies of human genetic syndromes. Certain classes of genes have been suggested based on evolutionary factors affecting selection (2,81,82), the importance of maintaining integrity of macromolecules and cell replacement (83), and effects of chronic caloric restriction in species in which this intervention extends life span (84). However, the utility of selecting candidate genes based on the above categories is limited because they include so many genes, and do not in themselves identify ones whose allelic variation is especially likely to affect human aging. The odds of finding such effects for a human candidate gene could be improved by focusing on the following types of genes:

Genes associated with selection for increased life spans in experimental animals, or in which allelic variation is related to life span . Studies of genetically selected or engineered animals indicate effects (including positive effects) of individual loci on longevity (85-87). Mutations causing increased life span in nematodes (88-93) have also been identified. There can be ambiguities in selecting human homologs for genes found in such studies, e.g., some gene families containing only one or a few genes in an animal model may have scores in humans.

Genes involved in progeroid syndromes . Rare mutations affect many aspects of senescence (e.g., Werner syndrome, Cockayne syndrome) (94,95). These have been termed "segmental" progeroid syndromes, in contrast to "unimodal" progeroid syndromes that affect specific traits expressed in late life (96,97). Study of the effects of polymorphisms and mutational variants of genes involved in progeroid syndromes (including alleles which that not cause the syndromes) could reveal mechanisms underlying the usual pathogenesis of age-related disorders, and may also reveal alleles causing unusually well -preserved health and function in old age (and possibly enhanced longevity) in significant segments of the population. One approach for genes involved in recessive progeroid syndromes, such as homocystinuria, is to examine aging and survival traits in heterozygotes who, for several such conditions, account for a significant percentage of the population.

Genes which affect human quantitative traits whose level in adults predicts mortality rates from multiple causes . Three such traits are resting heart rate (98), blood leukocyte count (99,100), and forced expiratory volume (101,102). These have been reported to have a heritability of approximately 60 percent (103-105). Studies on effects of such loci on rate-of-change phenotypes have the advantage of being feasible in young or middle-aged subjects who have fewer confounding effects of comorbidity than do many older subjects.

Populations and sampling frames

There are advantages and disadvantages of different types of populations for finding genetic effects on age-specified traits. Genetically homogeneous founder populations, particularly those for which demographic and other data are readily available, are of particular interest (106). It is also valuable to compare different populations to identify gene-gene and gene-environment interactions (107). Environmental interactions are especially important for age-specified traits because of birth cohort differences in environmental exposure, and environmental changes as individuals age.

Existing longitudinal studies provide particularly relevant populations for investigating genetic effects on rates of change with age. The diversity of populations in these studies allows genetic effects to be examined across a wide range of genetic backgrounds and environments. Several longitudinal studies in the United States are focused on aging, including national cohorts (108). Other relevant populations are found in longitudinal studies of chronic diseases, physiologic aging changes, disease and disability in the elderly, menopause, and the relationship of social, economic, and behavioral variables to age-related outcomes.

Some of these studies have data on participants' pedigrees, and on the longevity and health of their relatives (108). A few longitudinal studies include two or more family members as participants, exemplified by three Swedish longitudinal twin studies (109-112) and the Framingham Offspring Study (113), thus allowing study designs based on comparisons of relatives (such as linkage and allele-sharing approaches), which may be optimal in many cases. Most longitudinal studies currently do not include multiple family members, but could be expanded to do so. Conversely, extended follow-up of family-derived populations from family studies, genealogies, or twin registries provide opportunities for longitudinal genetic epidemiologic studies of aging.

In addition to other sampling frames discussed below, registries of very old people are potential sources of subjects for genetic epidemiologic studies. Though most do not contain pedigree data, they are useful for association studies and as a starting point for family studies. These include a Danish centenarian registry and family database, a cohort of approximately 4000 Italian centenarians (51,114,115), and a Netherlands panel of 800 persons over 85 years old (116). The Chronos Collection has information and specimens from 800 French centenarians and approximately 170 nonagenarian sibships (117). Many of these registries have banked DNA.

Large databases combining family, demographic and health information are particularly valuable for genetic epidemiologic studies of age-specified traits. These can provide information on many aging traits' frequency, measurement properties, and familial patterns, and could also allow very large sampling frames.

Many aspects of genetic epidemiologic studies of age-specified traits contribute to the need for these large sampling frames: Distinguishing among separate age ranges raises sample size requirements. The genetic complexity of many broad survival traits is likely to impose large sample size requirements for adequate statistical power. Conversely, strategies to reduce the complexity of these traits by stratifying subsets based on other characteristics also require large sampling frames to obtain enough subjects in each category. Studying extreme traits such as healthy nonagenarian status also will require a large population base from which to identify rare individuals. The large sampling frame requirements imposed by the above traits are increased further when (as will often be the case) the best study design requires families or relatives. Such studies may often not be feasible without very large sampling frames that include family information, since the yield of appropriate relatives or family structures will often be low.

Two approaches to generating large sampling frames combining demographic, health and family information are:

Linking national databases. Scandinavia has national demographic and health data sets that can or could be linked with national genealogic information (including longevity data) and family information. The U.S. does not have this range of national data sets, but additional linkages of American data sets could yield combined demographic, health and family data on individuals.

One such opportunity relates to databases of the Health Care Financing Administration (HCFA) and the Social Security Administration (SSA). The age of almost all Americans aged 65 and older can be tracked through HCFA's Medicare enrollment data, and linked to health data based on hospital and physician claims (118). However, with some exceptions, one cannot directly identify familial relationships from these data, although the potential to identify over 25,000 elderly twin pairs from the HCFA Enrollment Database has been demonstrated (119). SSA's NUMIDENT file contains information useful for identifying relatives, including place of birth, parents' names, and women's maiden names, and could be linked to HCFA databases. However, though HCFA has procedures to review requests to release data with personal identifiers and to contact beneficiaries identified through such data, SSA presently will not release such data.

A second potential national source of family data for linkage is the U.S. Census, which enumerates people by households, including name, age, sex, race, and relationship to head of household. The Census allows access to data on individuals from Censuses taken at least 72 years ago. (Individuals may also obtain their own family records for Censuses after 1920, and provide them to researchers if they choose.) The censuses of 1900 and 1920 are usable for data linkage through Soundex records for each state. Linking this family information with current information on vital status and health in HCFA, SSA and other databases is potentially feasible.

Linking large databases on relatives to demographic and health care databases.

Certain large genealogic registries such as the Utah Population Database (120) and the BALSAC registry of a genetically homogenous Quebec founder population (121) include computerized records spanning many generations, and have been linked to national and regional historical, social, and demographic data sets. BALSAC data can be linked using an automated linkage system (122). The Utah Population Database has been linked to national medical databases, and is being used to characterize multi-generation families with increased incidence of longevity (123).

Two American twin panels of special interest for aging studies have exploited linkages to national health care data bases: The National Academy of Science World War II Veteran Twin Registry is the largest American twin panel of older persons, containing about 16,000 white male twin pairs born between 1917 and 1927 (124,125). A wide variety of biomedical and psychological studies have included this panel (126,127). The Black Elderly Twin Study is a population-based study of African-Americans aged 65 years and older, using Medicare files to find potential twins (128). Two Scandinavian twin registries of interest to aging with extensive links to national databases are the national Swedish Twin Registry, which in 1995 included approximately 19,000 subjects (in 6000 in tact pairs) over age 65 years (109-111), and the Danish Twin Registry, which includes over 2500 living people above age 75 years (129).


We conclude by noting implications of the above considerations for other areas of research, and needs for methodologic development, infrastructure, and resources.

Besides insights to be gained into determinants of survival traits and rate-of-change traits, there are hazards posed by not using age-specified traits in genetic epidemiologic studies. The literature on genetic factors in age-related conditions is replete with contradictory findings. Though many of the causes of discrepancies are unrelated to age factors, age-specification can reduce heterogeneity among outcomes and thereby remove hidden causes of discrepancy. The reduced heterogeneity may also improve the likelihood of statistically significant findings.

The use of age-specified traits also facilitates exchange of data and ideas between genetic epidemiology and two pertinent disciplines: gerontology and demography. Survival and rates of change as functions of age, and mechanisms affecting them, are central issues in these fields. The implications of the data and theories they generate could be explored in genetic epidemiologic studies. Conversely, epidemiologic data on genetic determinants of age-specified traits can aid the choice of genes and outcomes for study in biological and demographic aging research.

The methodologies and infrastructure for genetic epidemiologic research on age-specified traits could be considerably improved. Many methodologic issues regarding design and analyses of these traits need refinement. Once such methodologies are developed, readily applicable software packages to apply them are needed. Easier, standardized methods to link data sets of familial, demographic, and health information are also needed. There is a scarcity of researchers trained in both gerontology and genetic epidemiology (1). This is especially important because strategies developed for the genetic epidemiology of other conditions will often need modification before they are applied to aging.

This field could also benefit from repositories of DNA and/or cells, particularly from persons with very rare traits (e.g. centenarians with no significant pathology). The time needed to recruit enough of these subjects could dissuade an investigator from studying them. However, if a repository acquired material from them as they were identified, studies could be done relatively quickly once enough were accumulated. A similar logic applies to elderly populations on whom there are extensive long-term longitudinal data. Many of these subjects may die before the full range of useful studies involving them can be designed. Establishing repositories for such populations would allow future research that would otherwise be impossible. Study design and planning would also be aided by establishing large registries and databases on twins and on families who have aggregation of traits such as extended longevity or absence of specific chronic diseases of aging.

The above intellectual and resource development would help in realizing the great potential of this research. Aging is a universal phenomenon whose relationships with effects of genetic factors cannot be ignored. The consideration of age-specified traits in genetic epidemiology adds the crucial dimension of age to the landscape of outcomes whose genetic influences are being sought in epidemiologic studies.


We would like to express our appreciation to Dr. David Burke and Dr. David Harrison, who also participated in the Aging and Genetic Epidemiology Working Group, to NIA staff who participated in the group's discussions: Dr. Richard Havlik, Dr. Tamara Harris, Dr. Richard Suzman and Dr. Richard Sprott. We also gratefully acknowledge the expert contributions of Clarissa Douglas in organizing the group's proceedings and preparing this manuscript, and of Winifred Rossi in critically reviewing it.



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